ETL vs ELT - What’s the Best Approach for Data Engineering?

The global data analytics market is expected to reach 68.09 billion USD by 2025. Businesses are finding new methods to benefit from data. Data engineering entails building data pipelines for ingesting, modifying, supplying, and sharing data for analysis. Data Engineering impacts organizations in several ways-

  • Faster and Efficient Decision-Making: Techniques such as insight data engineering enable executives to understand their client base. It aids in the identification of different types of clients or items, allowing for improved targeted marketing.

  • Explore Emerging Business Prospects: One of the most significant components of data science engineering is machine learning. Based on historical data, machine-learning algorithms allow you to estimate the future and predict market behavioral changes. This enables enterprises to remain one step ahead of the competition consistently.

  • Ensures that Decisions are Made Accurately: With the support of data analytics engineers, data engineering undergoes continuous self-improvement. Therefore, every decision is reviewed with this approach to develop new data-driven judgments.


Build a Real-Time Dashboard with Spark, Grafana, and InfluxDB

Downloadable solution code | Explanatory videos | Tech Support

Start Project

ETL vs ELT for Data Engineers

ETL (Extract, Transform, and Load) and ELT (Extract, Load, and Load) are two widespread data integration and transformation approaches that help in building data pipelines. Organizations often use ETL, ELT, or a combination of the two data transformation approaches. But which method is better? Read this blog for a detailed comparison between ELT vs. ETL to find out.

What is ETL?

ETL (Extract, Transform, and Load) is a data transformation and integration method. It starts with data selection from one or more data sources, then moves on to data transformation within a staging framework. Finally, you can store the collected and altered data in a warehouse for storage and analysis. 

What is ETL?

The following are the core operations of a standard ETL process:

  • Extract- Input data is extracted from an initial data source and temporarily stored in a staging area.

  • Transform- Data is validated, cleaned, and modified in the staging area so that you can integrate it with a target system.

  • Load- Finally, you can store the data in a data warehouse or other storage systems. Data scientists, analysts, and line-of-business teams can use it to support business intelligence and other essential processes from this location.

What is ELT?

Extract, Load, Transform, or ELT refers to how a data pipeline duplicates data from a data source into a target location, such as a cloud data warehouse.

ELT involves three core stages-

  • Extract- Importing data from the source server is the initial stage in this process.

  • Load- The pipeline copies data from the source into the destination system, which could be a data warehouse or a data lake.

  • Transform- Organizations routinely transform raw data in various ways and use it with multiple tools or business processes.

ETL vs. ELT- How do they Differ ? Difference between the two

ETL vs. ELT- How do they Differ ?





ETL involves

  • extracting data,

  • loading it into a staging server,

  • transforming the data on a processing server, and

  • loading it into the storage server.

It consists of

  • extracting data and

  • loading it into the target server/data warehouse in its raw form.

  • Data transformation takes place as needed and uses the system’s processing capability.

Ideal for

The ETL procedure is ideal for on-premises, organized, and relational data models and works better for a lesser volume of data.

The ELT approach is suitable for structured and unstructured cloud-based data sources, and it is suitable mainly for large volumes of data.


ETL is a time-consuming method as it first loads the data into staging, then into the target system. Lastly, it waits for data transformation to occur, so the waiting time increases as data size grows.

The ELT method is faster since the data loading into the target system happens only once. The size of the data has no impact on the speed of the ELT process.


ETL is ideal for importing data lakes because it connects to existing systems and can filter and integrate data.

ELT is an excellent option for importing data from a data lake or implementing SQL-based transformations.  But, it does not offer the same level of support for existing source systems.


Most ETL tools perform optimally with on-premise storage servers, making the whole process expensive.

The ELT process involves no additional hardware expenses.


ETL eliminates any confidential or vital data to keep it safe from hackers before storing it in the warehouse. The majority of ETL tools are HIPAA, CCPA, and GDPR-compliant.

Since all raw data is input directly into the warehouse, data leakage and hacking are risks.

Moreover, if the warehouse is in a foreign nation, it may breach international data compliance standards.


ETL vs. ELT- Pros and Cons

The most common pros and cons of ETL and ELT transformation approaches are as follows-

Pros of ETL

  • Consumption of Minimal Resources

The ETL method's focused load approach guarantees that the storage server only holds essential data. This results in lesser resource use and, as a result, reduced administrative expenses.

  • Data Security

Another advantage of more advanced ETL tools is that they offer additional capabilities to meet specific data security and compliance needs.

  • Supports Multiple Tools

The maturity of ETL tools has led to more outstanding support for a wide range of technologies. It means you'll be able to invest less time figuring out how to make different technologies function simultaneously.

  • Minimal Computing Expenses

The ETL method generates results that ensure only relevant data is collected and loaded into the data warehouse, lowering computing and administration costs.

Pros of ELT

  • Open-Source Cloud Support

The ideal ELT solutions often use open-source cloud services that collaborate to boost upgrades, security, and compliance throughout the organization. Open source ELT leads to global communities that minimize data concerns as they occur in your network.

