By Ocient Staff
The terms ETL and ELT seem virtually interchangeable, as though one is just the other mistyped. And while it’s true that both acronyms stand for the same three words, they are fundamentally different. But before we dive too deep into ETL (Extract, Transform, and Load) and ELT (Extract, Load, and Transform) data pipelines, we believe it’s important to identify the differences and similarities between ETL and ELT.
ETL and ELT Similarities
First, both pipelines, ETL and ELT, are established with a series of processes that prepare data for proper analysis by extracting, transforming, and loading data. They both enable additional processing of the data to deliver actionable insights for business decision making. Additionally, both ETL and ELT data pipelines allow organizations to merge data from a variety of data sources into a single repository. The single, unified data repository creates simplified access for data analysis and an environment for additional processing.
Now that we know how they are the same, let’s dive into their differences: their process flows.
ETL Data Pipelines
ETL data pipelines are a combination of the three data engineering processes, with the end goal of moving data from single or multiple data sources into a unified data repository, like a database or data warehouse.The question remains, how exactly is this done? As mentioned, there are three steps in the ETL process: extraction, transformation, and loading. Let’s learn a bit more about each.
Extraction is the first element of ETL. It occurs when raw data is pulled from a single source or multiple sources. This data may be extracted from transactional applications and data systems, such as mainframe servers, network switches, or object stores like S3. Extracted data may be in several formats, including CSV, JSON, and XML.
The next step of ETL is transformation. In this step, data is updated to align with organizational requirements and data storage solution needs. Transformation may involve converting all of the data types into their final format. This might mean merging string columns in a CSV source together, reducing precision on a floating point number from a JSON document, or verifying and cleaning up inconsistent or inaccurate data. Additionally, within the transformation process, data elements from several models can be combined and pulled from the other selected sources and processes. Throughout the entire transformation process, specific logic is implemented to prevent the inclusion of duplicative data.
The final stage of the ETL data pipeline is loading. During this process, data is delivered post-transformation and secured for sharing. This enables the business to deliver and analyze the data in its optimized format. At this point, authorized individuals can generate reports and queries to allow them to make data-informed decisions
ELT Data Pipelines
ELT data pipelines overall encompass the same elements of ETL; however, the process sequence varies. ELT begins with the extraction process, but then the data is natively loaded into its final form before the final transformation. This varied process allows organizations to preload raw data into a place where it can be modified.
Comparing ETL vs. ELT
Going back to the question of ETL vs. ELT, if both yield the same result, but just take a varying approach to get there, you may be wondering which is better. A simple modification in the process flow may seem like a minute change, but each approach possesses both benefits and potential liabilities.
By using ELT and loading data into its repository before transforming it, users have identified several advantages over the more-common ETL method. ELT data pipelines have been proven to offer increased flexibility, as their data warehouses enable multi-organization tenancy. Furthermore, the data loaded in an ETL pipeline is generally in its final form for analysis by the end user.
With both approaches, the larger and more complex the datasets, the higher the demand for computing resources. Whether outside or inside of the database, most systems are just not up to the task of performing transformations on petabytes of data in a usable time frame.
Ocient’s ELT and ETL Services
Whether you prefer to transform your data before loading or after, Ocient’s hyperscale data analytics solutions are suitable for both ETL and ELT data pipelines. Ocient provides use-case-driven solutions on a high-performance data warehouse platform that offers superior storage with a smaller digital footprint.
For workloads that require continuous streaming, transformation, and analytics, cloud data warehouses can be cost prohibitive at hyperscale. That’s why the Ocient Hyperscale Data Warehouse is optimized for the high-performance loading of hyperscale datasets without any hidden costs or extra tooling required. Its Zero Copy Reliability™ reduces database storage and drastically reduces the time required for complex transformations.