Aakash Pradeep
Principal Software Engineer at Twilio Inc.
Principal Software Engineer at Twilio Inc.
Hadoop, Data Lake and high volume ingestion
Twilio Inc.
https://www.linkedin.com/company/twilio-inc-
Leading the Storage and Compute platform.
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Principal Software Engineer
Salesforce
https://www.linkedin.com/company/salesforce
Worked with Data Lake and Data warehouse. lead the ingestion pipeline to ingest 500+ billion message a day and managed 50+ in-premise Yarn Clusters to provide MR based multi-tenant compute as service.
San Francisco, California
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Principal Member Of Technical Staff
Salesforce
https://www.linkedin.com/company/salesforce
Dealing with high volume low latency ingestion pipeline.
San Francisco Bay Area
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Lead Member of Technical Staff
Salesforce
https://www.linkedin.com/company/salesforce
Designed and developed a high volume and low latency data ingestion pipeline. This ingestion pipeline is based on new data structure called Concurrent Bounded Buffer and a Consumer Executor Service, which flushes the buffer based on size and time and consumes them in parallel fashion. This ingestion system is horizontally and vertically scalable. Currently, this ingestion system is used to ingest 100+ million events/hour in a shared and multi tenant set up with micro second latency.
San Francisco Bay Area
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Senior Member of Technical Staff
Salesforce
https://www.linkedin.com/company/salesforce
■	Designed and developed a system to parse structured log file using MapReduce and produce tenant specific file for each day. Besides parsing log file, these MapReduce job also filter log lines based on date, log record type and log record fields which do not follow a configured pattern and also sort tenant specific files based on timestamp and log record type. These log record are based on format which may change with different application version. This MapReduce based system is metadata- driven in order to make it version agnostic.
■	Part of the team that developed event-based system, which involves capturing, massaging and saving events to HBase. Currently, it captures events that are used for Identity forensic. It also has a component that is responsible to delete events that are older than the configured time.
San Francisco Bay Area
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Member of Technical Staff, Software Development
Temple University
https://www.linkedin.com/company/templeuniversity
Mobile web app developer for Fox School of Business.
Philadelphia
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Mobile Web App Developer
Temple University
https://www.linkedin.com/company/templeuniversity
I completed MS in Computer science (2011 - 2012) from Temple University.
Philadelphia
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Student
salesforce.com
https://www.linkedin.com/company/salesforce
Designed and developed a DELV tool, Data Extraction, loading and verification tool. It identifies foreign key dependencies even cyclic dependencies and resolves these dependencies in the best possible way while extracting and loading the data. APIs are designed in a generic way so that it can be extended to read data from any DB and write data to a different or same DB. Current version includes implementation for MySQL.
San Mateo, California
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Summer Intern
Yahoo!
https://www.linkedin.com/company/yahoo
Framework development,Automation Library development, White Box Engineering, Tested REST based Web application, Tested platform for Distributed processing of feeds(high volume less frequent and low volume high frequency) and tested Properties (e.g. shopping, real estate, finance) workflow for this platform
Bangalore, India
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Senior Quality Engineer
Yahoo! R&D
Designed and developed a framework in Ruby to automate the front-end testing of the web application using open
     source tool WATIR.
Intern
Master of Science
Computer Science
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Temple University
https://www.linkedin.com/school/templeuniversity/
Master of Computer Application
Gold Medalist,
awarded Merit Scholarship  for year 2005-06,2006-07,2007-08, 
Endowment Award 2007-08, 
Ranked among top 15 in IBM Great Mind Challenge 2006 - a National Level Software Development Program
Computer Application
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Vellore Institute of Technology
https://www.linkedin.com/school/vellore-institute-of-technology/
Bachelor in Computer Application
Computer Application
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Indira Gandhi National Open University
https://www.linkedin.com/school/indira-gandhi-national-open-university/
IEEE
Member
ACM
Member
Extreme scale computing has no implied scaling limit. The impossibility of implementing reliable communication between crashing hosts prohibits explicit-communication primitives in extreme scale applications. However, the alternatives are not well investigated. This paper reports the development and experimentation of a prototype implicit communication system called AnkaCom using a Statistic Multiplexed Computing (SMC) framework. The key concept of the SMC framework is total decoupling between application logical entities and the physical resources, such as processors, networks, databases and storages. The separation allows the integration of a SMC control layer that can automatically deliver mission critical services leveraging the maximal capabilities of the unreliable resources at runtime. The SMC control layer operates according to the principles of HDLC protocols that have been proven capable of delivering exactly once reliable services using unreliable lower level services. This paper reports the development and computational experiments of AnkaCom - an experimental SMC parallel processing system. AnkaCom is a Java implementation of a distributed tuple switch network (TSN) with in-memory replication capabilities. We study computational results of AnkaCom application against "bare metal" C/MPI application using a textbook dense matrix multiplication example. We show that granularity optimized AnkaCom application can consistently out-perform C/MPI application in a dedicated cluster of 12-120 cores. In-memory real time data replication effects are also reported to show marginal elapsed time changes with double and triple runtime tuple replicas.
AnkaCom: A Development and Experiment for Extreme Scale Computing
IEEE
http://ieeexplore.ieee.org/document/7363344/
The availability of high performance computing (HPC) clouds requires scalability analysis of parallel programs for multiple different environments in order to maximize the promised economic benefits. Unlike traditional HPC application performance studies that aim to predict performances of like-kind processors, this paper reports an instrumentation assisted complexity analysis method based on Amdahl's Law framework for program scalability analysis for different HPC environments. We show that program instrumentation helps Gustafson's scaled speedup formulation to quantify the elusive quality in Amdahl's Law. We report that without separating communication time from computing, prediction results are not trustworthy. We demonstrate a methodology that can transform asymptotic complexity models to timing models in order to separate communication time and to identify the optimal degree of parallelism. A traditional HPC cluster and a private HPC cloud are used to validate the proposed methodology by showing the feasibility of optimal parallel processing and by scalability analysis of five NAS benchmarks. Our results show that either cloud or cluster can be effectively exploited if the application can adapt to changing processing conditions dynamically. As we dig deeper into the performance analysis myths, “scalability limit” seems to mean less than its common interpretation but more on the inadequacy our programming habits and architecture support.
Program Scalability Analysis for HPC Cloud: Applying Amdahl's Law to NAS Benchmarks
IEEE
https://ieeexplore.ieee.org/document/6495929
he Peer to Peer Hadoop Distributed File System (P2PHDFS) is designed to store and process extremely large-scale data sets reliably. This is a first attempt implementation of the Statistic Multiplexed Computing Architecture concept proposed by Dr. Shi for the existing Hadoop File System (HDFS) to eliminate all single point failures. Unlike HDFS, in P2PHDFS every node is designed to be equal and behaves as a file system server as well as slave, which enable it to attain higher performance and higher reliability at the same time as the infrastructure up scales. Due to the data intensive nature, a full implementation of P2PHDFS must address CAP Theorem challenges. This MS project is only intended as the ground breaking point using only sequential replication at this time.
P2PHDFS: AN IMPLEMENTATION OF STATISTIC MULTIPLEXED COMPUTING ARCHITECTURE IN HADOOP FILE SYSTEM
Temple University
https://scholarshare.temple.edu/handle/20.500.12613/2184
An HDFS compatible FileSystem to store data in different cloud object storage like S3, GCS. In my view, a block storage system for a local Hard disk/SSD or different cloud vendor's object storage requires a hierarchical File System layer to make it easy to use it for processing and analyzing the data. The current implementation is based on that idea and it also tries to separate the actual data storage from the metadata storage so that either of them can be scaled independently. I also believe that the current set of big data processing tools at our disposals like Spark and MapReduce are heavily based on HDFS compatible Filesystem and require a layer like the current File System to work with cloud object storage in an optimal and less complicated way. It would allow using 2 phase commit to make sure either customer gets all the data or nothing after a processing step by allowing an atomic rename operation.
cloudfs
https://github.com/AakashPradeep/cloudfs
An embedded key value storage system with reliable distributed persistence. Current implementation uses HDFS as distributed persistence but idea is to use any distributed filesystem or object storage like S3 or GCS and it should be plug-gable for each storage. Since its embedded, there can be only one client application which can read and write from the storage system with strong consistency. Since persistence is distributed, client can move to different hosts as long as persisted data is accessible.

