Hello and welcome to the session my name is Mansi and I am going to help you with the following question. The question says evaluate the following definite integral that is integral e raised to power x dx where lower limit of integration is 4 and upper limit of integration is 5. So let us start with the solution to this question. We have to evaluate the definite integral this. Now we see that integral of e raised to power x dx is equal to e raised to power x. Here the lower limit of integration is 4 and upper limit is 5. So now first of all we put x equal to 5. We have e raised to power 5 minus then we put x to be equal to 4. So we will have e raised to power 5 minus e raised to power 4 that is equal to e raised to power 4 into e minus 1. So our answer to this question is e raised to power 4 into e minus 1. So this is our answer to the question. I hope that you understood the question and enjoyed the session. Have a good day.


Good evening all of you. Welcome to a par-park panel that I have on this discussion with title Marketing Automation. Sorry about the late beginning because the organizers had some technical issues they were trying to figure out and you're on now. So I'll quickly introduce the panel. I've got Abhishek here, who's the head of marketing at Bharatpe. I've got Parasas Sharma who's a VP product and growth at Bakefit. Anuradha Sriman is the head of marketing at Resultrix and Raul Singh was the senior marketing director at Indian subcontinent at SAP. So I mean I couldn't have imagined a better panel than this to talk about marketing automation. So I'll probably what I'll do is I'll just kick start the process by you know just setting up setting the context and open it up to a couple of questions and then then I'll direct it to someone at the panel or someone as a point of view on the topic and can pick up and continue the conversation. So the way that I look at marketing automation of course it's essential as market tiers are building the tech stack marketing automation actually becomes an essential and an imperative in the old tech stack. Essentially when I look at what does it really do in simple terms it obviously helps deliver the message across to the right consumers on the right channels perhaps at scale and more efficiently compared to what otherwise happens in the traditional way so actually I would call it programmatic plus plus. But if you just pull back a bit actually and you know I'm trying to see whether you know there is a huge amount of hype around marketing automation or in reality is there enough work happening on marketing automation. So if we just pull back a bit and look at the evolution of marketing in the traditional marketing era we had consumers moving through a linear purchase funnel awareness to preference consideration purchase move to modern day marketing and that came the linear funnel became a circular motion where people discover and they want buy click the buy button and buy and of course the channels kept evolving so single channel became multi-channel became cross became omni and as a result of which what ended up happening is that the market tiers had to actually build platforms on top of each other and interconnect all that perhaps through an API at best because that's the need of the R and the unfortunate part of that is that the customer the fluid customer journey the customers going through couldn't be tracked because customers you know platforms are different customers is kind of you know fluidly kind of you know moving around and then market tiers came up with a solution that okay let's build customer center city and then that becomes another silo it became another department. So I'm actually you know when I'm thinking and looking at marketing automation is it marketing is already fractured so is it another silo that we adding to the already fractured marketing ecosystem by calling it you know marketing automation or really are there use cases that we really have you know which is working well particularly in the Indian context the questions in my mind are also that would it be suitable for somebody who is talking to masses of consumers like an FMCG versus a luxury automobile who probably are talking to fewer consumers and where should it begin where should marketing automation begin I mean should it begin with the good CMS for example a content management system is that the is the is that the real need for a marketing automation to begin so this are some of the questions in my mind is it more hype than reality are there good cases working is it again fracturing the whole marketing ecosystem or you know I mean if it needs to work in the way it needs to work where should it really begin these are some of the questions in my mind can I open it up now to the panel and rather do you want to kind of take the lead and you know perhaps answer some of the questions or add your perspective to what I said or you have a different point of view always welcome so before I answer the question I would like to give a brief on resultics Zartics is a real time big data driven AI powered marketing cloud solution built from the ground up by experts in marketing data technology and business strategy we are a SaaS based as the almost marketing automation platform and our customers think of us as an always on real-time only channel customer engagement platform while we think of ourselves as a centrifugal force between the brand and the customer driving digital transformation through data touch points and air driven analytics so like you said your question where really use cases happening in the Indian scenario the simple answer I mean if you're looking at marketing automation whether it's a CRM or a CMS or an automation solution marketing has multiple aspects to it and it's really impossible to handle all of it and all of your marketing activities are focused towards making the prospect or a customer take a particular action drive him to a certain conversion point and automation is really the key when you look at customer interactions like you yourself mentioned there are multiple footprints he is you know he comes to the brand's website and he interacts from multiple channels touch points and devices it is does not necessarily reveal his identity every time he interacts but he leaves definitive preferences propensities f&p's and signals with the brand it is now up to the brand to stitch together all these interactions and establishes identity to be able to deliver the experience he really deserves and craves so I would say that would be the starting point of any automation platform wherein you know you have all these data points lying around in multiple systems where do you start it all starts with data you can solidate all your data lying across multiple systems whether it's your marketing system sales system your lead management system pure air systems analytics and basically bring it into a single single data hub and and marketing automation allows you to do this consolidation it allows you to orchestrate and deliver your brand's communication through multiple channels like you know any of the channels that you mentioned where we mail SMS push in a social paid campaigns qr codes digital assistance and and and finally all of this you know it has to basically get scattered and decimates crucial insights and analytics that can help you measure and track performance across multiple campaigns and at an individual customer's level the biggest opportunity that automation offers a brand is the ability to deliver customer experience and a good outcome of this opportunity is that marketers are able to optimize their marketing spend the biggest pain point the same of faces is the justification of cost and a well designed automation platform allows you to get the end to end metrics and attribution for any every activity that marketing undertakes now and and it is at the end of the day it's about doing business at a lower cost and it's really a powerful tool for same often justify to the board the revenue value now when you're talking about whether it's a hype I would I would like to quote an example of a blank backing customer that we're working with and they used to take about two to three days to create segments on a database of about 50 million customers and after taking the data from multiple systems including exergy and different systems and spend more than a couple of a couple of more days to basically once the segment is created uploaded into a campaign management system to run their campaigns and it will take another week to basically get the analytics in place with the result is they are now able to create segments within minutes run the campaign and get the analytics at the same on the same day all from a single user interface so that's one use case I would like to quote and the other you know really important aspect of a marketing automation platform is the ability to segment your customers how well you know you can create cohorts to and and deliver personalized experience based on these these cohorts in the current scenario I would say that the biggest opportunity that a marketing automation platform can provide again is the ability to segment your data you need to understand which of your customers are in a position to make a purchase because given the current scenario and the kind of job market the way it's going it's not every customer who will be able to make a big purchase it's not every customer yeah I got that actually so the the the fundamental principle you are saying that it needs to begin with data of course rich data that's kind of going in there right now Raul my question to you would be that if you look at India as a as a context and if you look at advertising in India per se we're looking at the big threes perhaps I may not be absolutely correct in the number whether you google facebook and the amazon put together probably basically percent of the monies have gone there right and we are still talking about digital from a whether it's a programmatic or automation we are still talking about digital we are not talking about additional media which is obviously a you know big number which is sitting there so within the within the digital ecosystem 60 70 percent of the monies are going away with the with the larger guys google now spoke about cookies not being allowed third party cookies are going to be you know thing of the past if that's so and and data needs to be the crux of all of this uh my question would be my question is that you know the remaining 30 percent of money which of course you know can't be perhaps tracked the way that is being tracked currently with cookies not being there in the future how would that system then marketing automation play out data as you are aware of course this whole challenge that you mentioned about you know especially and that's probably true of the consumer marketing space and most of my experience actually is in technology companies most of which were earlier adopters of technologies such as these and the three platforms that you mentioned may or may not necessarily be taking away most of our marketing budgets but as you but but I think you're right