ETL and ELT Data Processing Patterns
Why Are ETL and ELT Data Processing Patterns Important?
ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are foundational data processing patterns that underpin nearly every modern data solution. Understanding these patterns is critical because they define how data moves from source systems into storage and analytical platforms. For the DP-900 exam, Microsoft expects candidates to understand the differences between these two approaches, when each is appropriate, and how they relate to Azure services.
In real-world scenarios, organizations deal with data from dozens or hundreds of sources — databases, APIs, flat files, IoT devices, and more. ETL and ELT patterns provide structured approaches for ingesting, cleaning, reshaping, and delivering that data so it can be used for reporting, analytics, and machine learning.
What Is ETL (Extract, Transform, Load)?
ETL is a traditional data processing pattern that follows three distinct steps:
1. Extract: Data is read from one or more source systems. These sources can include relational databases, flat files (CSV, JSON, XML), APIs, SaaS applications, or on-premises data stores.
2. Transform: The extracted data is transformed before it is loaded into the destination. Transformations may include:
- Data cleansing (removing duplicates, handling null values)
- Data type conversions
- Filtering and aggregating rows
- Joining data from multiple sources
- Applying business rules and calculations
- Restructuring data to match the target schema
3. Load: The transformed data is loaded into the target data store, such as a data warehouse, a relational database, or a data mart.
In ETL, the transformation happens in a separate processing engine (sometimes called a staging area or an ETL tool) before the data reaches its final destination. This was the dominant approach when data warehouses had limited compute power and it was more efficient to transform data externally.
What Is ELT (Extract, Load, Transform)?
ELT follows a different order:
1. Extract: Data is read from source systems, just like in ETL.
2. Load: The raw data is loaded directly into the target data store (often a data lake or a modern cloud data warehouse) without being transformed first.
3. Transform: Transformations are performed within the target system, leveraging the compute power of the destination platform.
ELT has become increasingly popular with the rise of cloud-based platforms like Azure Synapse Analytics, Azure Data Lake Storage, and Azure Databricks, which offer massive, scalable compute resources. Because the destination system can handle heavy processing, there is no need for a separate transformation engine.
Key Differences Between ETL and ELT
Transformation Location:
- ETL: Transformation occurs in an intermediate processing engine (outside the target store).
- ELT: Transformation occurs inside the target data store.
Data Loaded to Destination:
- ETL: Only cleaned, structured, transformed data is loaded.
- ELT: Raw, unprocessed data is loaded first; transformation happens later.
Best Suited For:
- ETL: Traditional on-premises data warehouses, smaller data volumes, scenarios where you want to control exactly what lands in the warehouse.
- ELT: Cloud-based environments with scalable compute, big data scenarios, data lakes, situations where you want to preserve raw data for future use.
Speed of Ingestion:
- ETL: Slower ingestion because data must be transformed before loading.
- ELT: Faster ingestion because raw data is loaded immediately; transformation is deferred.
Flexibility:
- ETL: Less flexible — if transformation logic changes, you may need to re-extract and re-process from source.
- ELT: More flexible — raw data is preserved, so you can re-transform it as requirements evolve.
How ETL and ELT Work in Azure
Microsoft Azure provides several services that support ETL and ELT patterns:
Azure Data Factory (ADF): A cloud-based data integration service that orchestrates and automates both ETL and ELT workflows. ADF uses pipelines with activities to extract data from sources, optionally transform it using data flows (for ETL) or pass it to other compute services, and load it into destinations. ADF supports connections to 90+ data sources.
Azure Synapse Analytics: A unified analytics platform that supports ELT patterns natively. You can load raw data into a Synapse dedicated SQL pool or a data lake and then use SQL scripts or Spark notebooks to transform the data in place.
Azure Databricks: A Spark-based analytics platform ideal for ELT on big data. Raw data can be loaded into Azure Data Lake Storage and transformed using Databricks notebooks.
Azure Data Lake Storage: A scalable storage solution that acts as the landing zone for raw data in ELT patterns. It supports structured, semi-structured, and unstructured data.
SQL Server Integration Services (SSIS): A traditional on-premises ETL tool that can also run in Azure via Azure Data Factory's SSIS Integration Runtime.
When to Use ETL vs. ELT
Use ETL when:
- You need to comply with data privacy regulations and want to cleanse or mask sensitive data before it enters the target system.
