Your businesses might operate on exponentially expanding datasets. The challenge lies in managing and utilizing these large datasets effectively. This is where ELT becomes essential. It offers a sophisticated approach to data management and integration between several systems by streamlining data processing and enhancing analytical capabilities. This will help meet your evolving business needs with greater efficiency.
This article explores why data engineers are moving to ELT solutions, the key differences between ELT and ETL, and its use cases.
What Is ELT?
ELT, which stands for Extract, Load, Transform, is a data integration process that prioritizes speed and flexibility. It involves extracting data from multiple sources and directly loading it into a destination like a data warehouse or data lake without performing instant modifications. Transformations are applied whenever required, either within the target environment or by integrating with external tools.
How the ELT Process Works?
The ELT process works based on the following three steps:
- Extract: The extraction step involves gathering raw data from multiple sources, such as databases, files, SaaS applications, application events, and more. You can then temporarily store the extracted data in any database's staging area.
- Load: This step allows you to load the extracted data from a staging area into a target system, usually a data lake. This makes the data ready for downstream applications.
- Transform: Once the data is in the target system, you can apply transformations as required. This can include mapping, normalization, cleaning, formatting, etc.
How Is ELT Different than ETL?
Let’s take a look at the key differences between ELT and ETL:
To learn how ELT differs from ETL in more detail, refer to our comprehensive ETL vs ELT blog.
Why Data Engineers Are Moving to ELT Now?
Data engineers are increasingly moving to ELT for its numerous advantages over traditional ETL. Here are a few benefits:
- High Flexibility: ELT offers greater flexibility in data exploration and transformation by allowing you to load raw data first and apply changes later.
- Access to Original Data: This approach preserves raw data. It enables data engineers to revisit the original data without re-extracting it from the source.
- Faster Time-to-insights: By offloading transformations to the target system, ELT accelerates the data ingestion and analysis process.
ELT Tools in the Market Now
Take a look at the most popular ELT tools available in the market:
Airbyte
Airbyte is a no-code ELT data integration platform that offers 350+ built-in connectors to help you smoothly migrate from multiple sources to your preferred destination. If you cannot find a connector, you can build one according to your integration requirements using the Connector Development Kit.
Key Features of Airbyte
- Modern GenAI Workflows: You can automate your AI workflows by loading semi-structured or unstructured data directly into popular vector stores like Milvus, Weaviate, Pinecone, and more. Airbyte’s integrated support for RAG-specific transformations like LangChain-powered chunkings and OpenAI-enabled embeddings allows you to simplify your data integration process within a single operation.
- Developer-Friendly Pipeline: PyAirbyte is an open-source Python library that allows you to build custom pipelines. It helps access all Airbyte connectors programmatically to extract data from multiple sources within your Python workflows.
- Efficient Transformations: With dbt integration, Airbyte allows you to create and apply custom data transformations to suit your destination needs.
- Open-Source Version: Airbyte also provides an open-source edition that allows you to deploy your Airbyte instance either locally using Docker or on a virtual machine. This edition helps you use most of Airbyte's features, including 300+ connectors, schema propagation, and more.
- Data Security: Airbyte supports TLS, SSL, and HTTPS encryption, along with SSH tunneling, for safe data transfer. Airbyte also complies with ISO 27001 and SOC 2 Type II assessment regulatory standards for secure data management throughout the integration process.
- Vibrant Community: Airbyte’s active forum actively shares knowledge and experiences. The platform's active forum serves as a valuable resource for troubleshooting, data integration strategies, and deployment tips.
Hevo Data
Hevo Data is a no-code platform that helps you build an ELT data pipeline by providing a library of 150+ pre-built connectors. Its user-friendly interface and automation capabilities make it a popular choice for streamlining data pipelines.
Key Features of Hevo Data
- Automatic Schema Management: Hevo Data automatically identifies the format of the source database and copies it to the destination schema, reducing manual schema management.