a large part of that spend still goes to them to these three platforms that you mentioned from a consumer marketing perspective I agree but having said that I think irrespective of that plus plus also another aspect of data these days is it is now becoming highly regulated there are norms like GDPR etc that in that are in play and India probably is going to have its own data compliance laws very soon which will make a lot of aspects like email marketing a lot more restrictive if I may say so but having said that I think the fundamentals are still not going to change right I mean it is about being able to create content that is compelling for your audiences irrespective of what platform does it go go do and also I think you know if your content is compelling please remember that you know especially companies like ours are already for example following GDPR which means we cannot communicate with prospects or customers who haven't consented to to receive information from us so anyways for us that whole piece now becomes limited to the full factor right which means it totally depends on the content that you provide and that and and then the consent that you receive from your prospects and customers to be able to market that so fundamentally I think it does not necessarily you know where your fund funds are actually getting deployed does not necessarily change the strategy per se but on top of that I think I wanted to respond to your original question or the comment that you made in my personal opinion I think marketing automation per se seems to have become especially in the emerging experience economy it has become a bit of a misnomer one because customer experience load into the marketing function while it is actually a cross-functional approach that companies need to have and as a result of this saluting in of of experience within the marketing function I think we limit the possibilities and the vision that technologies like marketing automation can actually achieve so in my opinion ideally it should be called something like customer life cycle automation but then having said that I have a problem with automation as well because it sounds like archaic and lifted out of the era of industrial revolution because I have as I said my experience has been in the technology marketing space for almost all of my career and I have seen the evolution of marketing from you know very basic level of counting the number of people who showed up in your physical event that you actually conducted and then in those days email itself or a text based email itself was a very kind of a novel thing in that environment and then I saw the evolution to some very rudimentary CRM systems and all the way to the current stage when you're talking about AI and ML actually enabling your customer interactions I think it's it's a huge huge change whether it is hype or not I think the fact is that this some form of automation has always existed for more than two decades now and you know even as I think Anuradha also pointed out I think the fact is that and there are known enough use cases of this technology being deployed to deliver accountability within the marketing function predictability efficiency and I think in the current day and age where personalization or hyper personalization is becoming such a huge thing I think to be able to achieve all of this at scale there is no way you cannot you can do without without technology very valid very good point actually if somebody wanted to add something Parash you want to add something or you want to add something to that no okay yeah so as as Raul rightly said that in contextual advertising would still be valid and your own data will also be valid right so if you have your own audience you can you can anyway go after them anywhere you want and add platforms and still connect with your own data and we'll keep connecting with your own data to target those audience even though third party cookies and third party tracking would be going off soon but yeah but your own data would be your asset and contextual advertising is going to live yeah I want to add yeah which I got is saying so I wanted to quickly talk about wake fit so wake fit is our direct to home consumer brand right so we sort of started with mattresses now we are into a complete home solution I think yeah so what the issue started with right so where automation has come in so I think in the in the story of digital marketing to a automation we have seen that a lot of our marketing has kind of consumed into a technological era right so now every every automation that we spoke about now everything is about how do we reach the right guy at the right channel at the right time right now this the customer customers are smart right so in in wake fit I can give you a couple of examples that we have seen how do we use data like I wish I could say saying right their own data is so important that you know your your your data need to marry with the marketing automation right irrespective of the tools so we we sort of created couple of digital assets around it right so we have tied up with couple of partners for giving some special offers to our customers which we call it as a reward wake fit rewards now we use that data you know to send automated emails notifications or any other channels right and then we learn that okay what sort of partner gopons really works for our customers right so so in our scenario we use our data which sort of marries with the tool and then we try to reach out the right person whatever you know what sort of information that he wants to consume so I think in our scenario especially especially with the brand who sort of wants to doesn't looks at the user only through you know selling a mattress in 10 years and not talking to them like we have furnitures now we have we look at the user from a lifetime value wise right so it's important for us to sort of use the data properly right not to bombard you know sort of user with email sms's but to use that opportunity of reaching out in an environment which marries with the product data so we call it as you know like you know product market fit we look at as automation product fit right so if any sort of automation that sort of you know going to our user does it so we do a postmortem we see does it really resonance with the users are they reacting that's I think comes with the post analysis that the CTRs are okay I think everybody is speaking at the same time but in a practical scenario we do this postmortem right and our criticality lies that if the communication is in sync with the product that he or she sort of wants to consume in future or he has consumed right does he buy a bed every day no does he buy something else along with the vet yes how do we sort of cut this information right how do we store this information so we in fact we have a huge sort of a you know a data team which which sort of works not only for marketing automation but also for our product automations right so it's a continuous loop that we listen to our customers we listen to sort of you know it goes to the factories right so we are a manufacturing unit right so we we make sure that every every you know customer loop goes back to the factory so we sort of I treat any sort of you know discomfort or a comfort or anything good bad whatever we sort of listen to the customer and we make it changes I think that ways we are one of the being a very new in the market we try to change the narrative of buying mattresses right so people were not aware that bad mattresses also can be bought couple of years back online right because we're not aware so I think from there the stage we are in right now everything is yeah so essentially what we are saying is you know that data particularly first party data quality first party data is extremely important and particularly in the wake of the third party data you know correct correct so our user data is it's like gold right how do we use it is I so fully agree with you and I also you know there is a very valid point that I will not actually made I'll just pick that up actually now you know this whole like you actually use the terminology called lifetime value press you know this is one of the challenges that most brands are faced with today you know because all of the all of these stuff in my opinion is acquiring customers at scale that's exactly what we're doing right so you do automate the processes you show the right creative make it make people take some actions you click and you acquire customers once you acquire customers they are in a bucket somewhere else and you go behind the next set of acquisition so the people who acquired are sitting somewhere and you don't really bother about them so unless you really really have you know customer center city was supposed to be a department of that nature so unless you really have a practice internally or figuring out or like I would say it's not automation I would say transformation so under the marketing transformation really happens to actually start looking at and valuing first party data very very very very correctly and you know start building this whole lifetime value you know this will in my personal opinion and we can debate that this will be another you know mode of acquisition that you that you that you that you actually keep going and one challenge and rather you spoke about and I'll I'll also go to Abhishek on on that one of the things that you spoke about is that you actually are seeing that customer experience getting built through automation and I have a very I have a counter view on that in fact I will also have a counter view so can you give us an example of how customer experience is getting built I can understand that you're showing a communication or a making making the customer take an action through automation but how is the experience getting built actually the experience is the way you communicate with them right what is the communication that you're delivering at the end of the day at the time of at the time when he wants to be spoken to at the time when he when he really when he's in a move to interact that's the experience that our customers are actually looking forward to customer today wants to have an extremely individualized hyper personalized organization of the brand and he doesn't really want to be treated like one of the hurt and an automation platform can can really you know create this experience because of having the ability to understand the various signals that he's left through his past interactions in history have again it all goes back to the goes back to the customer data hub concept the customer data platform concept wherein you know it's not necessary that he's going to every time you know every time he interacts with the brand it's not necessary he identifies himself but then there are signals that he leaves every time he interacts with the brand and it is stitching together this entire you know all these interactions together and and creating that one view of him the building his profile building his identity is where your the platform can actually achieve understand his preferences his propensities his his interests