An ETL pipeline is the set of processes used to move data from a source or multiple sources into a database such as a data warehouse. ETL stands for “extract, transform, load,” the three interdependent processes of data integration used to pull data from one database and move it to another. Once loaded, data can be used for reporting, analysis, and deriving actionable business insights.
Benefits of ETL Pipeline
The purpose of an ETL pipeline is to prepare data for analytics and business intelligence. To provide valuable insights, source data from various systems (CRMs, social media platforms, Web reporting, etc.) needs to be moved and consolidated and altered to fit with the parameters and functions of the destination database. An ETL pipeline is helpful for:
- Centralizing and standardizing data, making it readily available to analysts and decision-makers
- Freeing up developers from technical implementation tasks for data movement and maintenance, allowing them to focus on more purposeful work.
- Data migration from legacy systems to a data warehouse
- Deeper analytics after exhausting the insights provided by basic transformation
Characteristics of an ETL Pipeline
The enterprise shift to cloud-built software services combined with improved ETL pipelines offers organizations the potential to simplify their data processing. Companies that currently rely on batch processing can now implement continuous processing methodologies without disrupting their current processes. Instead of costly rip-and-replace, the implementation can be incremental and evolutionary, starting with certain types of data or areas of the business.
Ultimately, ETL pipelines enable businesses to gain competitive advantage by empowering decision-makers. To do this effectively, ETL pipelines should:
- Provide continuous data processing
- Be elastic and agile
- Use isolated, independent processing resources
- Increase data access
- Be easy to set up and maintain
A data pipeline refers to the entire set of processes applied to data as it moves from one system to another. As the term “ETL pipeline” refers to the processes of extraction, transforming, and loading of data into a database such as a data warehouse, ETL pipelines qualify as a type of data pipeline. But “data pipeline” is a more general term, and a data pipeline does not necessarily involve data transformation or even loading into a destination database—the loading process in a data pipeline could activate another process or workflow, for instance.
New tools and self-service pipelines eliminate traditional tasks such as manual ETL coding and data cleaning.
Snowpark is a developer framework for Snowflake that brings data processing and pipelines written in Python, Java, and Scala to Snowflake's elastic processing engine. Snowpark allows data engineers, data scientists, and data developers to execute pipelines feeding ML models and applications faster and more securely in a single platform using their language of choice.
With easy ETL or ELT options via Snowflake, data engineers can instead spend more time working on critical data strategy and pipeline optimization projects without worrying about data transformation and data ingestion. And with the Snowflake Data Cloud as as your data lake and data warehouse, ETL can be effectively eliminated, as no pre-transformations or pre-schemas are needed.