ETL Database

Your central database for all things ETL: advice, suggestions, and best practices

ETL tools: an overview

ETL (extract, transform, load) tools get their name from the process they are used to accomplish. In the world of data warehousing, if you need to bring data from multiple different data sourcesinto a single, centralized database, you must first:

  • Extract data from a data source. This can be as simple as an Excel file to as complex as a Hubspot CRM.
  • Transform data by deduplicating it, combining it, and ensuring data quality.
  • Load data into the target database, data warehouse, or data lake, such as Amazon Redshift, Amazon S3, Google BigQuery, Snowflake, or Databricks.

ETL tools support data integration strategies by allowing companies to gather data from multiple data sources and consolidate it into a single, centralized location. ETL tools also make it possible for different types of data to work together.

There are many different sources of data: apps, Saas applications, and CRMs like Salesforce are just a few examples.

ETL tools for data integration and data transformation have been around for decades. The market has a number of big-name commercial players, including IBM InfoSphere DataStage, Microsoft SQL Server Integration Services, Azure Data Factory, Amazon's AWS Glue, Google Cloud Dataflow, and Oracle Data Integrator.

Technology developments have made ETL software both more sophisticated and easier to use. There have also been a newer crop of market entrants, both commercial and open source, to support better data management and accommodate a wider variety of data sources. These include Talend Open Studio, Informatica PowerCenter, Stitch, and Apache Hadoop.

Types of ETL tools

Organizations have three general options when it comes to ETL solutions:

  • Commercial tools, which could be on-premise or available as a cloud-enabled version
  • Open source tools, which are most often based in the cloud
  • DIY scripts, which are a hand-coded, on-premise option

Commercial ETL tools

Proprietary ETL solutions are often targeted at large enterprises with complex workflows, large volumes of data, and the need for real-time data processing. There are also commercial ETL tools that are designed to serve the small and medium business (SMB) market. Some of the most popular commercial ELT tools include:

  • IBM DataStage
  • Oracle Data Integrator
  • SAS Data Management
  • Informatica PowerCenter
  • Microsoft SQL Server Integration Services (SSIS)

Many cloud service providers also offer ETL tools as part of their packages. However, these tools typically only work with data that has been moved to that provider's cloud data warehouse, not on-premise data or data stored in other cloud platforms.

Open source ETL tools

Open source ETL solutions are attractive to many organizations because they can be powerful yet easy on the budget (even free!) Because they are widely accessible, these tools are also regularly via feedback from a large community of testers.Some high-performing open source ETL tools are:

  • Airbyte
  • Apache Camel, NiFi,and Kafka
  • CloverDX
  • Hevo Data
  • KETL
  • Logstash
  • Pentaho Data Integration
  • Singer
  • Stitch
  • Talend Open Studio

ETL scripts

If your organization has plenty of talented data engineers with time on their hands, the build-it-yourself path is another option for data integration projects. ETL scripts can be written in Python, SQL, or most other programming languages. Of these, Python remains a favorite option for data processing. In addition to being the language of choice of several popular open source ETL projects (i.e., Pygrametl, Petl, Bubbles), it's also a go-to for engineers and data scientists looking to DIY their ETL process.

While the DIY approach using Python is still complex and time-consuming, there are several tools now available to make the process easier, including the following:

  • Airflow, V which is an open-source project maintained by the Apache Software Foundation. Its sole purpose is to execute data pipelines through workflow automation.
  • Pandas, which is a Python library for data analysis. It is most widely used for data science/data analysis and machine learning tasks because it provides ready-to-use, high-performance data structures and data analysis tools. It runs on top of another module called Numpy.
  • Pygrametl, which is a full-fledged Python ETL framework with built-in functionality for many common ETL processes, allowing users to transform data into any data warehouse.

What is the best ETL tool?

The type of ETL tool that is best for any particular business will depend on several factors, including intended use cases, pricing, where the source data is stored, the type of data to be moved (complex data, unstructured data, etc.), need for scalability, and the level of expertise required to use it. For organizations without heavy IT support — or those who want to empower any employee to become a citizen analyst — desirable options include automation for creating no-code data pipelines, drag and drop interfaces, and user-friendly dashboards.

ETL tools for big data

Big data refers to large volumes of complex data that can come from apps, social platforms, SaaS applications, and other intensive data-generating operations. This data can be structured, semi-structured, or unstructured. The sheer size of big data makes it nearly impossible to process using traditional means. Yet that data is only valuable if it can be transformed and used for business and operational insights. The ideal ETL tool for big data will vary from company to company depending upon pricing and use case.

ETL tools for business intelligence (BI)

In today's competitive business environment, timely insights can be a game changer. ETL tools play a key role in providing business intelligence to companies of all sizes. Organizations generate data from a variety of disparate sources. To be properly analyzed by business intelligence tools, that data must be extracted from where it is residing, transformed by combining and deduplicating it, and then loaded into a central data storage option (lake, warehouse, lakehouse, etc). Once processed, the data can then be accessed and analyzed by data analytics programs such as Looker, Tableau, Chartio, and Power BI.

ETL tools for data migration

Data migration is the process of moving data from one system to another. Organizations undertake data migrations for a number of reasons. They might need to overhaul an entire system, upgrade databases, establish a new data warehouse, or merge new data from an acquisition or other source. Data migration is also necessary when deploying another system that sits alongside existing applications. Prior to thinking about which tools to use, it's critical to develop a data migration strategy.

ETL tools automate much of the migration process, saving time and greatly increasing the odds of a successful move. For migration purposes, the five key factors to consider when choosing an ETL tool are reliability, security, data sources and destinations, scalability, and pricing.

Stitch: an easy, cost-effective ETL tool

Stitch is a versatile ETL tool that can be used for big data, BI, and data migration. With Stitch, you can easily stream all your data to your data warehouse while improving data quality and reliability. With more than 130 pre-built connectors for data sources, Stitch is a versatile data integration tool with an intuitive user interface that automatically builds ETL pipelines. Features include free historical data replication, selective replication functionality, multiple user accounts, and an extensible platform that allows you to push data directly to the Stitch API. Optimize the value of your data today with a free, 14-day trial of Stitch.

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