When building their information architecture or data “ecosystem” to process big data, data engineers utilize a range of different data management tools to create data pipelines (e.g. ETL solutions), set up their data lakes, apply data analysis—often using artificial intelligence and machine learning algorithms—and use data visualization to generate reader-friendly business intelligence (BI) reporting. And accessible, actionable business intelligence can facilitate faster decision-making by up to a factor of 5X.
Whether or not you are a data engineer, you might find yourself needing to employ data engineering tools. This is partly because there are simply not enough data engineer experts to go around with about 6,500 people on LinkedIn calling themselves some variation of data analysts compared to 6,600 data engineering jobs for this same title in San Francisco alone.
You might be a data engineer looking for the best tools of your trade; or, you might be a product manager with DIY data engineering needs. With this in mind, we’ve rounded up 10 top tools for data engineers in 2022 below.
The Best Tools for Data Engineering
Here’s a shortlist of the best data engineering tools and what they’re best for:
What do I look for when I select the best data engineering tool? Here’s a summary of my evaluation criteria:
User Interface (UI): Is the tool’s design clean and attractive?
Usability: How easy is the tool to learn and master? Does the company offer good tech support, user support, tutorials, documentation, and training?
Setup Time: How long will this tool take to set up? Will it take weeks, months, or minutes to be useful for my use case? Is it a cloud platform or on-premise (or hybrid)?
Integrations and Extensibility: Is it easy to connect with other tools? Which pre-built integrations does it offer? Will this tool be compatible with my data sources? Does it offer custom integrations and have an API or SDK I can use to build my own connector?
Value for Money: How appropriate is the tool’s price for its features, capabilities, and use case(s)? Is the pricing clear, transparent and flexible?
Data Engineering Tool Key Features
Here are some of the key features to keep in mind when evaluating data engineering tools:
Tool & database integrations: Works with Apache Hadoop, Amazon Redshift & AWS, MongoDB, Apache Kafka, Microsoft Azure, Apache Cassandra, MapReduce programs, and noSQL database.
Extract-transform-load (ETL): Moves your data from the source to your data warehouse.
Data Warehousing/Data Lake Connections: Data storage and organization functionality accessible for engineering and analysis.
Data Lineage/Traceability: Tracks your data’s “chain of custody” for auditability.
Data Transformations: Converts your data from one format/structure into another format or structure.
Metadata Support:Preserves the context related to your data.
Batch or Stream Processing: Data is replicated either at intervals (batch) or in real-time (stream).
Workflow Automation: Templated workflows that can be reused to save time.
No-Code Features: User-friendly drag-and-drop wizards allow non-coders to use the tool or specific features of the tool.
Reporting and Data Visualization Capabilities: Enable users to turn data into reader-friendly charts and graphics in real-time.
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Overviews of the 10 Best Tools for Data Scientists
Here’s a brief description of each data engineering tool to showcase what each tool does best, including screenshots to highlight some of their features.
1. Databand – Best data processing tool for data pipeline observability
Databand.ai is a platform that enables data engineers to track data pipeline performance metrics and metadata from all their tools in real-time using a unified dashboard. This enables DataOps professionals to identify, troubleshoot, and address data pipeline issues—like delays, task failures, and quality problems—in real-time.
Databand is a great tool for maintaining visibility throughout your pipeline(s) and tracking data lakes, allowing you to manage data quality, freshness, and lineage; predict and prevent SLA violations; monitor efficiency and resource use; and run health checks on your data assets.
Databand offers out-the-box integrations with more than 20 tools including Apache Airflow, Apache Spark, Snowflake, and S3, and has a robust documentation library and open-source SDK to help you develop your own custom integrations.
Databand offers customized pricing upon request and also offers a free trial upon request.
2. Stitch – Best no-code ETL tool for data engineers
Stitch a cloud-based extract-transform-load (ETL) data pipeline that moves your data from the source to your data warehouse. Stitch’s main benefits are its extensibility and its simplicity—it’s a no-code tool, which makes it user-friendly and quick to implement even for non-technical users. Stitch is entirely self-serve, which means you don’t need to liaise with account managers or customer service reps.
While most ETL platforms only integrate with a few dozen of the most popular SaaS solutions and data sources, Stitch currently supports integrations with more than 130 data sources and analysis tools.
Stitch’s standard plan starts from $100/month for 5 users with users getting 2 months free if they choose to be billed annually. Stitch also offers a free trial.
Acquired by Salesforce in 2019, Tableau is a leading self-service visual analytics platform that aims to make data analytics and visualization accessible to everyone, using data from anywhere. Tableau’s user-friendly interface—with its drag-and-drop data query tool—and its massive user community and robust help resources make it a great choice for businesses that want to foster a data culture in their organizations.
Tableau can be deployed in the cloud, on-premise, or as a Salesforce CRM extension, and offers robust built-in AI/ML functions, data governance tools, and collaboration and visual storytelling features.