and interact interact with him contextually so that's that's what I mean when I say that Abhishek you know I don't know the spoken word experience in a different way my question is that you know when you really look at look at people I mean look at human beings and you know there are practice there are obviously theories and and even practices which Abhishek has spoken about and directed towards the fact that decisions get taken at the emotional part of the brain not at the neocortex not at the outer part of the brain right so if you just imagine pushing communication all of the acquisition customer acquisition related communication is all about come and buy me you know it's all about I've got something to give it to you come and buy me which is again you know at the outer part of the brain and it's not creating any emotional connection with the consumers actually so decisions were to be taken and you read necessarily you know when the in the marketing evolution from that you know preference consideration that I was talking about it also became storytelling at some point I mean we all of us heard those those terms and terminologies right so how does the marketing automation fit with storytelling right so I mean I'm also 100 percent of the same opinion that people buy emotionally and they justify it logically so so I also feel the same how marketing automation can play a role here or is playing a role nowadays especially for let's say for for a brand like Bharat pay their their beat on the payment side or on the lending side or on the cards and their insurance and there are 10 other products that as a financial platform we are we are we are selling so when when someone is looking for for an insurance or probably he does not even know that he needs an insurance or for something let's say for for his own health family or even for for his own shop but when when when we share a message in a you know in a storytelling format stories are really really powerful they have the ability to create an impression to to create that need to to to actually make people take action beat beat of beat of buying or or beat anything especially for on our platform we really want people to take actions based on based on whatever whatever message that we are we are we are running for them so how marketing atom automation can actually play a role here right from people when they come on the platform first thing is to educate them so before before expecting them to take any action on the platform the first the first step is to educate them what they can do how how they can do it and what value they can add to their life is the first thing that we do through and through a journey of messages that are delivered through a mix of channels triggered automatically based on their their own actions that we have we have configured in the in the in the marketing automation tool internal marketing automation tool that we have created and then depending upon how the user is is interacting with those messages with different mediums beat in-app beat outside the app or whatsapp or sms or ivy are calling or the in-app pop-ups or there are there are multiple channels that we have integrated depending on those channels we create different messages and these messages are then pushed in a sequence format these this sequence is carefully designed with messages that convey the key highlights of the off of the platform product or in a storytelling format across across formats beat images beat videos beat gifs so we use use them we use a combination of formats with a with a with a combination of channels to deliver these messages sometimes it's it's emotional let's say insurance which which people buy out of emotions because they they want to protect themselves at the can can you guys hear him I think you got dropped actually okay we'll pick that from there we'll wait for Abhishek to join back so the conversation that you're having was about experiences and you know leading to that is a question that I have is you know in the you know when the programmatic thing actually happened and there were lots of conversations that you know clients are the big clients moving the programmatic dust inside internally you know so it's not going to be in the agency's domain people are going to be you know kind of moving it and managing it within so does automation mean that actually the the the role of agencies are actually going to be unlike you know you're a digital agency unlike what a specialized automation agency is your role going to be as an agency your role is going to be diminished to a to my some extent because most of it is you know something that the client can manage it internally because it's about automating his process and you know with the right processes and tools he is able to you know kind of deliver the message across without going through a third-party agent so what happens to the agency in the whole ecosystem so Prasad any point of view on that I think the agency's role in sort of programmatic and reaching out to the right set of user base for any sort of brand I think it's sort of evolving right so I think all the agencies now have their own trading desk right and now every sort of like you started saying programmatic plus plus automation is nothing but then you know programmatic plus plus and almost all programmatic channels are now interlinked right so you buy all trading desk are interlinked right you want to buy X inventory in X sort of you know exchange you can probably buy through Y exchange so everything is interlinked right I think one thing which I see that it's evolving but then I think there is still to achieve is consumer you know consumer data kind of linking in with that right now industry consumer data because mostly agencies look at the lenses from reaching out the user but then you know the reach versus engagement will only increase when we get one more layer of data which is consumer data right I can give you one example what I exactly mean by that say for example an industry which have a specific user attribute does that also cut in across digital programmatic data right which eventually sort of bring more engagement to the ad formats or ad inventories across you know across brands right so I think that layer of data is I think it's evolving but then in in in near times I think we will sort of have industry wise user attributes data cut also to sort of I think agencies are sort of moving in that direction that's my thought any um Rahul any comment on the agency ecosystem yeah see as a see one of the outcomes of automation also means that there'll be a lot that one would be able to do on their own so clearly I think in the mid term I see reliance on external agencies going down to a certain extent but difficult to predict in the long term how this is going to evolve and I'm I'm sure agencies are going to evolve in terms of what they actually bring to the table as we move forward right so yeah in the short term I would expect yeah their engagement to go down yeah and one of the other you know obviously there is a great amount of inputs on data and particularly the correctness of data and robust data and first party data and which obviously is the beginning for anything to go forward in the automation you know while we're talking about this is my personal view and we can debate this while we talk about micro personalization and you know still I believe this still we are talking about consumer cohorts we are still creating groups of consumers it's you know you're still you know based on prices and patterns etc it's still a cohort it's you know to whatever personalization that we're talking about it's still not micro personalization for example so it's about you know creating okay this group of people could behave like this so let's kind of show them that to what extent will that be correct of course you know statistically we can we can prove all that but as we rightly said earlier it's emotions that actually really take the decision so when that really happens I don't know whether the cohorts are the way to go or really micro personalization real personalization is the way to go under any point of view on that personalization like you frankly said brands are really starting with cohorts right and marketing automation platform which also has the AI capability and you know a channel orchestration capability has the ability to really individualize and understand the customer at an individual level and deliver campaigns or deliver communication at an extremely personalized level so the possibility is there the potential is there it is at to what extent you are actually able to capture how much of data you have how much of the attributes around the customer do you really have so for example if a bank customer he has about just giving you an example there could be you know all the all his whatever personal details or you know transaction details and all the different attributes that you can collect about a customer there could be about 3000 attributes and the extent to which you're able to probably you know of the 3000 attributes a bank would probably talk about what percentage of that attribute you have you captured around that customer that that would actually you know determine the level of personalization you can bring to that to that particular customer so with a marketing automation solution like I said so the existing stacks most of the existing stacks are not you know AI powered and there are very limited ones available which have this capability and again it goes back to how much of that how many attributes can you really capture and against an individual customer to be able to personalize the individualizes experience not really personalize so you spoke about AI you know I remember reading an article sometime back about Open AI the company the Californian company they spoke about this language GPT-3 I'm sure some of you would have read that you know what does it really do it obviously it says that we are now going to render the creative jobs you know not required actually so which there's their prediction is that with their language you can actually get creative outputs human like creative outputs through the machine so if that happens and you know with automation etc that we're talking about and we've also spoken about you know the emotional part of the consumer's brain and so on and so forth do we also see an emergent scenario that's emerging where one is about the agency that we said that all of the agencies are going to be diminished what happens to the creative folks you know if AI takes over the creative jobs and they're able to produce results human like results and then all of them get really automated what really happens to the human creatives Raul I personally believe I've heard of this I think I think the role of technology and automation especially robotics for example our digital robotics is kind of a little bit overrated we all of us have heard about this hotel in Japan which is entirely the front office is entirely manned by robots and I happened to watch an interview with the with the the founder of that hotel and you know people generally the