The design of xStorage system is highly influenced from Level DB and Rocks DB. It uses LSM to provide high write performance and reliability, and it will be regularly flushed to disk (persistence layer) based on size and time.
xStorage
https://github.com/AakashPradeep/xStorage
The Peer to Peer Hadoop Distributed File System (P2PHDFS) is designed to store very large data sets reliably and to stream those data sets at high bandwidth to user applications. It has a reliable  distributed file system with automated replication. The main objectives of P2PHDFS is to attain maximum performance and reliability as the infrastructure gets bigger and to make the system highly fault-tolerant system, Which will have zero single point of failure.
P2PHDFS
https://sites.google.com/a/temple.edu/p2phdfs/
The Apache Hadoop is an open source framework
which uses simple programming model to process large data sets
across a cluster of commodity hardware. Since commodity hard-
ware is very prone to failure, monitoring the performance and
failure of Hadoop cluster become very important. In this paper
we evaluate the performance of Ganglia and Cacti monitoring
tools for Hadoop monitoring. We evaluate the CPU and memory
overhead incurred while using these tools. The experiment is
implemented on Temple University Cloud.
Performance and Failure Monitoring for Apache Hadoop Using Ganglia and Cacti Monitoring Systems
https://www.linkedin.com/in/lakshmanan-muthuraman-3b4620b
Mountain View, CA
Lakshmanan Muthuraman
Principal Software Engineer/Technical Lead at Twilio Inc.
https://in.linkedin.com/in/shobhitaagarwal
Bengaluru
Shobhita Agarwal
Engineering @Twilio | Ex-founder, Travel startup | Ex-Amazon
https://www.linkedin.com/in/dipeshc
San Francisco Bay Area
Dipesh Chauhan
Principal Software Engineer at Twilio Inc.
https://www.linkedin.com/in/scotthaines
San Francisco Bay Area
Scott Haines
Distinguished Software Engineer, ED&A Nike
https://www.linkedin.com/in/abhijaypatne
San Francisco Bay Area
Abhijay P.
Software Engineer at Twilio
https://www.linkedin.com/in/heng-li-29a99b67
San Francisco Bay Area
Heng Li
Software Engineer
https://www.linkedin.com/in/andrewryno
San Francisco Bay Area
Andrew Ryno
Principal Software Engineer at ngrok Inc.
https://www.linkedin.com/in/hubert-huipeng-ren-2468982b
Santa Clara, CA
Hubert (Huipeng) Ren
Principal Software Engineer at Twilio Inc.
https://www.linkedin.com/in/bhargav-guntamadugu-32063127
Sunnyvale, CA
Bhargav Guntamadugu
Staff Software Engineer and Tech Lead at Twilio. We are Hiring!
https://www.linkedin.com/in/kris-nova
San Francisco, CA
Kris Nóva
Senior Principal Software Engineer at Twilio Inc.
https://www.linkedin.com/in/dineshnitk
San Francisco Bay Area
Dinesh Garg
Data Stuff @ Twilio - Hiring !!!
https://www.linkedin.com/in/akshaypaw
San Francisco Bay Area
Akshay Pawar
Software Engineer - Data Platform @ Twilio Inc.
https://www.linkedin.com/in/priyankad93
Redmond, WA
Priyanka Deshmukh
Senior Software Engineer at Twilio, Inc.
https://www.linkedin.com/in/stevenhpham
Denver Metropolitan Area
Steven Pham
Principal Software Engineer at Twilio Inc.
https://www.linkedin.com/in/ctgoss
San Francisco, CA
Christopher Goss
Principal Software Engineer at Twilio
https://www.linkedin.com/in/devaraj-adimurthy
Englewood, CO
Devaraj Adimurthy
Principal Software Engineer at Oracle
https://www.linkedin.com/in/akshayjain90
San Francisco Bay Area
Akshay Jain
Software Engineer at Robinhood
LinkedIn User
      