Tableau provides native integrations with a large number of SaaS tools and data sources. It also offers tools and APIs to help developers customize and extend Tableau to meet their needs.
Tableau pricing starts at $70/user/month (billed annually). Tableau also offers a free trial.
Logilica Insights is a productivity assistant for software teams that pulls data from Git and DevOps tools to simplify the management of the engineering lifecycle. Logilica Insights enables you to apply data analytics, automate repetitive workflows and set alerts for delivery risks like missing or delayed code reviews and other bottlenecks. It also helps DevOps leads to identify potentially unhealthy work patterns, developer overload, knowledge silos, and other common pitfalls to promote better team health.
Logilica has built-in connectors for GitHub, GitLab, and other tools. The company also has a Web API for integrating custom data sources.
Customized enterprise pricing is available upon request. Logilica’s “Start-Up” and “Scale-Up” plans are currently in beta—and free.
5. Panoply – Best data engineering tool for rapid data warehouse deployment
Panoply is a data warehousing tool that allows users to set up a data lake and connect their data sources in mere minutes. Panoply’s cloud-based platform supports zero-code integrations with all your data sources, syncs automatically to keep data up to date, and requires no maintenance.
Panoply is highly secure—it’s SOC-II certified and HIPAA-compliant—provides granular control over how you store individual data sources and offers easy SQL-based view creation.
Panoply currently supports integrations with more than 300 data sources, data analysis tools, and visualization tools.
Panoply costs from $399/month and they offer a 14-day free trial.
ACL Robotics is a robotic process automation and data analytics solution designed for governance professionals. ACL automates the tedious and repetitive tasks involved in auditing and compliance processes, eliminating manual testing, sampling, and reporting. ACL Robotics helps to foster collaboration between IT, finance, audit, risk, and compliance teams and break down silos.
ACL Robotics has built-in connectors for tools like SAP and Concur and enables further extensibility through ODBC technology.
ACL Robotics offers customized pricing upon request.
7. Allstacks – Best data engineering tool for software delivery intelligence
Allstacks is a powerful DevOps tool that consolidates data from your software development lifecycle tools to give you comprehensive visibility into the status of your engineering projects and team performance, whether you’re an executive, engineering leader, data engineer, product leader, or agile team leader.
Allstacks aggregates data into a variety of thoughtfully designed visual dashboards including portfolio reports, milestone reports, pull request cycle time charts, WIP reports, and process stage visualization reports. Using AI and machine learning, this tool enables predictive forecasting to detect bottlenecks and reduce software delivery delays, with automated alerts to help keep projects on track.
Allstacks integrates with a variety of software development lifecycle tools including project management tools; source code management tools; builds, continuous integration, and deployment tools; and communication tools.
Allstacks offers customized pricing upon request. Schedule a demo for a 30-day free trial.
IBM Engineering Lifecycle Management (ELM) is a robust end-to-end ELM tool that improves engineering data traceability through customized reporting and dashboards. It facilitates collaboration and communication among stakeholders across the engineering lifecycle, from requirements through testing and deployment.
IBM ELM offers a variety of handy features that streamline software delivery. For instance, it allows you to reuse requirements, processes, and design data to fast-track the development of multiple product versions. It also helps you to identify the best design early in the product life cycle through features like visual modeling, simulation, and architecture testing.
IBM ELM supports a wide variety of integrations with other IBM and third-party products and enables extensibility through OSLC open standards.
IBM Engineering Lifecycle Management offers customized pricing upon request.
9. DIAdem – Best data engineering tool for data post-processing workflow automation & custom reporting
DIAdem is data management software that makes it easier for data engineers to post-process measurement data. The software is specifically geared towards aggregating, inspecting, analyzing, and reporting large data sets and facilitates workflow automation.
DIAdem offers a variety of built-in engineering-specific tools to search, view, investigate, and transform data, as well as a robust drag-and-drop report editor that enables you to save reporting templates.
DIAdem’s DataPlugins tool supports over a thousand file formats.
Diadem pricing is tiered by plan and pricing is available upon request. A free trial is available for DIAdem’s Professional tier.
10. Keboola – Best full stack data integration platform
Keboola is a cloud-based data integration platform with a highly intuitive user interface that allows even non-technical business users to execute key data workflows. The platform enables you to consolidate data workflows in their entirety using a wide range of automation features and integrations, so you can stop worrying about building your data stack and do everything in one place.
Keboola’s collaborative workspaces allow you to manage all your data projects in one place, with powerful data management, workflow automation, and security controls.
Keboola supports hundreds of integrations that are ready-to-use, so you don’t need to have API knowledge or write scripts to make your favorite tools play together.
Subscriptions start from $2500/month. The free version includes 300 free minutes each month, after which each minute is charged at 14 cents per minute.
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What Do You Think About These Data Engineering Tools?
Which of these tools is best for your needs? We’d love to hear from you in the comments. Want to stay in the loop about the best QA tools and the latest insights from top thinkers in quality engineering? Sign up for our newsletter.