Transforming Data Ingestion and ETL with Modern Table Formats
In the ever-evolving data landscape, ETL (Extract, Transform, Load) processes remain crucial. Recent downtime with AWS Glue disrupted our OLAP system, highlighting the need for a more resilient and cost-effective solution. This led us to explore Lakehouse architectures and modern table formats like Apache Iceberg. These formats address the challenges of traditional ETL processes and data ingestion, offering significant advantages for modern data platforms.
Challenges with Traditional ETL Processes
ETL processes are inherently complex due to the need to extract data from various source systems and load it into data warehouses or lakes. These processes often involve:
Full Table Extraction: Extracting entire tables repeatedly, which is straightforward but costly in terms of storage.
Incremental Extraction: Extracting only changed data, which is efficient but complex to implement and manage.
Change Data Capture (CDC): Continuously tracking and transferring individual changes, which requires sophisticated handling but is highly efficient.
Custom ETL: Tailored extraction methods for specific applications, often involving proprietary tools.
These methods each have their trade-offs, with full extractions being simpler but more storage-intensive, and incremental or CDC methods being more efficient but more complex.
How Lakehouse Table Formats Like Apache Iceberg Help
Lakehouse architectures bridge the gap between data warehouses and lakes, combining the best features of both.
A Lakehouse combines the ACID transactions and data governance of enterprise data warehouses with the flexibility and cost-efficiency of data lakes to enable business intelligence (BI) and machine learning (ML) on all data
New data storage methods like Apache Iceberg, Hudi, and DeltaLake fix common problems by letting you change data easily, just like in a regular database. When you update information, these systems only alter the parts that have changed, not everything. This gives you a clear view of all your data, like looking at a complete table. It also saves a lot of space on your computer or in the cloud because you don’t need to make a whole new copy of all your data every time you want to change something small. This approach makes working with big sets of data much easier and cheaper.
Apache Iceberg and similar formats (Hudi, DeltaLake) offer:
ACID Compliance: Ensuring reliable transactions with support for MERGE, INSERT, and UPDATE operations.
Cost Efficiency: Reducing storage costs by only storing delta changes instead of full snapshots.
Time Travel: Enabling access to historical data and tracking changes over time.
Separation of Storage and Compute: Flexibility to use different tools for various data processing needs.
Improved Query Performance: Utilizing metadata and compaction to optimize data access.
Implementation: AWS and Apache Iceberg Case Study
We revamped our data architecture using AWS services and Apache Iceberg:
Transformation: AWS Lambda for processing and storing as Parquet files.
Data Integration: AWS Athena for querying and updating Iceberg tables.
Automation: Lambda functions triggered by EventBridge.
Key to this process is the MERGE INTO statement:
MERGE INTO database_name.table_name target
USING (SELECT * FROM source_table_name) source
ON target.key = target.key
WHEN MATCHED AND source.operation = 'DELETE' THEN DELETE
WHEN MATCHED AND source.operation = 'UPDATE' THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *
This statement stores only the updates, eliminating the need for full daily data snapshots. For organizations with numerous large datasets, this approach can result in significant cost savings. As an Iceberg table user, you’ll always have access to the most current full table view. Additionally, you can utilize the time-travel feature to revisit previous states of the data.
Results and Benefits
Challenges Faced
Metadata Management : Handling numerous metadata files created by Iceberg transactions.
Data Deletion : Managing deletes and old data effectively.
Simplifying Data Ingestion
By using Iceberg, we standardize and automate data ingestion processes:
Unified ETL: Extract data using CDC, full, or incremental methods, and load it into Iceberg tables with MERGE & INSERT INTO statements.
Reduced Storage Needs: Store only changes instead of full daily snapshots while retaining access to historical data.
Efficient Incremental Updates: Track changes using columns like updated_at and created_at.
Conclusion
Implementing Apache Iceberg has transformed our data ingestion processes, leading to substantial cost savings and improved efficiency. This approach provides a scalable and flexible solution for modern data platforms, making it easier to handle diverse data sources and processing requirements.
I hope this article inspires you to explore Iceberg and other modern table formats to enhance your data ingestion and ETL processes.
For more insights into how modern table formats can enhance data ingestion and ETL processes, check out this blog post that inspired some of our approach.
Connect and Learn More
I’m passionate about sharing knowledge and experiences in the field of data engineering. If you have any questions about implementing Apache Iceberg or modern ETL processes, or if you’d like to discuss data architecture strategies, please don’t hesitate to reach out.
You can connect with me on LinkedIn: Varun Bainsla
I’m always eager to engage in discussions, share insights, or offer guidance based on my experiences. Whether you’re looking to optimize your data infrastructure, tackle specific ETL challenges, or simply want to exchange ideas about the future of data engineering, I’m here to help.
Let’s continue learning and innovating together in this exciting field of data engineering!
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