question really was how come you know this this entire hospitality industry is really about people to people interactions and stuff like that why do you think people would really be keen to have a robot to receive him at the reception and his answer was the hotel itself is you know targeted at young millennials who apparently are not interested in human to human interactions as much as probably our generation is so the answer probably lies there I think I think I would kind of take it back to the earlier point of how important is technology in this whole or automation itself is I think the problem with marketing automation and I'll still use that term is that you know while I personally believe it is about 30 to 40% technology and 60 to 70% strategy and vision and skill set okay and but the problem is most of the and when we've heard this through right every time we speak of marketing automation we are talking about customer acquisition okay that's where the entire problem in my personal opinion is I don't have actually voluntary disclosure SAP sales marketing automation solution but my question is and I'll give you a personal example I mean I don't know whether I should be citing this one or not but I had this insurance plan where in case of a you know certain set of illnesses that you develop you get paid for once it is diagnosed unfortunately I had one of those earlier in the year and then I obviously got paid so there was no issue with that but instead of writing me an email and I don't care whether it came from a robot or a human being I mean it was a great opportunity since we spoke about emotional part of the brain and stuff like that it was a great opportunity for the insurance company to ask me about my well-being but the first communication I get is is another term life term or something like that and how your case can still be you know so I'm saying that was such a huge missed opportunity because the entire strategy of marketing automation was not built around the customer it is built around customer acquisition which is where the fundamental challenge absolutely correct till all of those because you know the unfortunate part is that there are many many silos which has got built and we're adding to the silo which is exactly what we're doing in my opinion because there is a need for us to react in the market so we are reacting in the market the market is reacting in the market building another silo say you know to potentially solve a particular problem that exists and there are consumers coming in the silo and they're sitting in the silo and there is some other silo that happens there are consumers going there and sitting in the silo so there are you know the forget about this even the frictions consumers go through are not tracked you know unless you are able to so no amount of automation that happens this is my personal opinion I am you know always the you know creating controversies I think actually in my personal opinion you know no amount of automation of course all of this you know is this of course pretty interesting in terms of technology and good boardroom conversation and so on and so forth but unless it you know ground reality of you know touching human beings and figuring out their journey and their frictions and the experiences that they're going through all of this you know will at some point in time will be another you know cluster of data and I want to give you another another example experience I mean you can add a very good example Raul you know I was at this hotel in Delhi some time back a good hotel which I used to use when I was in the in my corporate life can't afford it as a startup so I called them after a long time I was visiting them and I called them and I said oh they said you're most welcome blah blah so I reached the reception they said we've decided to upgrade you to have you know better category than the one that you paid I was obviously pretty happy and I went into the room and I on the social media reports in customer center city epitome blah blah all that cut to next morning I'm checking out I get a mail can you rate us on a scale of one to five I said I did that last night I did that last night correct because that system on the system don't talk to each other you know I bought an agency on ORM agency which is the agency that manages their reputation they writing to me say oh thank you for talking talking about our hotel and so on and so forth cut to next morning this is what I get I said you know you had an opportunity if these systems were integrated you're an opportunity to talk to me as a as as your customer who reviewed you last night and probably you know continue the conversation further so the challenge in all of the automation process that you know the first party data is is not clean enough I mean there isn't no there no the journeys of customers are not track and the data is not clean enough actually coming back to the the creative conversation actually I wish I what do you think I mean you think the the you know the digital agencies of course will as you know as I would say we'll get faced with the medium term problem because clients will start probably looking at you know moving automation within so what happens to creatives what happens to creative agency for example you know I mean would this be real static you know come and buy me click me you know kind of a bait that we're giving consumers or you know storytelling actually plays a role here so if you'll see even for remarketing and retargeting there are templates available that gets created on the fly and gets served like you said buy me click me so this this is what is happening for the last number of years but with if there are really tools that are coming up that will that will really challenge the way a creative agency comes up with a creator with a k-cast copy with a with k-cast visuals that that really can can put them in the back seat then probably for marketers like us who feel that creativity is is the essence of marketing and if that can be automated I I mean that that's just my personal opinion human creativity can actually outperform creativity by machine that that's that's what that's my personal opinion and of course over the years the number of iterations and data and algos machine creativity can reach new heights but where where it will reach I mean no I mean none of us know but yeah but if if there are tools who can really really perform better than the creators that are that are done by creative agencies copies in terms of visuals color schemes and n number of parameters that that we draw the creative upon there's nothing like it I mean and it just makes our life easier we struggle with creative agencies a lot we debate over copies a lot we debate about visuals and and it just makes our life much easier then it's just investment decisions that we need to take and not even that now with the with the latest breed of programmatic tools I mean is a sit back relax put the goal and and and the job is done yeah which is which is again the other challenge right because that also works and works like a silo because the creative agency and the digital agency are two are two different silos you're like the creative agency and the media agency are again two different silos you know I mean I I don't know whether I could be right in this or not but I just want to throw this open to the panel for a you know for a word from you guys and we can probably sum it up with that I think we're also kind of running out of time I think and take a couple of questions from the audience as if there is time permits is that you know at the end of it when we're saying that okay I want to reach my customers I mean you're doing you know personalization and figuring out and in the absence of data micro percentage but still it's cohorts I'm looking at a scenario when you know the third party tracking cookies are completely gone out of the window and the emergence of these wall gardens which is like a facebook and google and amazon etc are there and you know large amount of money I don't know the percentage but a large amount of money goes there do you also see an emergence of smaller wall gardens for example you know people who's got validated good rich first party data that could become a wall garden so if there are 100 of them you know they may they may suddenly come up in business I mean it may not be programmatic anymore or it would be different kind of programmatic actually but do you see a scenario that you know you're probably talking to rich first party data owners you know like a facebook or a google or whatever and that actually potentially becomes a wall garden so automation for example then again come into a question that if I need to only talk to the 30 40 people like there is a google facebook and amazon and then maybe there are 50 other guys who's got rich first party data where I can get you know 30% of 40% of my consumers where does automation then go now you want to yeah so yeah so my two cents on this is I think there is a scope for sure right first party data so we spoke about a couple of things right we spoke about first party data how first party data is like gold and then we talked about personalization we talked about you know creative how robot versus human creation I think so everything is in sync the only thing what I feel is going to change is how big is the data right so if it's a first party so how big is the reach right even if it's a google or a facebook or any other sort of third party comes in what is the scale that he is giving for an advertiser right everything else are functional this is a core right if that data sort of reaches to x percentage which sort of even if it's for a niche brand or a bigger brand or a consumer brand or whatever you know cuts a slice and dice of data that he wants to do does it gives x reach in conjunction with other platforms with the functions which are creative which are personalization or x which creates higher engagement then it's going to you know work is there a scope or a vacuum for this I think it's there that's my two cents on this yeah definitely feel the scope for this yeah so go ahead and so you know there are very clear examples so you know any large aggregators that are there and they are certain industry focus they are likely to become world gardens and because they are you know they have data pertaining to a certain industry and the brands that belong to that industry is really going to reach out to them for and it's not going to be you know obviously you know when you have industry specific data and you know that these are the most likely customers who are likely to buy you are you know you would want to reach out to them so that's that's that's where I see the you know opportunity as far as smaller world gardens you know okay I'll I'll just you know my last words and I'll take a last word from each of you so muggy automation is here to stay correct I mean it's a it to me it's you know looking like a you know time will time will say that it's looking like a fad more than anything else and there is probably a herd mentality that everybody's talking about automation