ETL vs. ELT
What are the intricacies of ETL and ELT? Here are their key characteristics, advantages and drawbacks, plus some use cases where you'd use each.
When it comes to orchestrating data pipelines for data warehousing, there are two main methodologies: ETL and ELT. Although similar in name, these techniques handle data differently and are designed to meet specific data processing needs. In this post, we aim to demystify ETL and ELT by discussing their key characteristics, comparing their advantages and drawbacks, and outlining their ideal use cases. This knowledge is imperative for a broad range of professionals working with data, especially for data engineers designing and implementing data pipelines.
What is ETL?
In the 'Extract' phase of ETL, data is collected from heterogeneous systems such as databases, CRM systems, and other business applications. This data, which can be structured or unstructured, is ingested and readied for transformation. The 'Transform' phase involves a series of steps applied to the extracted data with the objective of converting it into a standardized format. This may include cleaning (removing anomalies or redundancies), validation (ensuring data consistency and integrity), and conversion (changing data types or encoded values to a unified format). Finally, in the 'Load' phase, this transformed data is written into the target data warehouse where it's structured optimally for reporting or analytics.
Advantages of ETL
There are several benefits to using ETL for data warehousing:
- Efficiency of data analysis: By standardizing and cleansing data in the transformation stage, prior to its insertion into the data warehouse, analytical queries can be executed more quickly. This is largely due to the reduction in data complexity and the optimized structure of the transformed data.
- Data governance and compliance: ETL provides a controlled environment to handle sensitive data. During the transformation phase, techniques such as masking or encryption, can be applied to sensitive data elements, ensuring compliance with data privacy regulations like GDPR.
- Maturity: ETL has been refined over the years and has a robust array of tools available, ranging from open-source solutions to commercial products. This maturity leads to a reliable, well-documented approach to managing data pipelines.
- Data quality and consistency: The transformation stage of ETL allows for data cleansing and standardization, ensuring that data from disparate sources conforms to the same format and quality standards. This uniformity improves data integrity, making cross-dataset operations and analyses more reliable. Data reliability also plays a huge role in ensuring that your data is consistent and available at all times.
- Performance optimization: By executing data transformations prior to loading data into the data warehouse, the computational burden on the warehouse is reduced. This off-loading of processing tasks improves the overall performance and responsiveness of the data warehouse.
- Source system independence: ETL is designed to handle data from a wide variety of sources, irrespective of the original data format or structure. This capability allows organizations to add or modify data sources without major disruptions to their existing data management processes.
Disadvantages of ETL
Despite its numerous benefits, ETL is not without its challenges:
- Maintenance: The ETL process, with its complex transformation rules and dependencies, requires careful monitoring and troubleshooting. Any changes in the source system schema or data types can potentially lead to pipeline failures.
- Latency and lack of real-time processing: Since ETL involves batch processing, there is a delay between when the data is captured from the source system and when it becomes available in the data warehouse. This makes real-time or near-real-time data analysis difficult, which can be a disadvantage in scenarios where timely insights are critical.
- Scalability: As the volume of data grows, the time and computational resources required for the transform and load phases can increase significantly. Large data sets can result in prolonged ETL cycles, leading to delays in data availability.
- High data storage requirements: During the transformation phase, intermediary results may need to be stored temporarily, which can increase storage costs. Additionally, the need to store historical data in the warehouse for trend analysis can further add to the storage burden. These factors should be considered while architecting an ETL solution, and mitigations such as elastic storage or selective data retention strategies may need to be explored.
What is ELT?
ELT (Extract, Load, Transform) represents an alternative approach to the traditional ETL method in data pipeline management. In the 'Extract' phase, similar to ETL, data is retrieved from multiple heterogeneous systems. However, ELT differs ETL in the order of the next operations. In ELT, the 'Load' phase occurs directly after extraction, where the raw, unprocessed data is loaded into the target warehouse. This transfer happens without any transformation of the data, meaning the data retains its original structure upon loading.
After the data is loaded, during the 'Transform' phase, data is manipulated and restructured as per the specific needs of the analytics tasks. This approach leverages the computational power of modern data warehouses, enabling transformations on large datasets directly within the warehouse itself.
Advantages of ELT
ELT presents several advantages for managing data pipelines:
- Lower maintenance cost: Since the data transformation occurs within the target data warehouse, there are fewer dependencies to manage compared to the ETL process.
- Scalability: ELT also scales well with large data volumes. By leveraging the computational capabilities of modern, highly-scalable data warehouses, it can handle the increased processing demands of big data sets. The architecture allows for distributed processing of data, enabling efficient scaling as data volumes grow.
- Real-time analytics: By loading raw data directly into the target warehouse, ELT can support near real-time or real-time data analysis. This capability allows businesses to gain immediate insights from their data, which can be crucial in dynamic, fast-paced environments.
- Simplified data integration: By loading data in its original format, it preserves the raw, granular data, which provides more flexibility for future transformations. This flexibility can simplify the process of integrating new data sources or altering transformation logic to meet changing business needs.
Disadvantages of ELT
ELT also comes with certain technical challenges:
- Data quality and consistency - Since the data is loaded in its raw state, without any prior cleansing or standardization, inconsistencies or inaccuracies in the source data can directly propagate into the data warehouse, potentially affecting data quality and analytics accuracy. One way to address this is to ensure that you test ELT pipelines using tools such as dbt tests, optimize them as much as possible, and layer on a data observability solution like Bigeye to overcome the limitations of SQL checks like those in dbt tests, or Great Expectations.
