Implementing ELT on an Open Lakehouse with Apache Iceberg or Delta Lake

In today's data-driven landscape, organisations are increasingly turning to lakehouse architectures to streamline their data management processes. By combining the best of data lakes and data warehouses, lakehouses enable businesses to store vast amounts of data in its raw form while also providing the tools needed for analysis and reporting. A prime example of this shift can be seen in companies like Netflix, which leverage open lakehouse technologies such as Apache Iceberg and Delta Lake to enhance their data accessibility and analytics capabilities.

Implementing an ELT (Extract, Load, Transform) strategy on an open lakehouse can significantly improve the speed and efficiency of data processing. Unlike traditional ETL (Extract, Transform, Load) methods, ELT allows organisations to load raw data into the lakehouse before transforming it, making it easier to work with diverse data types and sources. This article will explore how to effectively implement ELT using Apache Iceberg or Delta Lake.

Understanding the Open Lakehouse Architecture

The open lakehouse architecture integrates features of both data lakes and data warehouses, providing a unified platform for all data needs. Apache Iceberg and Delta Lake are two leading technologies that facilitate this architecture. Iceberg, developed by Netflix, focuses on table formats that support complex data types and transactions, while Delta Lake, created by Databricks, adds ACID transaction capabilities to Apache Spark and big data workloads.

For instance, a financial services company can use Delta Lake to manage their transactional data while leveraging Apache Iceberg for their analytical workloads. By doing so, they can ensure data consistency and integrity while also enabling rapid data access for reporting and analysis.

Implementing ELT with Apache Iceberg

To implement an ELT strategy using Apache Iceberg, organisations should first set up a compatible data processing pipeline. Iceberg supports various data processing engines like Apache Spark, Flink, and Trino, allowing flexibility in data ingestion and transformation. A practical approach is to use Apache Spark to extract data from various sources, load it into Iceberg tables, and then perform transformations on the data stored in those tables.

For example, an e-commerce platform can extract user interaction data from their website and load it directly into Iceberg tables. Once loaded, they can use Spark SQL to perform transformations such as aggregating user behaviour metrics. This method not only simplifies the data processing workflow but also provides the ability to handle large volumes of data efficiently.

Implementing ELT with Delta Lake

Delta Lake provides a robust framework for implementing ELT by enabling incremental data loads and ensuring data quality through schema enforcement and evolution. To begin, organisations can use Databricks or Apache Spark to extract data from various sources such as APIs or databases, load it into Delta tables, and subsequently transform it with SQL or DataFrame operations.

Consider a retail company that collects sales data from multiple channels. By using Delta Lake, they can periodically load new sales records into Delta tables and apply transformations to calculate key performance indicators (KPIs) like total sales and average order value. This approach ensures that the data remains up-to-date and accessible for business intelligence tools without the need for extensive ETL processes.

What to do now

1. Assess your current data architecture. Identify whether your organisation is using a traditional data warehouse or a data lake and evaluate the feasibility of transitioning to a lakehouse architecture with Apache Iceberg or Delta Lake.
2. Choose your technology. Depending on your existing data ecosystem, decide between Apache Iceberg or Delta Lake. Consider factors such as data volume, processing requirements, and compatibility with your current tools.
3. Design your ELT pipeline. Create a blueprint for your ELT process, detailing how data will be extracted, loaded, and transformed. Ensure that you include data quality checks to maintain data integrity.
4. Implement and test. Begin implementing your ELT pipeline using Apache Spark or Databricks. Run tests to validate that the data is being loaded and transformed as expected, addressing any issues that arise.
5. Monitor and optimise. Continuously monitor the performance of your ELT process. Use metrics and logs to identify bottlenecks or inefficiencies, and optimise your pipeline for better performance.

How to measure it

• Data Load Time: Measure the time taken to load data into the lakehouse to identify potential delays in the ELT process.
• Transformation Accuracy: Track the accuracy of transformed data against predefined benchmarks to ensure data quality.
• Query Performance: Evaluate the speed and efficiency of data retrieval queries to assess the effectiveness of the ELT implementation.
• User Adoption: Monitor the number of users accessing the lakehouse for analytics to gauge the impact of the ELT process on data accessibility.

Implementing ELT on an open lakehouse using Apache Iceberg or Delta Lake is not just a technical upgrade; it’s a strategic move that enhances data agility, accessibility, and reliability. By following the outlined steps and continuously measuring success, organisations can leverage their data assets to drive informed decision-making and foster innovation.