and I need to kind of get into automation personally for me I think it's more than and I'll rightly said that and it's for me it's more than automation it's actually transformation so if you really don't you know start looking at your consumers and lifetime value and you know the whole process is just about automation and acquiring customers and acquired customers again start sitting in a silo you know businesses may start you know feeling the pinch at some point in time automation is good but don't forget the creative angle and the emotional play and and the human in there it's not a you know robot that you're talking to you're talking to human beings at the end of it actually and first party data can you know become a valuable first party data insights you really don't have to have cohorts you can actually micro persuasion can really really exist that is my last bit and I mean I'll open to all of you to you know give me your uh some of comments on you know the way that you see automation yeah so I agree on one the particular aspect of you know not being able to deliver can you at at some point of time can your marketing automation platform deliver empathy at scale is is that is that something that that's a possibility and you know that's that's where I would you know look at because it end of the day what Rahul mentioned is a use case right so if a customer it's an event a customer making a claim and what did you do with that event what did it trigger in your system if you have a well oiled automation system you're you know you have all your communication in place and across different customer journeys what is it that you designed in the first place was it designed for empathy or was it designed to be to treat him as just another customer with whom you want to you know kind of upsell another product so I think that's that's the you know we'll have to really time we'll have to tell you because end of the day there is no replacement for creative human thought and and that the possibilities in terms of how things can you know change so one of the things that I felt in the in the current situation of healthcare brands have really lost quite a bit of huge opportunity in terms of how can you know it's not just about you know the number of patients that you deal with how how you treat your patients and things like that but given the current situation you hear stories of you know there are big celebrities who are spoken about having a feeling of isolation at the time of hospitalization and things like that and and how is it that you know how are you treating your patients once you have you know and how do you deal with this kind of a situation is there some way you can keep a channel of communication open it could be it could be anything it could be the hospital app which has a bot that kind of speaks to your patient asks him about what you know how he's feeling what is he what is he doing and you know have some kind of interaction is you know your hospital system designed to have you know personal interactions with your customer it's not necessary that you have to go you know given the situation you really definitely have to maintain distance but how is it that you can you know be empathetic towards these customers how do you make sure they don't feel lonely specifically the ones that you know the many years stories about people really being left on their own even in the last moments what is it that they could have done differently and and and and we that that's where empathy and the customer center really you know comes into play good good value you know unfortunately what happens in the case of automation is that your automation system is telling you that just create hand-washed videos because everybody's watching hand-washed videos which is what which is what actually happens any we have to shake your last words yeah so i just want to say that marketing these days is less about making the sale and it's more about the journey now and marketing automation just helps you craft or just just craft those messages and deliver them seamlessly to the user at the right time like like other colleagues here also said and delivering it in the right time to the right person and the you know very personalized and hyper targeted way so that's all from my side rosen your last two words yeah so i think everybody has sort of covered it so i would say that i think it's gonna evolve in a much much sharper way i think post analysis i think we are still not doing a lot of analysis around what automations that we are doing i think it will go to a model where every startup goes through you know this process of automation will also go through sort of a you know like every startup goes through a product market it will go to a automation market right what works what doesn't work and it will go to a stage where we will have own templates of automations and the evolution will go in that direction that's good thank you very much i mean that's been a pretty interesting discussion i've learned some interesting things and i hope the viewers also you know could have enjoyed it and thank you very much for making it meaningful thank you making it thank you for making it enriching simran over to you thank you so much all of you again and sorry for the delay which we had and this is really an engaging session and we look forward to engaging with you once again thank you so much thank you so much for having us thank you thank you


All right, good Cool. Can you guys hear me? Yeah, yeah, cool So thanks for coming out today. We're gonna talk about everyone's favorite project the salameter My name is Gordon Chung. I'm engineer at Red Hat and The new ptl of salameter And I'll let Pratt introduce Our topic for today Hello everyone. Thanks for coming Today, we're going to talk about Deploying salameter scaling out salameter. I mean how many in this room in the show of hands are operators who try to deploy salameter And awesome perfect. I think you guys will be like fit right in here So let's get through some of the obligatory slides here I'm going to read that mission statement. Meanwhile, you guys can look at that picture To reliably collect measurements of the utilization of physical and virtual resources comprising deployed clouds Persist this data for subsequent retrieval and analysis and trigger actions when defined criteria are met Well, that's a mouthful What does this really mean right so Just just a quick intro to what salameter is really, you know, what what we want to do So the the idea being you know, salameter is Basically, you used to collect the physical and you know, virtual resources running in your cloud Once you capture these resources, you know into whatever the data that you want If the data is exactly what you need to persist and use great if not, you know You can use some transformers to convert the data to something measurable Super next we want to publish this data to various targets. So the various targets here are Multiple publishers that salameter supports, right? Like HTTP publisher or I mean recently we added Kafka publisher, you know, it could be as simple as a file Or you know a notifier publisher So, you know things like that you have multiple options here So now that you publish the data for, you know, external consumption, whatever it's using it We also want to persist this data, right? We want to be able to access this data in future For querying or for building whatever so we persist this data to storage And then we provide a rest API for you to be able to access, you know for further analysis, you know building or You know coming up with pretty graphs, whatever you want to do So with that said, you know, let's look at historically how the architecture was So quickly I'm not going to go very deep into this architecture. This is ice house based And kind of that's where you know salameters You know experience came from or you know what people thought of it So if you look at this architecture, it's very simple. So we have open-stack services, you know publishing data to you know The notification bus We have polling agents polling off of, you know, the service API is getting the data that way We have notification agents, you know grabbing the data off of the notification bars You know your collector is getting the data and persisting it in the database We have an API through which we expose the data and then, you know You you basically query the data and do some cool stuff with alarms with the alarm evaluator and notifier Things like that. So this is kind of the basic workflow On how things work in you know ice house and pretty much even now for the most part So with that there are a lot of limitations, right? I mean with a given architecture So couple of things we highlight here. So if you want to, you know, how horizontally scale this whole application as is It's really not possible in ice house, right? And for example, you know the polling agents you can, you know Scale that horizontally in an active passive banner using pacemaker. Okay, great. Perfect Notification agents you could you could deploy that in H.A.  So there's like a heavy API load and above all We had one single database with all the data dumped into it, right and you know We Mongo was the default of course we had SQL support But you know, it it was it was a real joke. I mean it didn't scale very well the queries took like forever So with this You know, we we asked the cloud admins, you know, we spoke to a bunch of people and hey, what do you guys think of? Solometer and Voila, this was this was the perception and this is really not the perception we want, right? I mean this says that cloud admins are really scared of salameter and The key here is the word perception because it's how it's been perceived You know, it might or might not be true in every case, but that's just how it was perceived With that so let's get into some of the complaints that cloud admins had right, I mean Yeah, of course, they were you know, it's obvious that they're scared to deploy this in their data centers But you know, everyone loves to complain and you know, that's how we get things fixed really So let's look at some of the complaints that people have Number one API response too slow.  When salameter dies glance dies I don't know if this is really salameter's fault I mean, this is probably a bottleneck that rabbit has or the messaging queue that you are using that's getting flubbered because you don't have You know a limit on the number of messages and that's taking down the services down So you can't really blame salameter for that Salameter has memory leaks. Okay. All right We can't argue that they might be some Salameter doesn't scale. Okay, sure. Oh perfect.    