- Data Transformation: Hevo Data offers drag-and-drop transformation blocks and Python-based transformation scripts to help you standardize the data, making it compatible with the destination format.
Stitch Data
Stitch Data is a fully managed ELT data integration platform with a no-code interface. It allows you to quickly transfer your data using its 140+ data sources into a data lake or data warehouse.
Key Features of Stitch Data
- Automatic Scaling: Stitch Data’s robust infrastructure can handle billions of records daily with its automatic scaling feature. This feature allows you to adjust to growing data volumes without the need for manual hardware provisioning or workload management.
- Pipelining Scheduling: The Stitch Data enables you to set up a data pipeline to run at certain intervals or in response to specific triggers. This ensures that you always have timely access to the most relevant data.
ELT Use Cases
Here are a few use cases of ELT:
Health Care
ELT can help quickly process data from electronic health records, electronic medical records, remote patient monitoring, and other systems used by healthcare professionals.
Using the ELT approach, Intermountain Healthcare loads 300 CSV files of patient data from multiple medical records in 10 minutes into a data analytics platform without manual coding. The industry can then quickly analyze the healthcare data, which enhances patient satisfaction.
Manufacturing
ELT enables manufacturing companies to process and analyze vast amounts of data from multiple sources. Manufacturers can gain up-to-date insights into their production processes, which allows them to make decisions that improve production and ensure resilience.
Rockwool, a leading manufacturer, utilized the ELT approach to integrate and process data from manufacturing facilities in 39 countries. This allowed Rockwool to analyze production information in real time, which led to an increase in its total sales by 23%.
Financial Services
ELT is beneficial for financial institutions such as banks, capital markets, and insurance agencies to thrive in agile environments. It helps prevent fraud, complies with regulations, and enhances customer satisfaction.
Western Union processes over 1,700 transactions per minute using the ELT approach. It uses ELT as part of its data management system to handle complex transactional data cost-effectively.
Limitations of Using ELT
While ELT provides several benefits, you may encounter the following challenges while implementing it:
- Data Quality: Loading raw data into your destination without pre-transformation may affect data quality and accuracy. You may need to perform additional processing to clean and standardize the data, which slows down analysis.
- Data Governance: Establishing clear ownership and access control can be complex. It involves ensuring that only authorized personnel have the right permissions to access raw data and perform transformation processes.
- Storage Resource Constraints: The ELT approach requires substantial storage resources to store your enormous datasets. This can lead to increased costs for managing these significant storage resources.
Summary
ELT is an effective approach to modern data integration. By leveraging cloud-based platforms, you can handle large data volumes, enable faster data loading, and provide greater scalability compared to ETL methods. With this approach, you can quickly unlock valuable insights from your data.
FAQs
What is an example of ELT in real-time?
An example of a real-time ELT is the stock market, which generates vast amounts of data continuously and demands immediate analysis for informed decision-making. By employing ELT, financial institutions can rapidly ingest stock prices, trading volumes, and market data into the data warehouse or data lake. Transformations can then be applied to extract valuable insights for trading.
Is ELT a data pipeline?
Yes. An ELT data pipeline can help you extract data from varied sources and load it into a preferred destination system, such as a data lake. Then, you can transform it whenever necessary to meet your business requirements.
Is Airbyte an ETL or ELT?
Yes. Airbyte is an ELT platform that enables you to build a data pipeline to automate large-scale data integration through its 300+ pre-built connectors.
Is ELT an alternative to ETL?
Yes. ELT is an alternative to ETL, and the key difference between them is when data transformation takes place.
Which databases are good for ELT?
There are several good database options for the ELT process, including MySQL, PostgreSQL, Oracle, SQL Server, and MongoDB.
Does ELT cost more than ETL?
No, the cost of an ELT process is lower than that of ETL as ELT leverages cloud data warehouses’ processing power for transformations.
In ELT, does the transformation happen in the data warehouse or on its way to the end source?
In ELT, the transformation occurs within the data warehouse, not when the data is on its way to the final source.