11 Best Data Engineering Tools Reviewed In 2025
Best Data Engineering Tools Shortlist
Here's my pick of the 10 best software from the 11 tools reviewed.
Our one-on-one guidance will help you find the perfect fit.
Managing data efficiently is a challenge you face every day. You need tools that help you process, analyze, and visualize data without breaking a sweat. Data engineering tools can be your ally in tackling these tasks, saving your team time and effort.
I’ve independently tested and reviewed numerous software solutions to bring you the best options available. My goal is to provide you with an unbiased, well-researched review of top picks that fit your needs.
In this article, you’ll find detailed insights into each tool’s features, benefits, and potential drawbacks. Whether you're looking to simplify data integration or improve pipeline monitoring, there's something here for you. Let’s dive in and explore the solutions that could transform your data processes.
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Best Data Engineering Tools Summary
This comparison chart summarizes pricing details for my top data engineering tools selections to help you find the best one for your budget and business needs.
| Tool | Best For | Trial Info | Price | ||
|---|---|---|---|---|---|
| 1 |  Tableau Tableau makes data analysis and visualization accessible, helping organizations to establish a data culture that drives informed decision-making. | Best for data visualization | Free trial available | From $35/user/month (billed annually) | Website |
| 2 |  Stitch Stitch’s highly extensible no-code platform integrates with more than 130 data sources. | Best for ETL simplicity | Free trial available | From $100/month | Website |
| 3 |  Panoply Panoply’s user-friendly UI makes it easy to keep track of individual projects, data sources, tables, and users. | Best for quick data integration | 21-day free trial + free demo | From $1,558/month | Website |
| 4 |  Keboola Connection Keboola comes with a library of Scaffolds––existing data workflow templates––that you can deploy with just a few clicks and customize to your needs. | Best for team collaboration | Free trial available | Pricing upon request | Website |
| 5 |  DIAdem DIAdem enables users to standardize analysis and reporting procedures through reusable data queries, analysis scripts, and report templates. | Best for test data management | Free demo available | From $735/year | Website |
| 6 |  Logilica Insights Logilica integrates with Git and DevOps tools to track your dev team’s performance, flag software delivery risks, reduce cycle times, and promote team health. | Best for software delivery insights | 14-day free trial available | Pricing upon request | Website |
| 7 |  Allstacks Visual dashboards provide granular real-time insights into the delivery status of your software roadmaps, with ML-powered predictive forecasting to flag delivery risks like bottlenecks. | Best for predictive analytics | Free demo available | From $400/contributor/year | Website |
| 8 |  Databand Databand offers unified visibility into all your data tools | Best for data pipeline monitoring | Free demo available | Pricing upon request | Website |
| 9 |  IBM Engineering Lifecycle Management IBM ELM promotes full lifecycle traceability and offers a variety of integrated planning, design, testing, analysis, and reporting tools. | Best for lifecycle management | Free demo available | Pricing upon request | Website |
| 10 |  ACL Robotics by Galvanize ACL Robotics’ data visualization gives governance professionals instant insight into risk and compliance status. | Best for audit automation | Not available | Pricing upon request | Website |
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Deel
Visit WebsiteThis is an aggregated rating for this tool including ratings from Crozdesk users and ratings from other sites.4.8 -
New Relic
Visit WebsiteThis is an aggregated rating for this tool including ratings from Crozdesk users and ratings from other sites.4.3 -
Checkmk
Visit WebsiteThis is an aggregated rating for this tool including ratings from Crozdesk users and ratings from other sites.4.7
Best Data Engineering Tool Reviews
Below are my detailed summaries of the best data engineering tools that made it onto my shortlist. My reviews offer a detailed look at the key features, pros & cons, integrations, and ideal use cases of each tool to help you find the best one for you.
Tableau is a business intelligence tool designed for data visualization, primarily serving analysts, data scientists, and business users. It helps users create interactive and shareable dashboards that depict trends, patterns, and insights from data.
Why I picked Tableau: It's known for its data visualization capabilities, making it ideal for users who need to present data engagingly. Its drag-and-drop interface allows you to build visualizations without needing deep technical knowledge. Tableau supports various data sources, enabling you to connect to your data wherever it resides. Its strong community support offers a wealth of resources to help you maximize its features.
Standout features & integrations:
Features include interactive dashboards, data blending, and real-time analytics. You can create compelling visual stories with minimal effort. Its ability to handle large datasets efficiently sets it apart from many competitors.
Integrations include Salesforce, Google Analytics, Amazon Redshift, Microsoft Excel, SQL Server, Snowflake, Oracle, SAP Hana, Hadoop, and Dropbox.
Pros and cons
Pros:
- Interactive data exploration
- Customizable visualizations
- Real-time data updates
Cons:
- Steep learning curve
- Higher cost for large teams
Stitch is an ETL tool designed for data engineers and analysts who need to move data from various sources into a data warehouse. It simplifies the data pipeline process, allowing users to extract, transform, and load data efficiently.
Why I picked Stitch: Its straightforward approach to ETL makes it ideal for teams looking to simplify data movement. With Stitch, you can set up data pipelines in minutes, reducing the need for complex coding. It supports a wide range of data sources, giving you flexibility in where your data comes from. Its automatic schema detection helps you maintain data integrity without manual intervention.
Standout features & integrations:
Features include automatic data updates, which keep your data fresh without manual effort. Real-time data replication ensures that your team always has the latest information. Its user-friendly interface allows you to configure data sources quickly.
Integrations include Salesforce, Google Analytics, MySQL, PostgreSQL, Amazon Redshift, Snowflake, Microsoft SQL Server, MongoDB, Facebook Ads, and Shopify.