That's just how the perception is so Now I'll hand it over to Gordon to talk about how salameter has evolved from here on So that's kind of where we started or where we came from and I'm just gonna highlight some of the changes we made in the past few cycles So this is a similar architecture to what you saw originally, but there are some subtle changes to it So the one thing I want to point out is that salameter itself is composed of several discrete services there's a polling Functionals there's polling functions notification handling storage and alarming and it's possible to run all of them as a complete solution or If you should choose you can run each one of them individually if you want And also they're designed to scale horizontally So if you have a large node or a load you can just add more agents to the cope with that But specifically in Juneau what we did was we started to focus on some of the core issues around storage and Also the durability of our services One thing we did was we split off the alarms database into its own section We also updated a sequel database back in to kind of simplify the model originally the model had a lot of weird Relationships that we needed to handle both the v1 and v2 models of our API But in Juneau we deprecated the or we dropped the v1 API and we were able to simplify the model quite a bit and In a we ended we end up storing a lot less data and it improved performance dramatically Another thing we did in Juneau was we added Oslo messaging support So basically what it can do now is it publishes to a topic rather than using RPC which has a certain overhead to it And this also allows a lot more flexibility in that Because we're publishing to a topic you can have not right now the collector by default will consume Message off the queue, but you can there theoretically use any consumer to take that the data from the queue And just that yeah, just flexibility there and The last thing we do is we added coordinated HA for our polling agents and we used a Tool called twos and I'll go into that so Basically twos is how we handle our Coronation between agents It's a group membership tool that some of the guys that you know Vance created a while back It supports various back ends like Redis memcash zookeeper and the basic premise of it is that When you have an agent it knows about all the resources it it can pull and when it starts up They will register with twos and it'll find out all the other agents that are live and active and from that It uses a hash ring to kind of bucketize some of the resources so it knows What resources it needs to pull doesn't actually so it the one Benefit of that is that it's kind of ignorant to everything else It just knows what it's kind of lightweight and knows exactly what it needs to do and doesn't actually care about what other agents are doing So it's similar to something I think what ironic does in its conductor and So every time you start or you add or drop an agent You'll register with twos and it'll broadcast the new or the new members of a group and they will redistribute the resources accordingly and In kilo we kind of furthered went with the same idea of just kind of supporting discrete services In the pipeline we added support for Kafka and HTTP HTTP publishing so Again, if you don't want to use slumber for storage you can Push it to another queue or to another HTTP target and consume the data that way We also added the same coordination Technique to the notification agents so you can have multiple notification notification agents and If they are if there's transformers, it's fine We also added better event support so in slumber we captured both metrics Metering measurement data as well as events that happen within the open sacrum system So we originally had that in ice house, but it was kind of completed within in the kilo cycle So it functions similar to how we capture meters You can push them through pipelines and publish them to multiple topics or destinations and Also, we split off the events DB. So now there are three databases metering one for meters one for events and One for alarms, but they can also be just one database if you want Also for elastics for events we added support for a last search which is Really good for full text Quering I won't go too much into that we I have a session later on that talks about that if you were interested and Lastly we added added support for we added jitter support.  So we saw a bunch of complaints so far But you know as I was mentioning not everything with salameters fault really I mean, there are other bottlenecks that that was bringing down salameter or the performance was impacted by other applications That are running behind salameter So it's it's always better to understand, you know As a whole how you're deploying the application what it involves and then you know deploy it based on You know your needs So with that, let's let's go over some of the best practices, you know, this is just a subset.  You know Keep away that way Number one, you know, your data is Pristine to what your needs are so you're quick your queries aren't going to take too long because you're not collecting like data that you don't really care about You know when you're querying your things are going to be much faster your database sizes will be, you know At least, you know, you don't have needless data that you need to worry about So, you know things like that. That's one of the best practices Again polling intervals, you know, there's a default polling But if you want to you know tweak it to your needs, you know, maybe you don't want to you know poll every You know 20 minutes or whatever Instead you want to pull like twice a day sure, you know go and tweak that that way you're not, you know putting load on other service APIs, you know unnecessarily, right And jittering to polling this is something that we added in kilo So imagine a scenario where you have I don't know thousand compute agents running right and And imagine a situation where all the thousand compute agents are trying to talk to the Nova Nova API at the exact same time That's a huge load on your Nova API Versus, you know, you can enable this jittering what what this jittering does is it adds a random delay in You know how each of these agents are talking to the API is that way you are kind of distributing the load And you know moving it around that way, you know, the Nova APIs are not, you know hit by The agents all at once and again, this is something that you can easily turn on in the config file Scaling out, you know add Agents as the load increases. So this is this is something that you as a cloud admin should know best, you know How your infrastructure is scaling how how much load is coming at what what what point of time? So, you know use your best judgment and you know add add more agents based on your needs, you know that way, you know you are you are You know addressing the problem ahead of time before you know your application goes down for heavy loads it while you have You know few agents that are not handling the load properly This is something I mentioned earlier So instead of using the RPC publisher use a notifier publisher. It's it's much more scalable. It uses Oslo messaging RPC comes with a Overhead so, you know, you can avoid that because now that we have that support in Juno All right, so some of the data store practices, so You know, we have we have heard a lot of complaints about, you know, Mongo doesn't scale or you know The back end is you know taking too much space So that there are there are certain things that you can do so for example the queries, right? I mean make sure your queries are, you know, full-formed don't don't do open-ended queries and You know, obviously, it's going to take a lot more time if you just want to you know query a tons and tons of data by just a single point Instead, you know have a full-formed queries Run-slameter behind mod-whiskey The advantage is that it gives you a lot more knobs to tweak So you for example in in the in your Apache configuration You can add the number of threads and processes that you know apache needs to spawn so that way you are kind of distributing the load And your API can scale much better Set the TTL so of course so this is again This is probably one of the best practices for you know other applications It's not necessarily salameter any application you're running So if you have a database, you know, and especially in salameter case we we capture so much data That you know, it always makes sense to figure out what data that you really need You know, how much do you want to keep? You know keep the data You know if you have years worth of data, do you really need it? You know or maybe do you only care about last week's or last month's data So figure out what's what's best for your infrastructure and your needs Similarly, Mongo, right? So don't run Mongo on the same node as the API I mean, that's just that's just basic common sense, right? Just put it on a separate node that way, you know number one it makes You know the load and as well as the space all those constraints on a separate node And you just don't have to worry about you know, oh my API is not scaling or you know Oh, I see a bunch of errors in the logs Oh, it looks like you just ran out of space and you were running the API on the exact same box as Mongo So just move it to another box and another node and just That way you you are isolating the problem in case you have issues in future At least you know that it is Mongo and not necessarily salameter scale Again when you're running Mongo enable sharding and replica sets, you know, just don't run Mongo You know as is, you know, you use your application you sharding You know that that helps scale, you know much better So the bottom line with all these is, you know you as an operator you as a cloud admin try to learn The application try to learn what what takes for me to deploy salameter and figure out what are the best practices for me to follow To deploy this instead of you know, obviously every application out there every project out there has some issue or another It's it's just that you know, how big a problem it is and you know, if you think Mongo is the problem You know, maybe you can investigate different back end for your needs But again, it's not necessarily salameter small. I mean, we're not saying salameter is perfect I mean salameter has issues absolutely, but you know, so so does other projects, but you know So you need to understand what your needs are and follow some of the best practices that way, you know You can you can have some better experience in future with that Cool so I mentioned so as I mentioned before there salameter is composed of multiple Discrete services so you can kind of deploy it in many different ways to suit your needs So I'll run through a few deployments that I've seen or are possible with salameter So this is the lambda design the basic premise of the lamb design is there's like a fast path and a slow path So in this case we support our pipeline supports publishing to multiple targets and This narrow you would take a single data point and publish it to do two different places You'll have a short term database which would have maybe a shorter time to live setting so you would expire data quicker and You'd have another archive database that would kind of keep more data the short-term path obviously you Would you'd send it'd be more for kind of tense time sensitive queries, so if you for alarms such for example if you use alarms you probably set that against the short-term database, so You can get