ETL vs ELT: 12 Important Differences Explained
ETL Vs ELT – Both these terms seem so similar yet they cannot be more different. If your organization has a data warehouse, then you are likely to use or have used one or both of the following strategies
to move data from various data sources into a data warehouse. ETL and ELT are standard methods of transferring and combining data from different sources, reliably store the data in one location, and allow your employees from various departments to access the data. The distinction between the two terms is related to the order in which specific processes occur. Each strategy fits different circumstances well.
What is ETL?
ETL is a process involving extraction (E) of data from disparate sources, transformation (T) of data in a staging server by performing actions such as modifying the data types or application of specific calculations, and then loading (T) data into the target location – usually a data warehouse.
ETL processes are typically associated with relational databases. Any data, whether structured or unstructured, should be transformed into a relational form before loading into a data warehouse. A staging server that usually runs independently of the data warehouse executes the transformation logic which may include data filtering, sensitive data masking, data enrichment, data mapping, data de-duplication, and combining data from multiple sources.
Because transformations are happening before data is loaded, the data structures required to load the data must be pre-defined. Careful planning and preparation must go into this exercise to determine the right data structures to support business requirements. Any lapses in this phase may result in rework on the entire data pipeline leading to delays and cost escalations.
ETL has been the dominant strategy for data movement for many decades. However, that approach is being challenged now with the rise of cloud data warehouses. Cloud data warehouses make it less expensive and easy to store and process data. This change had led to the application of new ETL techniques to build the data warehouse where experimentation and rapid iteration have replaced careful planning and execution.
Some applications of ETL strategy:
- When the source and target databases are different and use different data types
- When volumes of data to be processed are relatively small
- When transformations require extensive compute resources
- When the data to be processed is structured
To learn more about what ETL is, click here.
What is ELT?
ELT is a process involving extraction of the data, loading the data into the target warehouse, and later transforming the data once the data gets loaded to the destination. Transformations get applied in the data warehouse without the necessity of having a separate staging server.
ELT is a reasonably new concept made possible by advanced cloud data warehouse technologies. Cloud-based data warehouses provide unlimited (nearly) storage and flexible computing capacity. Platforms like Snowflake, Amazon Redshift, and Google BigQuery have ushered in an era of ELT pipelines made possible due to their impressive processing capabilities.
ELT techniques allow for the application of transformations at run time and that too only the data needed for analysis. Although runtime data transformations can slow down the queries, they make up for the slowness by offering more flexibility to the analysts because you can convert data on the fly in various ways to generate multiple types of measurements, predictions, and reports.
Conversely, for ETL, the entire ETL pipeline and data structure in the data warehouse may require modification if a new form of analysis is not allowed by the previously agreed upon data structures.
Some applications of ELT strategy:
- When the source and target database are of the same type
- When there are large volumes of data
- When the target database engine is capable of handling large volumes of data
- When the data is unstructured
To learn more about what ELT is, click here.
ETL vs ELT: 12 Major Differences
|Availability of experts||ETL technology has been around for many decades and resources are readily available||ELT is a new paradigm of doing things. Depending on the use case, finding an expert to build your ELT pipeline may vary from easy to very difficult.|
|Handling of raw data||Raw data gets transformed before loading into the data warehouse||Raw data gets loaded as is to the data warehouse|
|Handling of transformations||Transformations are handled in a staging server before data is loaded into the data warehouse||Transformations can be applied on the fly using SQL viewsTransformations can also be applied using MapReduce and other technologies|
|Support for data lakes||ETL doesn’t lend itself well to data lake use cases||In the ELT approach, data is typically loaded to object storage which acts as the data lake. This data is processed downstream based on the use case to be tackled.|
|Compliance||In an ideal world, all sensitive data should be redacted before leaving the production environment. When that happens, both ETL and ELT strategies end up with the same security posture. However, in most cases, source systems may not have native data masking and redaction capabilities. In such cases, having a transformation engine with limited access where the sensitive data is being handled offers greater compliance and unauthorized access.||In cases where source data cannot be masked, ELT approaches do pose challenges downstream as sensitive data is replicated to the destination only to be masked and transformed later. Stringent security measures are necessary to ensure compliance requirements are met, and data is not compromised.|
|Data size considerations||ETL is generally suited for data sizes varying from a few GB to a few TB||ELT is often a better architecture pattern for large data sets|
|Supported Data warehouses||ETL has been around a long time. ETL techniques can be applied to both legacy on-premise data warehouses as well as modern cloud data warehouses.||ELT is a new paradigm that works with cloud-based data warehouses like Snowflake, Bigquery, Redshift and others. Although Oracle’s Data Integrator product supported ELTs for many years now, the adoption of this strategy has blossomed after cloud technologies came to the forefront.|
|Hardware requirements||Most ETL solutions today are cloud-based. However, they generally require installation of software in a cloud VM.||Modern ETL tools are cloud-based and require no additional hardware.|
|Maintenance||Cloud-based ETL or data pipeline solutions, like Daton, require zero maintenance. Legacy ETL tools that run on-premise do require some upkeep||Modern ETL tools are cloud based and require no additional hardware.|
|Staging area||A staging area is required for an ETL process to function||No staging area is needed for an ELT process to function|
|Costs||Cloud-based ETL or data pipeline solutions, like Daton, start at $20. Usage-based pricing is a standard strategy for these tools.||Cloud-based ELT or data pipeline solutions, like Daton, start at $20. Usage based pricing is a standard strategy for these tools.|
|Unstructured data support||ETL processes don’t provide an elegant solution to handle unstructured data||ELT is the perfect strategy to handle unstructured data. Downstream transformation routines can support various use cases based on the raw data in the data lake|
|Data processing time||ETL processes take longer time to run before data is loaded in the warehouse.||Since the transformation happens after the loading processes are complete, data is available in the warehouse sooner.|
How can Daton help?
Daton’s automated ETL platform provides a cloud-based, visual, and no-code interface making data replication and transformation a breeze. Daton’s pre-built connectors eliminate the burden of data engineering and simplify the life of analysts and developers alike. By making data available in the data warehouse in just a few clicks, Daton empowers analysts to focus their efforts on finding insights and contributing to business growth rather than on building the data pipeline.