ELT: The Modern Data Integration Approach for Data Lake Houses

Last updated: 01 Jul 2024

ELT, short for Extract, Load, Transform, is a data integration methodology that prioritizes loading data into a data storage system first before performing transformations. This approach contrasts with the traditional ETL (Extract, Transform, Load) methodology, where data is transformed before loading.

The primary difference lies in the timing of data transformations. In ELT, transformations happen after data is loaded into the target system, typically a data lake. This approach allows for a faster and more efficient ingestion process, especially when dealing with large datasets.

Understanding ELT in the Context of Data Lake Houses

The rise of data lake houses has further emphasized the importance of ELT. A data lake house is a modern data architecture that combines the benefits of data lakes and data warehouses. It allows for storing raw data in a data lake while providing tools for structured analysis, like those offered by a data warehouse. If you arent familiar with lake houses you should check out our article What is A Data Lakehouse?

Here's why ELT is a perfect fit for data lake houses:

• Data Variety: Data lakes are designed to handle diverse data formats, from structured data (like CSV files) to unstructured data (like images, videos, and text documents). ELT allows for the ingestion of all these data types without extensive pre-transformation.
• Scalability: Data lakes can handle vast amounts of data, making ELT's approach of loading data first more efficient. This eliminates the need for complex pre-transformation processes that can become bottlenecks for large datasets.
• Flexibility: Data lake houses offer flexibility in how you analyze data. With ELT, you can apply transformations as needed, enabling you to experiment with different data analysis techniques without needing to pre-transform the entire dataset.
• Cost-Effectiveness: ELT often reduces the need for complex data processing infrastructure, making it a more cost-effective approach for handling large datasets.

The ELT Process in Action: A Step-by-Step Guide

Let's break down the ELT process into distinct steps:

1. Data Extraction:

   • Sources: The first step involves extracting data from various sources, which can include databases, APIs, file systems, cloud storage services, and more.
   • Tools: A variety of tools are available for data extraction, including:
     • ETL Tools: Traditional ETL tools like Informatica, Talend, and SSIS can also be used for data extraction.
     • Open-Source Tools: Apache Spark, Flink, and Kafka are popular open-source tools for extracting data from various sources.
     • Cloud-Based Services: Cloud platforms like AWS Glue, Azure Data Factory, and Google Cloud Dataflow offer managed extraction services.

2. Data Loading:

   • Target: Data Lake: The extracted data is loaded into the data lake, which is a massive, centralized storage repository for raw data.
   • Formats: Data is typically stored in its native format within the data lake, providing flexibility for later analysis. Popular file formats include:
     • Parquet: A columnar storage format optimized for efficient data retrieval.
     • ORC: A highly efficient, compressed format designed for data warehousing.
     • Avro: A schema-based format that allows for self-describing data structures.

3. Data Transformation:

   • Purpose: The final step involves transforming the data loaded into the data lake. This transformation can include:
     • Cleaning and Normalization: Removing duplicates, handling null values, and standardizing data formats.
     • Data Enrichment: Adding context or additional information to the data.
     • Data Aggregation: Combining data from multiple sources or performing summary calculations.
   • Tools: Transformation is often performed using:
     • Spark: An open-source distributed processing framework that's widely used for big data analysis and transformation.
     • Hive: A data warehouse software that provides SQL-like querying capabilities for data stored in Hadoop-based data lakes.
     • Python Libraries: Pandas and PySpark offer powerful data manipulation capabilities within Python.

Benefits of ELT for Data Lake Houses

The ELT approach offers significant benefits for data lake houses, making it the preferred data integration methodology in modern data architectures. Here's a breakdown of the key advantages:

1. Faster Data Ingestion: By loading data first, ELT eliminates the processing overhead associated with pre-transformation. This results in faster data ingestion times, which is crucial for handling the massive volumes of data generated in today's digital landscape.

2. Flexibility and Scalability: ELT allows you to load data in its raw format, providing flexibility for future analysis. You can transform data on demand, adapting to evolving data needs and analytical requirements without needing to pre-transform the entire dataset. This approach also makes ELT highly scalable, as it can handle ever-increasing data volumes without significant performance degradation.

3. Cost-Effectiveness: ELT often requires less complex data processing infrastructure, resulting in cost savings. You can leverage the cost-efficient storage capabilities of data lakes and perform transformations only when necessary.

4. Improved Data Quality: The ability to transform data after loading allows for more granular control over data quality. You can perform specific cleaning and normalization steps after understanding the data's characteristics and requirements, leading to more accurate and reliable analysis.

5. Enhanced Data Governance: ELT facilitates better data governance by enabling you to apply transformations and data quality checks based on specific business rules and regulatory requirements. This ensures data integrity and compliance.

ELT: A Modern Data Integration Approach

ELT has emerged as a critical component of modern data architectures, particularly data lake houses. By offering speed, flexibility, cost-effectiveness, and scalability, ELT empowers organizations to leverage the full potential of their data for business insights and competitive advantage.

As the volume and complexity of data continue to grow, ELT is likely to remain a cornerstone of modern data integration strategies, enabling organizations to unlock the value of their data and drive data-driven decision-making. Check out our full article on What is Data Integration?

Related Concepts: ETL

While ELT is the preferred approach for data integration in data lake houses, it's important to understand the traditional ETL (Extract, Transform, Load) methodology.

Key Differences between ELT and ETL:

Feature	ELT	ETL
Order	Extract, Load, Transform	Extract, Transform, Load
Data Type	Raw, Unstructured, Semi-structured	Cleansed, Structured
Transformation	After loading	Before loading
Performance	Fast loading, slower transformation	Slow loading, faster transformation
Scalability	Highly scalable	Can be challenging for large datasets
Flexibility	High	Lower
Cost	Often more cost-effective	Can be more expensive

Conclusion

ELT represents a modern and efficient approach to data integration that aligns seamlessly with the capabilities of data lake houses. This approach offers numerous benefits, including faster data ingestion, flexibility, scalability, cost-effectiveness, and improved data quality. As organizations continue to embrace data-driven strategies, ELT is poised to play an increasingly important role in enabling them to leverage their data assets for competitive advantage and business growth.

Author: Grayson Campbell