your queries back quicker and for the archive one if That's more for queries that if you you're okay waiting a few minutes or hours Then that's also that path for that one The next one is data segregation. This one is similar to the lambda design for this one the use cases if you have two data two different data points one of them being I don't know CPU you utilization you would send that to the public data store and if you had more More sensitive data like audit data you would send that to the audit database and you'd have two different api's trolling controlling access to these databases to Meet the required access control So Salama itself also allows you to not just store and write into databases, but also write JSON files So there are some big data tools out there that can consume JSON files like a patch of spark So you directly could write to a JSON file and have that load into a patch of spark and do your data processing that way Fraud detection. Um, so this is what I did with the previous company What we did was we had a proprietary learning system. So we didn't actually use Slammers storage or alarms. We just sent all the data via HTTP to our own system and we would build our rules and alarms that way custom consumers We also in kilo supported Kafka And one of the consumers of Kafka is a patch of storm which is similar to patchy spark So, yeah, this is also an option if you want to go that won't go down that route if you're familiar with both these tools I should add disclaimer that none of these tools are actually part of Slameter. You can use them like as Extension, but Slammer itself doesn't include these tools Debugging um so in in addition to metrics. We also collect event data, which is kind of Notification each so every service emits notifications on kind of the state of the resources that the state of a resource it has and Slammer consumes that and builds metrics of it, but also builds events and Using that you can send that data into your last search and use cabana or Slammer's API to query that data And kind of do deep diving into it and kind of see what data is a is available And the last scenario is noisy services. So there are certain services out there that will emit significantly more Notifications than other services So one of the things that Slammer can do is it listens to multiple notification buses so you can actually have It can consume from multiple places. So if you have one that's particularly noisy You could could have that on its own dedicated service so you don't overflow the bus and have some more quieter ones on its own bus So where do we go from here? Now I'm just gonna highlight some of the goals we have for Liberty So you might have heard of this term called no key that's been flowing around It's a pasta. It's also It's also Storage mechanism we're doing on or we've been building some of the Slammer developers have been working on that for The last few cycles and we'll talk more about that next But it's something we're looking to make as a viable back-end in the livery cycle we also in Kilo we we've been working on events and We hope to kind of extend that functionality in the upcoming cycle I'll just do alarming on events and maybe build add some the ability to Transform or build alarms build samples from the events, sorry and Lastly we have Design session around that declarative data collection. So right now to collect data or metrics. We actually write code to build that Which is very restrictive if you add something Mid-cycle or beyond It's kind of hard to like bring in master constantly. So what we want to do is a Load in a file like declare our metrics in a file. So you can actually Load that in and not have to write code and that should improve the flexibility of our data collection And lastly, I think for me when I ran for ptl The basic the main goal was to minimize the bloat like we we will add new features slumber, but We want to make sure whatever we do that slumber remains as lightweight as possible No key. I'm even hungry yet So what is no key so as a garden mentioned, this is a project that Julianne started You know kind of to address some of the performance problems that salameter has So to to kind of give you a very high-level overview I'm just going to trigger your taste buds here You're going to read about it. There are other sessions that you know Owen and Julia and presented in the Paris summit which goes in very detailed on what knocky is So what what's knocking knocky is resource metering as a service? So in salameter, you know as as we discussed all the data is kind of treated as You know a single data point, right? I mean we don't really differentiate between, you know What is a meter? What's the metadata associated with it? It's all one giant blob that goes into the database What knocky does is it kind of you know differentiates the you know the data into kind of two categories So one is metrics. These these are more like, you know a single point in time These are like time series data that are very lightweight, which we just care about like the value of it and then We look at another data point, which is the resource that that kind of maps to this measurement So think of resource as you know an instance or a volume, right or an image, right? And a metric a measurement here is You know the the CPU utilization of the instance or you know the size of the image or you know the number of disks in a volume So it's it's a measurement. So what knocky does is it kind of separates these two into like It realizes that these are two separate problems to deal with and uses the right tools to handle them appropriately So it uses this light lightweight time series, you know data So we have this concept called a storage and an indexer So the storage is kind of responsible for this time series data Which you know by default we have something called a carbon error Again, yummy. So it is like Swift plus You know pandas based, you know a canonical implementation And you can also there are other patches to support inflex DB and you know open TSDB and you know a similar Databases to support the light light kind of data and then the indexer It it stores all the resource information and it maps the resource to the metric and the measurement is in in your storage And you basically add the measurements to the metric So this way you are handling the right amount of the right type of data with the appropriate tools Without, you know handling everything with like one giant tool, which could be Mongo or which could be my sequel or whatever in salamander And again, it comes with you know a few other, you know nice little features So we do like pre aggregation eager pre aggregation So in in salamander when you make a query all the aggregation is done on demand, right? I mean you make it very and everything is going on behind the scenes right there versus in knocky We actually do eager pre aggregation And similarly we have support for cross metric entity This is this is mostly for like, you know auto scaling with heat so if you have multiple instances and you know, you want to take the measurements off of you know, multiple resources And again, so we have retention policies so you can define a retention policy on a poor metric basis So again, knocky realizes that you know, you you don't want a single retention policy for your entire data, right? I want to retain In a CPU utilization metrics one way and you know, this metrics another way So you kind of you know can define on a per metric basis How long you want to keep one versus the other so these are some of like the advantages of you know Knocky and and it's it's addressing the problem with the right tools the right way. So and you know, it looks pretty promising So, you know, this is a very good, you know Example of you know knocky versus salamander data point So if you see the amount of data that salamander stores on a per data point basis versus in knocky It's just the timestamp and the value. So it has much much smaller footprint It's going to be much more performant and you know, it's it's pretty obvious from this a little picture You know why so we don't need to go much deep into that So we have some metrics that may be, you know collected. This is this is basically using 10 You know clients Parallely posting, you know doing post requests hundred times per hundred measurements So this is these are the post measurements and these are the get measurements. So you kind of see the performance This is for Swift based and he also has a blog post where he he he focuses on the SAF implementation as well So you kind of get the idea of you know, if you want to use canonical Swift versus you want to use SAF as the you know back end and the performances Pretty pretty on par for both if you go and look at it So with that I'll pass it on to Garden to you know, talk about where exactly no key fits into our future architecture Cool. Um, so this is how to keep it in the basic idea is that your alarm evaluator will now query the no key API which should significantly speed up your Sets of the your statistic queries because it's already pre-aggregated and This would be how you deploy it so Tomorrow we actually have a operators discussion or Obsession where You guys can I think a lot of slumber developers will be there and we can chat about what needs to be fixing We also have a few design tracks on Thursday or Wednesday and Thursday The main topics we have our event alarms and slumber Componentization, but there's also a few other ones that we Will be working on I don't know if you're Free you're welcome to join And I'm gonna do a self-plug here on Thursday, I also have a talk where We show how we use slumber events to debug an environment Yeah, and also, I guess as always there's IRC if you end up ping us with questions and the mailing list Just take it with slumber and someone will answer This is just to highlight some of the resources that or some of the stuff. We've been talking about I you can kind of jump into these links if you want and kind of learn more about what we've been talking about and That concludes our talk there's beer I think


