How can you choose between ETL and ELT for data integration?

ETL can access sources through either bulk, record level or streaming APIs, push down predicates for the sources that support it, and perform transformations in the flow. Say you need to load North American customers from some operational db to the data warehouse and concatenate first, middle and last names into a full name. ELT would extract all the customer records, load them into the data warehouse, then read them out again, filtering out NA customers and concatenating names and finally load into the destination table. A good ETL tool would only extract NA customers and perform concatenation in the ETL engine as the data is flowing through and load final results into the destination table saving about 8x compute and 4x network traffic.

ETL is also used when we are loading data into a Data Model which case it becomes necessary that we perform basic checks like deduplication, data consistency and data standardization.

ELT stands for Extract, Load, Transform. It is an approach to data integration and processing that differs from the traditional ETL (Extract, Transform, Load) method. In ELT, the data is extracted from the source systems, loaded into a target system or data warehouse, and transformed or processed within the target system.

ELT is particularly useful when you need to join very large datasets from different sources. Distributed join strategies are tricky and not well supported by many ETL tools, while loading all the data into the target database and then joining is far more efficient. Another advantage of ELT is that it is using the same compute power as the target database, usually during the off hours and does not require additional servers. Finally, since it uses the SQL of the target database, developers do not need to learn yet another language to specify their transformations and can often be home grown avoiding additional software licensing fees.

ELT is newer compared to ETL and started getting used more widely after Data Lakes became popular. In this scenario, you don't worry about consistency, quality but focus only on data integration and let the consumers worry about quality.

ETL stands for Extract, Transform, and Load. It refers to a process used in data integration and warehousing to extract data from various sources, transform it into a consistent format, and load it into a target system such as a data warehouse or a data lake.

ETL offers cleaner data and better support for validation, but can be slow and resource-intensive. ELT provides scalability, faster data availability, and leverages distributed systems like Spark, but shifts data quality responsibility to consumers and may require duplicate transformation logic. Choosing between ETL and ELT depends on factors like data profiles and use cases, with ETL emphasizing governance and ELT focusing on scalability and agility.

The main differences have to do with efficiency of handling different data patterns. ETL can do record level API calls to sources and perform transformations as data is flowing through, while ELT can handle joins of very large datasets that come from different sources. Please see my notes on ETL and ELT strengths above for more details.

One of the primary benefits of ETL is that it ensures the data loaded during the process is clean and consistent, resulting in fewer quality issues. However, there is a drawback where significant time may be spent cleaning data that ultimately remains unused. Conversely, ELT shifts the responsibility of checks and transformations to consumers, guaranteeing that attributes genuinely needed are processed. Yet, a potential downside arises from redundant efforts, as each user must apply the same transformations independently before utilizing the data.

The purpose of integration of this kind, if aimed at providing enterprise-wide data, is to semantically aligned and harmonized the data. If ETL and ELT are simply a timing thing yielding at the end an enterprise repository, then fill your boots. ElT does mean that the lineage of the data must be maintained along with its data structures and values. Transformation is to structure (ontology) and to values and processes (taxonomy). However you get there, a semantically aligned enterprise-wide data set built on the business structures rather than many different software system designs, is paramount. Without this forget about enterprise reporting, ML, AI, or anything else.

I see both ETL and ELT as serving the same goals and achieving more or less the same results. For most use cases, either ETL or ELT will work just fine. ETL tools can usually pass through unstructured data just fine and ELT tools can work with well structured data. ETL can be much more performant since it transforms data as it streams it, usually completing the jobs much faster, while ELT can handle massive joins that can stump an ETL system. However, most ETL developers would just stage the source datasets in the target and run another job to join them and if they support push down of queries to the source, this would perform the same steps exactly that an ELT system would do, while providing all the other advantages of ETL.

Data Volume and Complexity: ETL is typically well-suited for handling complex and heavy data transformations. If you have large volumes of data that require significant processing and manipulation before loading into the target system, ETL allows you to perform these transformations during the ETL process. Conversely, ELT is more suitable when you have simpler transformations and relatively smaller data volumes, as the transformations are performed after loading the data into the target system.

ETL has higher upfront costs but provides cleaner data with standardized schema. ELT has lower initial costs, but may require redundant transformation logic across consumers. ELT can handle bigger data volumes and unstructured data well.

The most important thing is to understand your data patterns and structure a test that measures the capabilities and performance of your typical and most complex jobs. The second most important thing is to understand error handling. The recovery from data integration errors is tricky and sometimes requires manual fixes of the production data. Some ETL and ELT tools provide built-in data quality capabilities that help detect and sometimes prevent the problems by loading problematic data into separate exception logs that can be fixed and reapplied. Other must-have capabilities include: lineage capture and change management (versioning, change detection, remediation, and coordinated release of new incompatible versions of data and jobs).

?? ETL for medium, structured datasets needing transformation. ?? ELT for massive, unstructured data where flexibility and scalability are critical.

Another element I would consider is how the architecture is integrated: If you have a solid performance function in the extract tools, you would prefer a classic ETL approach. On the other hand, if you have only what you need to extract information (e.g. connection and low bandwidth) but a high-performance repository for raw data (e.g. a DataLake house or a separate instance for your structured data), you would probably prefer an ELT approach.

In my experience, I still see a lot of ETL happening-whether its because that's how we have all been wired for a long time, existence of skills in specialized software for ETL etc. However, with the growing adoption of cloud data warehouses and target databases with increasing compute capacity, ELT is definitely becoming more of a choice. At the end of the day, it really comes down to use case. If you need combine data from different sources, harmonize and normalize before you load your targets, then ETL is a better choice. On the other hand ELT allows you to load data faster to your target database and do the transformations after.

I worked with a retailer that initially used ETL to extract, transform, and load sales data into Redshift for analysis. As data volumes increased, ETL became slower and more expensive. By migrating them to ELT, loading raw data directly into S3 and leveraging Spark for transformations, we improved performance by 5x and reduced costs by 70%. The key takeaway is to carefully consider where transformations should be performed - either in the ETL tool or natively in the destination system. As data scales, ELT can offer efficiency gains by utilizing the processing power of cloud data / transformation system such as Spark. However, a hybrid ETL/ELT approach is often the optimal choice, depending on data usage patterns.

Scalability: Consider the scalability requirements of your data integration solution. ETL processes can be resource-intensive and require substantial computing power to handle large-scale data transformations. Conversely, ELT leverages the processing power of the target system, which can be more scalable as it utilizes the infrastructure already in place.