ETL Pipeline Meaning
Extract, transform and load (ETL) is a process in data warehousing responsible for pulling data out of the source systems and placing it into a data warehouse.
The ETL process became a popular concept in the 1970s and is often used when referring to data integration. The extract, transform and load process encompasses identification of the source data, extraction from that source and transition into a format usable by the business. The final stage of the ETL process takes place when the data is loaded into the target system for analysis and reporting.
For example, you run a website that sells widgets. Each widget has a name, description, price and the date it was created. Your website receives hundreds of orders each day, with customers buying different quantity of different types of widgets. ETL will take the information your website receives from these customers and how they're interacting with your stock and send it back to a data warehouse, transforming it into a language that the warehouse understands on the way there.
Building an ETL Pipeline
ETL processes are typically handled by software such as Stitch Data or Fivetran. However, these tools can be expensive and don't provide the level of flexibility and control that many data scientists want. If you're working with sensitive customer data or building a startup on a tight budget, you may prefer not to use cloud services at all. Also, if you want to build something from scratch and understand how everything works under the hood, it's important to know how to build an ETL pipeline yourself.
An ETL pipeline extracts data from one or more sources, transforms the data according to business rules and technical requirements, then loads it into a target system for use. This can be done on-premises with software installed locally, or in the cloud with services from cloud vendors like AWS Glue or Matillion.
In a typical business environment, you need to extract data from multiple sources and load it into a centralized location. From there, you can perform analysis to generate insights that drive key business decisions.
The process of extracting data from various sources and loading it into a single location is known as ETL (extract, transform, load). If the extraction and transformation components are automated, it becomes an ETL pipeline.
ETL pipelines work well for most use cases. They're simple to implement and can process large amounts of data.
The most common use case for ETL is to move data from one source to a data warehouse. For example, you might have transactional data in a relational database (data from customers’ orders), and you want to extract that data and load it into your analytical database where it can be used for reporting purposes.
Why You Should Build One
Building an ETL pipeline takes raw data and makes it possible to provide analytics and insights from the data. This is what contributes to business intelligence, which helps organizations make data-backed decisions that are more sound than those directed by unreliable or incorrect data. The major benefits of building an ETL pipeline include:
- Migrating data from archaic systems and data stacks, to a data warehouse
- Increasing data team capacity. ETL Pipelines make working with data more time efficient, which leaves your data team more time to contribute to high-impact projects, instead of day-to-day data requests and maintenance
- Standardizing data in one place. This makes data accessible to those who need it, and ensure that it's reliable.
Modern ETL pipelines are becoming increasingly important in the world of big data. Here are a few examples of modern ETL pipelines:
- Real-time streaming pipelines: With the increasing use of real-time data, there is a need for ETL pipelines that can handle streaming data. Tools such as Apache Kafka and Apache Spark Streaming are commonly used for this purpose.
- Cloud-based pipelines: The rise of cloud computing has led to the development of cloud-based ETL pipelines. These pipelines are built using cloud-based tools such as Amazon Web Services (AWS) and Microsoft Azure.
- Data lake pipelines: Data lakes are becoming a popular way to store and process large amounts of data. ETL pipelines can be used to move data from various sources into a data lake, where it can be analyzed and processed.
- Machine learning pipelines: ETL pipelines can be used to prepare data for machine learning models. This involves cleaning, transforming, and normalizing data to ensure that it is suitable for use in machine learning models.
- Automated pipelines: Automation is becoming increasingly important in the world of ETL. Automated pipelines can be used to reduce the amount of manual work required to build and maintain ETL pipelines. Tools such as Apache Airflow and Luigi are commonly used for this purpose.
- Low-code ETL pipelines: With the advent of low-code platforms, it has become easier to build ETL pipelines without requiring extensive programming knowledge. These platforms offer drag-and-drop interfaces and pre-built connectors to make building ETL pipelines faster and more accessible to non-technical users.
- Data warehouse pipelines: ETL pipelines are commonly used to populate data warehouses, which are used to store and analyze data in a structured way. Tools such as Amazon Redshift and Google BigQuery offer cloud-based data warehousing solutions that can be populated using ETL pipelines.
- Data synchronization pipelines: ETL pipelines can be used to keep data in sync between different systems. This is common in scenarios where data needs to be shared across multiple systems, such as between a CRM and an ERP system.
- Data migration pipelines: ETL pipelines can be used to migrate data between different systems, such as when moving from an on-premise system to a cloud-based system. This involves extracting data from the source system, transforming it to match the target system, and loading it into the target system.
- Data integration pipelines: ETL pipelines can be used to integrate data from multiple sources into a unified system. This is common in scenarios where data needs to be combined from different systems, such as when analyzing sales data from multiple stores.
Learn More with Secoda
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