Tom here from Lauren systems and one of the challenges in technology is keeping up with the ever-changing landscape of technology and That comes down to reading lots of new sites and people ask what sites do you read and let's get more specific about how I read those sites So if you just want to know what sites I read Hey, I've left a link down to a forum post where I have my list But I don't read them natively on their sites because the algorithms are Dominating them to try to push you towards clicking all the fancy shiny things on the site to sell more ads and that can be a distraction from what you came there to do and this is where an RSS reader comes in specifically today We're gonna talk about fresh RSS because it's my favorite RSS reader I started using it last year and I just can't say enough good things about how much better I am at organizing the news I've used RSS region to pass and I've kind of got away from them And I really made a commitment to stop going to it's not getting distracted by all these different sites and consolidate all these RSS feeds But it's more than RSS feeds. It'll actually do RSS Adam OPML HTML plus X path web scraping it'll even pull in YouTube feeds and it's mobile friendly So this is a pretty cool open source self hosted by the way application you can use to curate and minimize the distractions of all the different news articles that you want to keep up with So let's jump over and talk about it and speaking of distractions that running in the background happens to be a little Linux utility called Hollywood I think it's always fun. It's always a good conversation starter and it's easy to see how easily people are distracted So that's why I threw it up there for a discussion and topic, but leave your comments What you think of it down below? Let's start here at the manual Don't worry. I won't read it all to you But I want to talk about some of the features because maybe there's features in here that I don't use but maybe interesting to you So RSS and Adam aggregation mark article as favorite if you liked it and we want to read it later I do use that feature filter and search functionality helps easily find articles Now this is not full regex filtering that you can do but you can say I want to find articles of a certain title Across all the new sites that I've aggregated in here. So that's pretty cool Statistics neat if you're curious just how often articles have been posted in some of the publishing frequency of the website Import export themes available extensions by the community Google reader like API to connect Android applications I don't use this but I think it's kind of neat, but the interface itself is mobile friendly So I just view it on my phone logged in so not a problem there The application is responsive, which means small screens No problem as I said there and one of the other things is neat There's not only is this self-hosted and free But you actually have the ability to create an RSS feed out of all your aggregated RSS feed So if you wanted to pipe this in back to the API like connect option, you could actually feed this somewhere else as well Now they have documentation on it administration and getting it set up There's a couple different ways to set it up. I happen to run it in a docker They do not have their own official docker images, but the folks at Linux server.io do maintain this This was updated just two days ago they have installs for for through Docker CLI or through Docker compose and Also, I'm using it myself because true charts has a version of it and I just use the host path option I had mentioned this the other day in my video. So you can load this up through true charts You just point it to the host path that you would like and all of your data is saved right there So if you have to rebuild the application you just point it right back at this and it sets up again makes it easy to back up and restore on another NAS if That was something I needed to do. All right, let's take a look at the fresh RSS interface right now It says I have no articles. We can try to refresh to see if there's any more There's no beats to refresh because they're all refreshed This shows me the show on red and this would be show red articles and we can click these and for example If I had some on red articles, I could filter for only red only on red Or maybe I should say only articles that I've put a star on or only ones I haven't put a star on as and made it my favorite We also have this as a bookmark option to show articles based on a query So this would find any articles where in title equals Chrome and instantly find those So I can go back to even right here on August 18th of 2022 Google patches a Chrome zero day. So this is from threat post I can look at this article have some of the data from it go right to the article as needed and be able to see that an Insufficient flaw input flaw one of 11 patch and update this week now These are ways you can easily find if you're looking for something or a company name and have they appeared in the news before This is one of the reasons I keep so much of this news So when something happens currently, I can also go back historically and say was this company in there before and of course if I changed us to Last pass there's been a few articles about last pass dating back to December that are in here about them Now as far as customization goes you have these global options for display for reading for Archiving and even for sharing so we go here to the sharing for example I have it so I can share to Twitter or share to LinkedIn and if you want to add another sharing option Maybe I should put it in for mastodon. I can hit the plus and put the share name display share URL use hit submit and now that would be on the sharing options Where those sharing options show up is when you click on any one particular article We have the share option here Twitter and LinkedIn is primarily where I share things But you can customize those as needed back over to our mainstream here Anytime you're managing the news feeds we go to subscription management and we'll look at like bleeping computer I'm just putting their feed. It's bleeping computer comm slash feed pretty simple And when you added this I can customize and override any of these defaults if I have a specific setting I want for any of the customizations for any input source I have for RSS or web scraping Etc because it can also do HTML plus XPath web scraping now let's cancel this and look at something like the YouTube So if we go down here to things I have for YouTube and I have like Lawrence systems in here We can go and customize that one and we can see Lawrence systems It's got the channel ID and if you look it's got the full feed URL, but you don't have to know the feed URL This is not anything you have to Have any knowledge of when you're doing this matter of fact when we'll go back over here And we just want to add something new to the feed drop in the feed URL So my youtube.com slash at Lawrence systems I'm gonna put that in the YouTube category I've added it so we'll let me add it again So give an error, but it will do the smart thing and figure out that feed It takes this input and converts it it does the same thing for bleeping a Peter I just putting it slash feed it Understands and looks and goes, okay I found the RSS in here and I can pull that information and then aggregate it together now for each of these feeds Whether it's new sites you decide on each of them part right here We'll say manage whether or not you want these in the mainstream by default So I can have where I only show in a category or show in the mainstream The advantage to doing that is some things I don't want in my mainstream because that would be too much data But specifically with reddit, for example, I have homelab subreddits linux subreddits and support subreddits I don't always want these in my main But when I have the time I go through and see what things I haven't looked at that belong to these particular multi reddits And if we click on these and go to the manage these feeds, you'll see that there's simply reddit.com slash user warrant system slash m support and msupport.rss This is all you need and these are going to be linked down below in a forum post where if you want to see what multi reddits I have public basing and grouped so you can just pull these feeds in as well This allows me to pull them in and curate what I call my support subreddits and my linux subreddits and my homelab subreddits What this also allows you to do is as you add to these multi reddits And I put different things within them such as xcp and g ubiquity churnass pf sense pf blocker Bitwarden if I add another one of these in here to what I refer to as support subreddits Basically the topics I discuss and then I look for comments and interact with people in these forums This allows me to without updating fresh rss I just update these particular feeds and speaking of reddit your main page for reddit reddit.com slash press Slash rss you can get your own private listing feed now This is not something I include to the public but this is all my main Reddits that I subscribe to Curated and as I changed those once again, they're fed into fresh rss because the rss feed is always your current subscriptions You can also get more data in there if you want like anytime someone mentions you on reddit Self post replies only comment replies Etc and pull those into fresh rss as well Having all this data in one place makes it really convenient and one last part I'll talk about here is the social media I'm actually testing this out where you go and we'll manage this feed There's a free service right now. I say right now because twitter's a dynamic ever-moving target, but it's called nitter nitter.net And with nitter you're actually able to feed things from twitter As an rss so by doing this and saying look for tweets for tom lorenz tech my Public name and because my tweets are public you actually able to pull these in and get a list of the things I've tweeted without subscribing or having to log into twitter This is currently a service that has not been affected by changes at twitter So you're still able to pull certain people in there and it's nice if you just want to go I want to see a feed from a certain person But I don't want to deal with going to twitter where I see lots of dumb things that will distract me Doing this level of aggregation just really helps keep you focused Which is a challenging goal curating all the data around us into something that's more concise So I can look at it examine it learn about new things But also cut out all the extras that might be Distracting me on there someone probably pointed out, but tom don't you have a curated list of memes? I do have a guilty pleasure of looking at silly things on the internet that are curated on reddit So that is my thing that I will not put into my fresh rss fresh rss keeps me focused My meme list does not go in there because that's where I get distracted But by not going to the main page of reddit, I can pretend it's not there while I'm working and trying to aggregate data Nonetheless, let me know what you think of fresh rss Leave some comments down below what you like what you don't like if you have an rss reader you like even more Maybe I'm wrong about fresh rss about it being the best one because it's only the best one that I know about today One of you could leave a comment and I can learn about one that's even better And maybe I'll be reviewing that one at a later date than aggregating all the data, but for now this one's open source It's easy to host. It's easy to set up. It's got So many great features on it and I especially like the youtube one because you know Please like and subscribe and click that bell icon to get noticed of things and go ahead and throw your favorite creators url right into fresh rss and then have a nice list of all your YouTube creators and when they come out with new videos and don't have to be reliant on only youtube's That sometimes notifies you that you liked my content. So nonetheless, love hearing from you Head on my forums for a more in-depth discussion on this and other topics. Thanks