- You are working with a traditional, on-premises data warehouse that has limited processing capacity.
- The volume of data is relatively small and manageable.
- You want to minimize storage costs by only storing transformed, relevant data.
Use ELT when:
- You are working in a cloud environment with scalable compute and storage.
- You are dealing with large volumes of data (big data scenarios).
- You want to preserve raw data for future analysis, auditing, or reprocessing.
- Your transformation requirements are complex and benefit from the power of the destination engine (e.g., Spark or Synapse SQL).
- You want faster data ingestion times.
Common Steps in Both Patterns
Regardless of whether you use ETL or ELT, the core activities are similar:
1. Identify source systems — Determine where data comes from.
2. Define the extraction method — Full extraction, incremental extraction (only new/changed data), or real-time streaming.
3. Apply transformations — Clean, reshape, aggregate, and enrich data (either before or after loading).
4. Load data — Write the data to the target destination.
5. Schedule and orchestrate — Use tools like Azure Data Factory to schedule pipelines, handle dependencies, and manage retries.
6. Monitor and validate — Ensure data quality, completeness, and accuracy.
Real-World Example
Imagine an e-commerce company that collects data from its website logs, a CRM system, and a payment gateway:
ETL Approach: Azure Data Factory extracts data from all three sources, applies mapping data flows to cleanse and join the data in a staging area, and then loads the transformed data into an Azure SQL Database for reporting.
ELT Approach: Azure Data Factory extracts data from all three sources and loads the raw data into Azure Data Lake Storage. Then, Azure Synapse Analytics or Databricks runs SQL or Spark transformations on the raw data to produce analytics-ready datasets.
Exam Tips: Answering Questions on ETL and ELT Data Processing Patterns
1. Know the order of operations: The most fundamental exam question will test whether you know the difference. ETL = Extract → Transform → Load. ELT = Extract → Load → Transform. If a question asks where transformation occurs, remember: ETL transforms before loading; ELT transforms after loading.
2. Associate ELT with cloud and big data: If a question mentions cloud-based data lakes, Azure Synapse Analytics, Azure Databricks, or large-scale data processing, the answer is likely ELT. ELT leverages the power of the destination system.
3. Associate ETL with traditional and on-premises scenarios: If a question references on-premises data warehouses, SSIS, or scenarios requiring pre-processing before loading, think ETL.
4. Remember Azure Data Factory supports both: ADF is Microsoft's primary orchestration service for data movement. It supports both ETL (using mapping data flows for transformation) and ELT (loading data and then invoking stored procedures or external compute for transformation). If a question asks which Azure service orchestrates data pipelines, the answer is usually Azure Data Factory.
5. Understand raw data preservation: If a question highlights the need to keep raw data for future reprocessing or auditing, the answer points to ELT, because raw data is loaded first and preserved.
6. Watch for keywords:
- "Staging area" or "intermediate processing" → ETL
- "Data lake" or "load raw data first" → ELT
- "Transform in the destination" → ELT
- "Transform before loading" → ETL
7. Don't confuse ETL/ELT with data streaming: ETL and ELT are typically associated with batch processing. Real-time or near-real-time data ingestion uses streaming services like Azure Stream Analytics or Azure Event Hubs. However, ELT can also apply to streaming scenarios where raw events are loaded and then transformed.
8. Understand the concept of data pipelines: A pipeline is a logical grouping of activities that together perform a data processing task. In Azure Data Factory, pipelines contain activities for copying data, transforming data, and controlling flow. Exam questions may ask you to identify what a pipeline does in the context of ETL or ELT.
9. Know the benefits of each pattern:
- ETL benefits: Data quality control before loading, reduced storage of unnecessary data, compliance with data governance.
- ELT benefits: Faster ingestion, scalability, flexibility, raw data preservation, leveraging powerful cloud compute.
10. Practice scenario-based questions: The DP-900 exam often presents scenarios and asks you to identify the correct pattern or service. Read the scenario carefully, identify whether transformation happens before or after loading, and match it to ETL or ELT accordingly.
Summary
ETL and ELT are two fundamental data processing patterns that every data professional must understand. ETL transforms data before loading it into the destination, making it ideal for traditional, on-premises environments. ELT loads raw data first and transforms it within the destination, making it ideal for modern, cloud-based big data environments. For the DP-900 exam, focus on the order of operations, the location of transformation, the associated Azure services, and the scenarios where each pattern is most appropriate.
