Given that a data warehousing environment includes data from disparate sources, many users deploy some varation of extract, transform, load (ETL) -- often automated and scheduled -- to process heterogeneous data and unify it for analysis. Having the right tools for the task at hand is important to ensuring a seamless flow of data from pirmary sources to end-user analysts or data scientists. Extract, transform, load is a primary component of data integration, along with data preparation, data migration and management, and data warehouse automation.
ETL tools collect, read and migrate data from multiple data sources or structures and can identify updates or changes to data streams to avoid constant whole data set refreshes.Operationally, the tools can filter, join, merge, reformat, aggregate and for some, integrate with BI applications. ELT (Extract, Load, Transform) is a more recent variant that acknowledges the transformation part of the process is not always required before loading,
What to look for in an ETL tool
- Easy to use, maintain, and highly secure
- Connects to all required data sources to fetch all relevant data
- Works seamlessy with other components of your data platform, including data warehouses and data lakes (via ELT)
Snowflake and ETL Tools
Snowflake supports both transformation during (ETL) or after loading (ELT).
Snowflake works with a wide range of data integration tools, including Informatica, Talend, Tableau, Matillion and others.
In data engineering, new tools and self-service pipelines are eliminating traditional tasks such as manual ETL coding and data cleaning companies. With easy ETL or ELT options via Snowflake, data engineers can instead spend more time working on critical data strategy and pipeline optimization projects.
In addition, with a Snowflake as your data lake and data warehouse, ETL can be effectively eliminated, as no pre-transformations or pre-schemas are needed.