  • Easily Auditable

ELT retains raw data which makes it easy to analyze each phase of the data pipeline, and also, detecting a problem is faster when all of the outcomes are available.

  • Scalability

ELT can be highly adaptable when using raw data. This, paired with the fact that data is readily available, simplifies the development process.

Cons of ETL

  • Outdated Technology

ETL, according to professionals, is obsolete because it relies on old infrastructures. It's not a cloud service and cannot handle modern data pipelines like streaming analytics.

  • Time-Intensive

Sometimes, a data processing technique having a fixed target location can be a problem. If you require additional data missing in previous projects, you will have to wait for the next launch, which can take several months.

Cons of ELT

  • Need for Data Lakes

If you have direct access to raw data through a data lake, ELT is the ideal choice. However, this necessitates the use of a data lake by businesses.

  • Lack of Run-Time Data

There is a loss of entire run-time tracking data and insights when implementing ELT for data warehouses.


ETL vs. ELT Use Cases | ETL vs. ELT- When to Use Which One?

Both ETL and ELT are useful data transportation and transformation techniques. Employing an OLAP data warehouse or a cloud-based data lake may play a role in determining which approach is optimal for your organization. Below are some potential use cases of the two methods-

ETL Use Cases

ETL is a great approach when a client needs a specific data format as it alters data before loading. ETL works best when

  • there is a mismatch in supported data types between the source and destination.

  • There is a constraint on the target server’s abilities to rapidly increase computation.

  • You want to store all structured and unstructured data in your organization, irrespective of the size.

  • There is a need to access a readily available community of ETL experts and toolsets;

  • There are necessary resources to manage data lakes.

  • There are security concerns that make retaining raw data difficult, etc.

ELT Use Cases

ELT is a great approach when targeting a cloud-native data warehouse like Snowflake, Amazon Redshift, Google BigQuery, or Microsoft Azure SQL Data Warehouse. ELT works best in situations where

  • smaller datasets don't entail extensive transformations; 

  • you possess the resources to retain ELT experts,

  • a robust cloud-based target central database analyzes incoming data streams,

  • organizations don’t need to adhere to GDPR or other regulatory requirements.

ETL vs. ELT- Azure | How Does Azure implement ETL and ELT?

One of the most significant data innovations has been the migration of analytics workloads to public cloud services, such as Microsoft Azure. This section will look at how you can implement ETL and ELT in Azure.

How does Azure support ETL and ELT methods?

Azure Data Factory is a Microsoft Azure data migration service that assists Azure users in creating ETL and ELT pipelines for their business data.

It enables users to

  • import and transform data from various on-premise and cloud-based services,

  • release the transformed data, send it to a data storage or analytics engine, and

  • analyze the data streams using a visual interface.

You can use Azure Data Factory to build and manage data-driven workflows or pipelines that can input data from many sources. With compute services like Azure HDInsight Hadoop, Azure Databricks, and Azure SQL Database, you can create extensive ETL/ELT processes that modify data dynamically. However, Azure Data Factory is not a complete ETL/ELT solution on its own. This is because it specifies control flows that can carry out various tasks, some of which may or may not be relatable to a data source.

Let us now understand how Azure Data Factory supports ETL and ELT implementations -

ETL in Azure

Source: Azure documentation

This sample model is significant for businesses planning to adopt Data Factory as their primary cloud-based ETL engine while merging existing SSIS packages into their new cloud data pipeline.

Companies can use their current ETL packages with Data Factory, cutting the overall budget for on-premises ETL implementation. This example shows how to use existing SSIS packages as part of a new cloud data pipeline created using Azure Data Factory v2.

  • Dataflows- Blob storage acts as a data retrieval source for Data Factory. The model uses Blob storage to load data into Data Factory.

  • Data Processing- The SQL Server Integration Services uses the on-premises ETL packages to run task-specific workloads. The above Data Factory pipeline uses the Integrated Runtime to perform an SSIS job hosted on-premises using a stored procedure. 

  • Data Analysis and Reporting- Azure Data Factory performs data cleansing tasks to arrange the data further down the pipeline. After completing the data purification task, a copy task loads the clean data into Azure.

  • Data Loading-  The clean data goes into Azure Synapse Analytics tables. Azure Synapse centralizes data on the cloud and uses regular ANSI SQL queries to retrieve it.

This is how Azure data Factory implements ETL pipelines.

Access to a curated library of 230+ end-to-end industry projects with solution code, videos and tech support.

Request a demo

ELT in Azure

Source- Azure documentation

This sample design depicts how to execute incremental loading in an extract, load, and transform (ELT) pipeline. Azure Data Factory automates the ELT pipeline. The pipeline transfers the most recent OLTP data from an on-premises SQL Server database into Azure Synapse in a batch process. For analysis, it transforms transactional data into a tabular format.

  • Data Source- The source data is stored locally in a SQL Server database. In addition, this model loads and combines an external data set with the data from the OLTP database.

  • Data Ingestion and Storage- It uses blob storage as a buffer site for the source data before importing it into Azure Synapse. Then, it enables massive parallel processing (MPP) and performs high-speed analytics using Azure Synapse. Also, this model uses Data Factory to coordinate the several phases of the ELT process.

