Top ELT and ETL Tools for Data Integration and Migration
If your company is like most, you’re employing some version of an extract, load, and transform (ELT) or extract, transform, and load (ETL) process to migrate business data to a central data warehouse, most likely in the cloud, and prepare it for analytics. But if you’ve been relying on a legacy data integration solution, most likely one that relies on ETL, for too long and you’re ready for a more modern platform, you may have a few questions. What’s the difference between a tool designed for ELT and one designed for ETL? Is it important to use a solution built for the cloud? What are the top ELT and ETL tools right now?
What are ELT and ETL tools? What are they used for?
ELT and ETL tools both perform core data migration and data integration tasks. The key difference between the two? Where data transformation occurs in the process.
ETL platforms extract data from various sources and transform it before loading it to a destination; ELT solutions extract and load data to the destination before transformation. Companies are increasingly more likely to use ELT tools when consolidating data in cloud-based data warehouse environments in order to take advantage of the scalable processing capabilities of the cloud.
Types of ELT and ETL tools
Some organizations do not use a data integration solution and instead rely on handing coding to migrate and transform data. There are also some solutions out there that aid with hand coding to help simplify and manage this process. Even with relying on such tools, hand coding adds a great deal of complexity to standard data management tasks and processes. In a hand coded ETL workflow, each individual data source requires pages of code, and data gathering requires multiple scripts, which are difficult to keep updated. Over the long term, hand coded solutions become more and more costly to run and maintain.
Designed originally for on-premise storage environments, legacy ETL platforms perform standard extract, transform, and load processes. While an organization’s legacy system may still be capable of performing these basic functions, they tend to be far slower and less flexible than modern alternatives.The also present organizations with data security and sovereignty concerns as the data has to leave an organizations secure corporate environment to be transformed before being loaded into a data warehouse, on-premises or more likely in the cloud.
Data pipeline only
Some organizations rely on simple data pipeline tools to move their data to the cloud. Like ETL platforms, these solutions extract data from sources and load it to a destination; however, they don’t perform data transformation as part of their standard workflow. Organizations who use data pipeline only tools usually perform the transformation process by hand coding or using an additional tool. While typically easy to use on their own, many data pipeline tools lack the flexibility offered by more complete modern ELT solutions.
Built for the cloud, modern ELT tools perform a complete extract, load, and transform process without any add-ons required. These platforms are designed to extract large volumes of data from a multitude of data sources for loading and transformation in cloud data environments.
How to assess ELT and ETL tools
When choosing a platform for your organization, consider:
- Does the tool offer the pre-built integrations and connectors all your data sources require? Is it easy to create custom connectors?
- Is the tool easy for your data team to use?
- Is the tool designed to scale as your data volumes increase?
- Is the pricing structure - whether it’s based on volume of replicated data, the number of data sources, or the number of authorized users - a good fit for your needs?
- Does the tool offer the data encryption capabilities your company and industry security regulations require?
A comparison of ELT and ETL tools and technologies
Tools for Hand Coding
Legacy ETL Tools
Data Pipeline Tools
Modern ELT Tools
|dbt; Spark; Dataform; Airflow||IBM DataStage; Informatica PowerCenter; SAP BusinessObjects; Talend||Fivetran; Stitch; Airbyte; Rivery.io; Hevo Data; Matillion Data Loader||Matillion ETL; Azure Data Factory; Talend Cloud; Informatica Data Management Cloud (IDMC)|
|Tools that assist with manual coding for data pipelines (e.g., Airflow and Spark for extraction and loading; dbt and Dataform to automate SQL code for data transformation and modelingmodelling)||Platforms that started out as traditional ETL; focused initially on on-premise warehouses and data lakes; have been adapting slowly to cloud technology||Tools that extract data from common SaaS sources and databases and load it into a destination||Platforms that are built for the cloud; perform complete ETL/ELT workflows without add-ons required; can extract and load from a multitude of data sources into cloud data environments|
|Data engineers, data developers, database architects||Data engineers, database architects, data developers||Data engineers, data analysts, marketing analysts||Data analysts, marketing analysts, data engineers, data scientists|
|Startup analytics; one-off data pulls; low-budget data modelling||On-premise data environments; Master Data Management (MDM)||Business intelligence; marketing analytics||Business and data analytics for digitally transformed enterprises and cloud native companies; startup growth analysis|
|Flexible; affordable; developer-friendly||Large, well-established vendors; features beyond scope of ETL; broad awareness; certified professionals available for support||Performant; easy to use||Scalable; quick processes; easy to deploy and use.|
|Manual, time-consuming processes; constant maintenance; not scalable; lack continuity in growth; requires time and skill from expensive data engineers||Cost and pricing model; not originally built for the cloud; least flexible; more complex than cloud native tools; data security concerns when working with cloud platforms||Niche; lacks flexibility; not developer friendly; data security concerns with SaaS products||Limited customization; less friendly to developers who want to write code|
Choosing ELT vs. ETL
When you use a modern ELT solution (as opposed to an ETL platform), you load your data in its raw form into a target destination, leveraging the power of your chosen data warehousing platform to perform transformations. And by pushing these processes to a cloud data warehouse, you have a high-performance, massively scalable environment for easier, faster data transformation. You save on infrastructure, accelerate workflows, and get your data analytics-ready more quickly.
Plus, with an ELT process, you don’t need to know exactly how your organization will use the data you’re currently processing. You have the freedom and flexibility to transform the data you’ve migrated at a later stage when specific use cases present themselves.
Matillion is an industry leader in ELT
Don’t be fooled by the name. “ETL” is more widely used to describe the data extraction, transformation, and loading process, and our cornerstone platform is referred to as Matillion ETL. But our cloud native solution is actually based on an ELT process, loading source data directly into the cloud data environment of your choice - Amazon Redshift, Microsoft Azure Synapse, Delta Lake on Databricks, Google BigQuery, or Snowflake - to harness the power of the cloud in preparing your data for analytics.
Want to find out more about Matillion ETL? Sign up for a free personalized demo now.
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