What Is an ETL Pipeline?

One of the most common ways for transferring data to analytical environments is by ETL pipelines. Let's break down the term ETL pipeline:

• ETL stands for extract, transform, and load.

• A pipeline broadly applies to any set of processes connected sequentially. Sequentially is passing one process’s output as the input of another process in the chain.

An ETL Pipeline is a collection of software processes that fills data warehouses and other data repositories with data using the steps extract, transform, and load. It’s an automated and scheduled pipeline for converting raw data into analysis-ready data and loading it to an OLAP system—all according to specific business needs and context.

OLAP vs. OLTP

OLAP stands for “online analytical processing system.” Any system or data repository optimized for handling complex queries and conducting data analytics is considered an OLAP system. A data warehouse is an example of an OLAP system.

Data warehouses are denormalized databases that store past data instead of current data and are typically column-based. Examples of data warehouses are Google Cloud's BigQuery, Snowflake, and Amazon Redshift. An alternative to an OLAP system is an OLTP system, which stands for “online transactional processing system.”

OLTP systems are optimized for handling high volumes of simple CRUD queries (create, read, update, and delete) and keeping the data consistent. This is why they’re suitable for storing daily transactions and other financial-related data. An example of an OLTP system is a company’s production database.

OLTP databases are usually normalized, store current data, and are typically row based. MySQL, Oracle, SQL Server, and Google Cloud SQL are popular examples.

ETL pipelines are mostly used to transfer data to OLAP systems for analytics and reporting. However, they can also be used for OLTP systems for high-volume processing or to synchronize data between OLTP and OLAP systems.

ETL pipeline vs. data pipeline

ETL is sometimes used interchangeably with data pipeline. Simply put, an ETL pipeline is a widespread type of data pipeline that usually transfers batches/streams of data to analytical or OLAP systems and environments using the paradigm mentioned above, i.e., extract, transform, and load.

Data pipelines are a generic term for tools and processes that automate data transfer between a source system and a target repository.

Tools for ETL pipelines

An ETL pipeline usually involves multiple tools, technologies, or languages working in sync to get the job done. We can build custom ETL pipelines that suit our organization's needs from scratch or pay for an enterprise-ready tool. An ETL pipeline will most likely involve various tools, such as:

• Various data repositories, either on-premise or on the cloud

• Query languages, either or both SQL or NoSQL

• Programming and processing languages such as Python and Apache Spark

• Streaming platforms like Apache Kafka or Apache Flink

• Data orchestration tools like Apache Airflow

• No-code or low-code platforms like Hevo or Integrate.io.

The list goes on. All of this can be a bit overwhelming, which is why we need to remember that, in the end, it doesn't matter which tool we use as long as we get the job done because all ETL processes are different. The implementation of each ETL pipeline depends on the use case, the company, the budget, time requirements, and whoever builds the pipeline.

Choosing which tools to use at each stage of the pipeline is essential. However, it’s not as important as eventually bringing value to the organization by delivering clean data to their analytical environments and supporting data-driven decision-making.