          



            
              
        
              
  

    
        Aakash joined Big Data team at Salesforce to work on Hadoop and HBase. During initial few days he impressed me and everyone in the team with knowledge of Hadoop and Distributed Systems in general. He is very dedicated, knowledgeable and talented individual who is also a nice and fun to work with. He explained me his MS thesis P2PHDFS to make HDFS masterless which is very impressive to be a masters thesis. He also has done partial implementation of it changing core parts of HDFS. He quickly grasped Salesforce multi-tenant environment and proposed, designed also developed sorting of logs. He quickly learned HBase internals. He also proposed, designed and implemented metadata driven log parsing with map reduce with high performance and utilizing the underlying resources effectively. Way to go Aakash!! Moussa Taifi Ph.D.
      
          



            
              
        
              
  

    
        Aakash is a stellar, dedicated, and productive software engineer. I had the pleasure to work with him on a distributed data storage project where he brought a high level of expertise in software development and project management. His experience with distributed systems is considerably superior to other students in his class. The depth of his interest in maintainable and testable systems was very helpful for me personally, because he brought his industry experience to our research project. This infused the end result of our project with his excellent knowledge of object-oriented programming, design patterns and performance-reliability tradeoffs in distributed systems. I would highly recommend Aakash as a sure value for any software project.
Liked by Aakash Pradeep
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Great leaders, customers and global team at this week’s VMware Worldwide Kickoff. Thanks to all. Laura Heisman, John Sabino, Zia Yusuf, Kit…
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Hello!My name is Vitaliy Georgiev and just three weeks ago I worked as a strategic director in a leading marketing agency AIMBULANCE based in Kiev,…
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I got roped into some LinkedIn drama yesterday...On a beautiful post about a mother who interviewed for a job but had her babysitter fall through…
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Bengaluru
Aakash Pradeep
Soft Eng. at Yahoo!
https://in.linkedin.com/in/aakash-pradeep-9b59b610
Bengaluru
Aakash Pradeep
Software engineer at Yahoo! R&D
https://in.linkedin.com/in/aakash-pradeep-6294a0193
Bengaluru
Aakash Pradeep
Music Teacher at Greenwood High International School
https://ae.linkedin.com/in/aakash-pradeep-1187171ba
Sharjah, United Arab Emirates
Aakash Pradeep
Student at Heriot-Watt University
Cloud Storage
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The Official Association for Computing Machinery (ACM) Group
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Int. ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC)
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Chicago Young Professionals
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Amazon Careers
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Hadoop India
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Yahoo Employees and Alumni
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Algorithms (O)
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Hadoop Users
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OpenStack
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Software Design Patterns And Architecture
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Test Automation
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Salesforce Alumni Network
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Java Developers
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