- Target system resource consumption - By transferring the transformation workload to the data warehouse, ELT increases the computational load on the warehouse system. This can affect the performance of the warehouse, particularly when dealing with large data volumes or complex transformation operations.
- Data modeling - ELT might lead to more complex and difficult-to-manage data models. Since transformation occurs after loading, the data model needs to accommodate a wider range of raw data structures, leading to increased model complexity.
- Data governance and compliance - Loading sensitive data directly into the warehouse without prior cleansing or anonymization could lead to potential breaches of data privacy regulations. Typically, many employees at a company will have access to the data warehouse. Therefore, careful attention to data governance strategies is required when implementing ELT pipelines.
ETL vs. ELT: A side-by-side comparison
The following table summarizes the key differences between ETL and ELT across various characteristics:
FactorETLELTHigh-level definitionExtract, transform, and load process. Transforms data before loading into the target system.Extract, load, and transform process. Transforms data after loading into the target system.Transformation processTransformations occur before the data is loaded into the data warehouse, usually within an ETL tool.Transformations occur after the data is loaded into the data warehouse, usually leveraging the computational power of the warehouse.Load processLoad occurs after the transformation process. Only clean and structured data is loaded into the warehouse.Load occurs before the transformation process. Raw data is loaded directly into the warehouse.Process speedThe ETL process may be slower due to the time spent in transforming data before loading.The ELT process may be faster as it loads raw data directly into the target warehouse.Analysis speedFaster, as transformed and clean data is readily available in the warehouse.Might be slower initially due to transformation after loading. However, it can support near real-time or real-time analysis.Process maturityMature, with well-defined methodologies and a broad range of tools.Less mature compared to ETL but gaining popularity with the rise of cloud-based data warehouses.ComplianceBetter controlled as sensitive data can be managed during transformation before loading.Can be a challenge since raw data, including sensitive data, is loaded directly into the warehouse.CostsCan be higher due to the need for robust ETL tools and intermediary storage for transformed data.Can be lower as it requires less intermediary storage and leverages the computational power of the data warehouse for transformation.Data outputStructured and clean data is loaded into the warehouse.Both raw and transformed data can be available in the warehouse, offering more flexibility.Data volumeCan handle large volumes, but might experience performance issues with very large datasets.Better equipped to handle larger volumes due to leveraging the power of the target data warehouse for transformation.Data warehouse supportWidely supported by traditional and modern data warehouses.Primarily supported by modern, cloud-based data warehouses due to their high computational capabilities.Data lake supportETL can be used with data lakes, but might not fully leverage the flexibility and schema-on-read nature of data lakes.ELT is more suitable for data lakes as it allows for storing raw data and performing transformations as per analytical requirements
Use cases for ETL
If your dataset contains sensitive information, ETL offers an opportunity to handle this data appropriately during the transformation phase, ensuring compliance with privacy regulations. It allows for the anonymization or removal of sensitive data elements before loading them into the target warehouse, significantly reducing the risk of data breaches.
Diverse/legacy data sources
ETL is often used when your source data is in a bunch of different formats and needs to be standardized in order to be loadable into the data warehouse at all.
This is especially true when you have to integrate data from legacy systems. Many businesses still operate older systems that store data in outdated or proprietary formats. The ETL process allows for extraction from these diverse systems and transforms the data into a modern, standardized format that can be used with contemporary analytics tools.
Analyses that require historical data
Another use case is when there is a requirement for historical data storage and analysis. ETL processes can consolidate data from multiple systems and time periods, creating a historical record in the data warehouse. This enables trend analysis and other retrospective examinations of data, providing insights that support strategic decision-making.
Complex business logic
ETL is beneficial when dealing with complex business rules that need to be implemented in the data transformation stage. The transformation phase in ETL is not only about cleaning and standardizing data but also about applying business rules to create derived metrics. For instance, calculating aggregate metrics like total sales, or applying certain categorizations based on business logic. This makes ETL a valuable tool for transforming raw data into actionable business information.
Use cases for ELT
One prominent use case is when dealing with massive quantities of data. With the increase in big data, businesses often grapple with petabytes of data from various sources. ELT leverages the robust and scalable computational power of modern data warehouses to process and transform this data after loading, making it better equipped to handle the sheer volume of big data scenarios.
In addition, ELT is the preferred choice when real-time or near-real-time analytics is a requirement. Given that the raw data is loaded into the warehouse first and transformed as needed, ELT allows for more agile data exploration and analysis. Users can work with data as it arrives, enabling real-time insights.
Furthermore, ELT shines when there is a need for flexible data manipulation. As both raw and transformed data are available in the warehouse, analysts can decide how and when to transform the data based on evolving business needs. This allows organizations to respond more swiftly to changing business scenarios.
Which one is right for you
ETL may be preferable when:
- The raw source data is difficult to reproduce so you need to archive it. Transforming first allows you to reprocess historic data if needed.
- There is lots of irrelevant data that can be filtered out early to reduce storage and throughput needs.
- Certain transformations are significantly faster to run on extract vs. on the full loaded dataset.
ELT may be better when:
- The source data is ephemeral and needs to be loaded quickly before it disappears.
- Transforming the raw data is computationally infeasible. ELT allows you to load it first and transform incrementally.
- Requirements are likely to change so you want the flexibility to retransform in-place later.
- Loading the data into the warehouse provides access for other systems even before transformation.
Remember, the decision isn't necessarily a binary one. In some cases, a hybrid approach, doing some transformation before loading and some after loading, may be the most effective strategy to meet diverse data processing needs. The status quo for many companies is not ETL or ELT, but ETLT.
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