ETL vs ELT: What’s the Difference and Do You Need an iPaaS?
With how important data is to businesses today, there is a crucial decision to make in how that data is migrated and stored across platforms. There are two common methods: ETL (Extract, Transform, and Load) and ELT (Extract, Load, and Transform). Their suitability depends on your business and data integration needs, as both are viable options. There’s also a third option: an integration platform as a service (iPaaS) which brings automation and scalability into the data transformation process. The question is which one is suited best for you?
Why do you need a data transformation process?
Data comes from multiple sources and is often stored in either structured SQL databases or unstructured NoSQL databases. To make data useful to the business, data engineers and data scientists often standardize the data so that it can be used in a compatible format with the target data system. Data comes from any number of sources. (Think of all the SaaS applications your business uses, plus all the other sources of data that are generated every minute by the business and its consumers.) These sources need to go through a process to make this data consistent, trustworthy, and useful.
Data transformation is necessary to reconcile and standardize data so business intelligence platforms, like Tableau, can work from one set of “data truth”, and your team can use clean consistent data.
Data pipelines control where data flows. However, where and how it gets transformed differs depending on the process used.
What is ETL?
Dating back 20 years, ETL (Extract, Transform, and Load) is a tried-and-true method of data migration and transformation. It begins with the selection of data from one or more data sources followed by the transformation of the data within a staging environment. Finally, the extracted and transformed data in the staging environment gets loaded into a data warehouse for storage and analysis. The core operations of a traditional ETL process are:
- Extract: Source data is pulled from an original data source and then placed in a temporary staging area.
- Transform: Within the staging area, data is standardized, cleansed, enriched and transformed so that it can integrate with a target system. This is also where sensitive data is scrubbed, encrypted, redacted, or protected before it is exposed to business users..
- Load: Finally, the data is deposited into a data storage system, such as a data warehouse. From here, data scientists, analysts, and line of business teams can use it to support business intelligence or other core operations.
ETL can be on-premises or cloud-based. However, most traditional ETL tools are geared toward on-premises data.
What is ELT?
Contrary to its predecessor, in the ELT process, loading data into storage comes before the transformation. ELT uses cloud data warehouses to load all types of data without having to make complex transformations first. This is possible through data lakes, a special type of storage where any raw information can be uploaded. From here, data can be selected and transformed as needed. Data loading becomes extremely fast and the business captures all data — whether it will eventually use that data or not. ELT is cloud-based and does not work with on-premises systems.
ELT’s speed and scalability is made possible by modern cloud server technology. By utilizing cloud-based data warehouses, businesses gain exponential storage capabilities and can drastically scale their processing power. Platforms like Amazon Redshift and Snowflake have immense processing abilities and make ELT pipelines possible.
What’s the difference?
The difference between choosing ETL or ELT technology comes down to a couple of different factors. For example, when do you want transformation to take place? What is your tolerance for the potential exposure of sensitive data? Finally, are you being selective about the data you store or are you willing to capture and store all your data, even if you never use it?
ETL must transform data before loading it into a structured relational data warehouse system. This allows for more organized, focused work at the cost of being more time-consuming. ELT, on the other hand, throws all data types (raw data, structured data, semi-structured data, and unstructured data) into a data lake, where analysts can then pick and choose what to transform. While this is the simpler approach, the bloated storage requires a business that can keep up with the high costs and complexity of storing large amounts of data. Unlike ETL, in which sensitive data can be removed or masked during the transformation process, ELT must upload all data first, leaving sensitive information vulnerable for a time, and possibly exposing your organization to violations of GDPR, HIPAA, CCPA, or other regulatory standards.
Benefits of ETL
- Greater flexibility – ETL allows for more complex transformations and broader toolsets while minimizing data silos and lock-ins for your transformed data.
- Easier compliance – Sensitive data sets can be redacted or encrypted, making it easier to satisfy GDPR, HIPAA, CCPA, and other compliance standards. When you transform data before it gets to the data warehouse, you greatly reduce the risk of exposing non-compliant data.
- Wider availability – ETL has been around for 20 years; there are numerous ETL tools in the Data Integration space and ETL knowledge is a common skill set among developers.
- Higher volumes – An ETL approach is the better option for handling large volumes of data, both structured and unstructured because it reduces the high compute costs associated with cloud data warehouses.
Benefits of ELT
- Faster availability – ELT provides faster data availability and data loading for quicker analytics.
- Stronger focus – ELT is ideal for smaller datasets with simple transformations that have minimal impact on cloud data warehouse processing utilization.
- Lower costs – ELT is cloud-based and does not require extensive, costly hardware.
- Lower maintenance – ELT generally incorporates automated solutions, so very little maintenance is needed.
ETL and ELT Use Cases
ETL and ELT are both viable solutions for data movement and transformation. Deciding which is the right solution for your business may depend on factors such as using an OLAP data warehouse or a cloud-based data lake. Here are some use cases:
ETL may be best if you:
- Want to capture all structured and unstructured data, regardless of data size, within your business
- Have the resources to manage data lakes
- Need to tap into a readily available pool of ETL toolsets and experts
- Must reduce any risk of exposing non-compliant data
- Use an OLAP relational database or SQL data warehousing
- Do not need to pass unstructured data into a target data system
- Are willing to wait for longer load times
ELT may be best if you:
- Have smaller datasets that do not need complex transformations
- Have the resources to employ ELT experts
- Are not a highly regulated business and do not have to comply with GDPR or other regulatory standards
- Have a powerful, cloud-based target data system to process incoming data volumes
- Prioritize fast data loading
What about an iPaaS?
An iPaaS (integration platform as a service), like SnapLogic, is a cloud-based platform that simplifies and automates data integration and application integration. An iPaaS uses API endpoints that allow different applications within your organization to share information across a common communication channel. This is the basis of Enterprise Automation and enables end users to work from the same data set and create better workflows. An iPaaS moves data across systems in real-time and works with cloud, on-premises, and hybrid systems. Some iPaaS providers, such as SnapLogic, also offer an ELT solution.
The SnapLogic ELT is a low code/no-code platform that gives you the flexibility to leverage the power of a cloud data warehouse to transform your data. SnapLogic ELT uses a visual programming language to load data quickly and transform it in place while leveraging the cloud data warehouse’s compute power to prepare structured data for analysis.
With more than 600+ pre-built Snaps, SnapLogic powers data ingestion from application and data endpoints while ELT capabilities within the platform manage transformation through cloud data-warehouses such as Snowflake, Redshift, Azure Synapse, Google BigQuery, and Databricks Delta Lake. A true differentiator in SnapLogic is the ability to extract summarized data from a cloud data warehouse to any application or analytics endpoint to gain greater insights into things like load efficiency, compute consumption, data volume trends and many others.
Whether you choose ETL, ELT or the combined power of an iPaaS with its ELT capabilities, the ability to trust your data and readily access it is critical to the success of your business. SnapLogic delivers all of these data movement and transformation methods in a proven and powerful, cloud-based platform that addresses the unique needs and goals of your organization. Try SnapLogic today!