  • Data Analysis and Reporting- Azure Analysis Services load the semantic model. Power BI evaluates the semantic model stored in Analysis Services.

  • Authentication- Azure Active Directory (Azure AD) validates users who link to the Analysis Services server through Power BI. By employing a service principal or Managed Service Identity (MSI), Data Factory can also authenticate to Azure Synapse using Azure AD. 

This is how Azure helps build an ELT pipeline using Data Factory.


ETL Projects for Data Engineers to Practice

Here are some interesting and fun project ideas for you to learn more about the ETL approach-

  • Yelp Data Analysis using Azure Databricks

This ETL project idea will guide you in understanding the ETL process, which entails gathering, processing, and transforming data to obtain actionable insights. You'll also learn about Azure Databricks, Data Factory, and Storage. The Yelp dataset includes company information, user reviews, and other publicly available data for personal, educational, and scholarly usage.

Source Code- Yelp Data Analysis using Azure Databricks

  • YouTube Data Analytics using AWS Glue, Lambda, and Athena

In this Python ETL project, you'll develop an ETL Data Pipeline for YouTube data using Athena, Glue, and Lambda. This project will allow you to manage, simplify, and analyze structured and semi-structured YouTube video data based on video categories and trending metrics. The dataset comprises daily popular YouTube video data (in CSV files). Begin by importing data into Amazon S3, then creating ETL tasks with AWS Glue. Use the Glue crawler to add/modify tables in the data catalog. You can use AWS Lambda to define functions, making it easy to run your code. Use AWS Athena to create interactive queries and AWS IAM services to gain secure access to all AWS resources.

Source Code-  YouTube Data Analytics using AWS Glue, Lambda, and Athena

  • An End-to-End ETL Pipeline on AWS EMR Cluster

This project will help you better understand how to build a Big Data pipeline from the ground up on AWS. This advanced-level ETL project includes everything a data engineer should know, including analyzing sales data using highly competitive big data stacks like Amazon S3, EMR, and Tableau. Begin by uploading the raw sales data to Amazon Web Services (AWS S3). Create an EMR cluster with the required specifications on AWS. Create an external Hive table on top of S3 for staging reasons. Use Hive as an ETL tool, which means you'll set up many ETL pipelines to store the processed data in a table. Lastly, employ Tableau to display the clean and updated data in various graphs.

Source Code- Build an End-to-End ETL Pipeline on AWS EMR Cluster

  • Amazon Customer Reviews Analysis using Redshift ETL

This ETL project leverages Amazon Redshift Cluster, AWS Glue, and Step Functions to collect source data and achieve faster analytical insights. This project's ETL solution employs in-house AWS tools to execute end-to-end loading and obtain business insights. The cluster uses Amazon Redshift Spectrum to read data from S3 and load it into an Amazon Redshift table. The cluster executes an aggregation query and exports the results to another Amazon S3 location using UNLOAD. In case of a pipeline failure, the state machine will send a message to an Amazon Simple Notification Service topic.

Source Code- Amazon Customer Reviews Analysis using Redshift ETL, AWS Glue, and Step Functions

  • Real-Time E-commerce Dashboard with Spark, Grafana, and InfluxDB

In this project, you will build a real-time e-commerce user analytics dashboard. This project creates user purchase events in Avro format over Kafka for the ETL pipeline. The Spark Streaming Framework performs batch and real-time join actions on user purchase and demographic events. Through Kafka connect, events from Kafka streams move to influxDB. Grafana creates graphs by connecting to several databases, including influxDB and MySQL.

Source Code- Real-Time E-commerce Dashboard with Spark, Grafana, and InfluxDB

ETL vs. ELT - Which is Better?

The primary goal of comparing ETL vs. ELT is to determine when to use which approach for your data engineering project rather than to choose one over the other. ETL is suitable for transferring data from older systems into a data lake. In contrast, ELT is excellent for self-service analytics, allowing data engineers and analysts to add new data for relevant reports and dashboards at any time. ELT is ideal for most current analytics workloads since it significantly decreases data input time compared to the old ETL approach. This enables businesses to make better decisions and improve their analytical capabilities. However, some organizations will continue with the ETL method since it fits their deployment strategies.


Is Data Lake ETL or ELT?

Data Lakes are relatable to the ELT method. If you wish to use ELT, you'll need to create a Data Lake. You extract data from local files, upload it into your Cloud Storage Data Lake, and then process and clean the data. The primary approach is to start exploring and analyzing your data, discover it is unclean and clean it up.

What is ELT in MySQL?

The ELT() in MySQL returns the string expression (or string value) at the index position supplied in the first argument. In MySQL, the basic syntax for ELT is as follows:

SELECT ELT (Value, Str1, Str2,...., StrN)

FROM Source




Access Solved Big Data and Data Science Projects

aws image

Start Your First Project

Learn By Doing

What Users are saying..

profile image


Co-op Engineer, Machine Learning at AMD
linkedin profile url

I recently came across an effective site called ProjectPro. This website has many end-to-end solved projects, aimed at data science and big data professionals of all levels. Whether you are a... Read More