What You’ll Learn in This Blog
- The difference between ETL and ELT
- The benefits of using an ELT over ETL or “hand-cranked” code
- How the Cloud, with the next generation of tools, can simplify the data integration landscape
- Key data integration terms
ETL vs ELT
Let’s start by understanding the difference between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform).
ETL
ETL emerged in the 90s with the rise of data warehousing. The process involved:
- Extracting data from source systems
- Transforming the data integration process
- Loading the transformed data into a database for analysis and reporting
Before ETL tools existed, this was done using hand-coded scripts, which was time-consuming and lacked lineage and maintainability. ETL tools like OWB, DataStage, and Informatica simplified the process by performing transformations on application servers rather than source systems or target databases.
The benefits of ETL tools include:
- Lineage tracking
- Logging and metadata
- Simplified slowly changing dimensions (SCD)
- Graphical user interface (GUI)
- Improved collaboration between business and IT1
ELT
ELT tools leverage the power of the underlying data warehouse by performing transformations within the database itself. This minimizes the need for excessive data movement and reduces the latency that typically accompanies traditional ETL processes.
With the rise of Hadoop during the “Big Data” era, computation was pushed closer to the data, leading to a more siloed approach between traditional data warehouses and big data systems. This shift increased the need for specialized programming skills, complicating data accuracy, lineage tracking, and overall management in complex environments.
The Next Generation of ELT Tools
Cloud-based data warehouses like Snowflake, Google BigQuery, and AWS Redshift have enabled the resurgence of ELT. Next-generation ELT tools like Matillion fully utilize the underlying cloud databases for computations, eliminating the need for data to leave the database.
Modern analytical platforms like Snowflake can satisfy both data lake and enterprise data warehouse requirements, allowing the use of a single ELT tool for transformations. This reduces the total cost of ownership (TCO) and development time while improving maintainability and impact assessment.
Streaming and Governance
Streaming enables real-time analytics by combining data sources to help businesses make quick decisions. Tools like HVR can replicate data cost-effectively, blending replication with ELT (RLT).
Governance is crucial for ensuring data lineage, metadata, audit, and log information, especially for compliance with regulations like GDPR. ELT tools like Matillion provide this information easily through their GUI, generated documentation, or APIs to connect with data governance tools.
DataOps and Migration
The rise of DataOps emphasizes the need for easy deployment of changes using tools like Git. Modern ELT tools support agile working by building deployment pipelines and regression testing capabilities, allowing regular changes to accommodate source system updates or new data sources while ensuring data integrity.
Migrating to a modern analytical platform can be achieved by transitioning from a legacy analytics platform. Leading Edge IT can assist with this process.
Conclusion
Cloud-based platforms such as Snowflake offer immense scalability for compute tasks, making them ideal for modern data platforms. Incorporating ELT tools like Matillion further optimizes these setups by streamlining workflows and reducing the total cost of ownership (TCO). By integrating replication solutions such as HVR, you can automate data synchronization across environments. When paired with ELT and cloud-based data warehouses, these tools enable efficient, reusable templates with shared components, eliminating manual coding and fostering agility in data management. This combined approach drives efficiency, scalability, and flexibility in your data architecture.
Cyber Whale is a Moldovan agency specializing in building custom Business Intelligence (BI) systems that empower businesses with data-driven insights and strategic growth.
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