Pros and cons
Pros:
- Easy-to-use interface
- Quick setup process
- Automatic schema detection
Cons:
- Limited customization options
- No built-in data visualization
Panoply is a cloud-based data platform aimed at data engineers and business analysts who need to quickly integrate data from multiple sources. It automates the data pipeline process, allowing users to store, analyze, and visualize data without complex infrastructure.
Why I picked Panoply: It's designed for quick data integration, making it perfect for teams that need rapid access to data insights. The platform automates data management tasks, which means you can focus more on analyzing data rather than maintaining it. Its scalability allows you to handle growing data volumes with ease. With built-in tools for data visualization, Panoply helps you turn raw data into actionable insights swiftly.
Standout features & integrations:
Features include automatic data warehousing, which simplifies data storage and access. The platform offers built-in SQL workbench, empowering you to run queries directly. Its user-friendly dashboard provides a clear view of your data operations.
Integrations include Google Analytics, Salesforce, Amazon Redshift, MySQL, PostgreSQL, MongoDB, Shopify, Facebook Ads, HubSpot, and QuickBooks.
Pros and cons
Pros:
- Automated data management
- Built-in visualization tools
- User-friendly dashboard
Cons:
- Limited advanced analytics
- Not suited for small datasets
Keboola Connection is a data operations platform aimed at data teams and business analysts who need to collaborate on data projects. It simplifies the process of data orchestration, making it easier for teams to work together and manage data workflows.
Why I picked Keboola Connection: It's designed to enhance team collaboration, allowing multiple users to work on data projects simultaneously. The platform provides real-time data sharing, which helps your team stay updated with the latest changes. It offers a flexible environment where you can customize data processes to suit your needs. With its user-friendly interface, Keboola Connection makes complex data operations accessible to everyone on your team.
Standout features & integrations:
Features include real-time data processing, which ensures your team always works with the latest information. It has built-in data governance tools that help you maintain data quality and compliance. The platform's automation capabilities reduce manual tasks, freeing up your team for more strategic work.
Integrations include Snowflake, Amazon Redshift, Google BigQuery, MySQL, PostgreSQL, Microsoft Azure, Salesforce, Google Analytics, HubSpot, and Slack.
Pros and cons
Pros:
- Real-time data collaboration
- Flexible customization options
- Built-in data governance tools
Cons:
- Higher learning curve
- Requires technical expertise
DIAdem is a data management software designed for engineers and data analysts who need to analyze, visualize, and report on test data. It helps users efficiently manage large volumes of data from various sources, providing valuable insights into test results.
Why I picked DIAdem: It's specifically geared towards test data management, which is essential for teams dealing with extensive test datasets. DIAdem offers advanced data analysis tools that allow you to perform complex calculations and derive meaningful insights. Its visualization capabilities enable you to create detailed charts and graphs, making it easier for your team to understand data trends. The software's reporting features help streamline the process of sharing results with stakeholders.
Standout features & integrations:
Features include a customizable user interface that lets you tailor the workspace to your needs. The software provides automated data processing, reducing the time spent on manual tasks. It also offers script development capabilities, allowing you to automate repetitive processes and increase efficiency.
Integrations include LabVIEW, MATLAB, Microsoft Excel, Python, CANalyzer, TestStand, Oracle, SAP, SQL Server, and PostgreSQL.
Pros and cons
Pros:
- Advanced data analysis tools
- Detailed visualization capabilities
- Supports script development
Cons:
- High learning curve
- Limited offline access
Logilica Insights is an analytics platform designed for software development teams and project managers. It provides insights into software delivery processes, helping teams improve efficiency and track performance metrics.
Why I picked Logilica Insights: It's focused on providing software delivery insights, which are essential for teams looking to optimize their development processes. The platform offers detailed analytics on team performance, allowing you to identify bottlenecks and areas for improvement. With its predictive analytics, you can anticipate future challenges and plan accordingly. Its dashboard offers a comprehensive view of project progress, helping your team stay on track.
Standout features & integrations:
Features include real-time performance tracking, which keeps you updated on team activities. The tool offers customizable reporting, allowing you to focus on the metrics that matter most to your team. Its collaboration features enhance communication among team members, fostering a more cohesive working environment.
Integrations include Jira, GitHub, GitLab, Bitbucket, Slack, Trello, Asana, Microsoft Teams, Azure DevOps, and Jenkins.
Pros and cons
Pros:
- Real-time performance tracking
- Predictive analytics capabilities
- Detailed insights into bottlenecks
Cons:
- Requires technical expertise
- High learning curve
Allstacks is an analytics platform for software development teams and project managers. It provides predictive insights into project health and delivery timelines, helping teams make informed decisions and manage risks effectively.
Why I picked Allstacks: It's tailored for predictive analytics, which is vital for teams needing foresight into project outcomes. Allstacks offers real-time visibility into project metrics, allowing you to track progress and anticipate issues. Its machine learning models provide forecasts on delivery timelines, enhancing your planning capabilities. The platform's risk management tools enable your team to identify and mitigate potential problems early.
Standout features & integrations:
Features include customizable dashboards that let you focus on key metrics relevant to your team. The platform offers historical data analysis, helping you learn from past projects to improve future outcomes. Its collaboration tools facilitate communication among team members, ensuring everyone is aligned.
Integrations include Jira, GitHub, GitLab, Bitbucket, Slack, Trello, Asana, Microsoft Teams, Azure DevOps, and Jenkins.
Pros and cons
Pros:
- Predictive analytics capabilities
- Real-time project visibility
- Customizable dashboards
Cons:
- Requires technical expertise
- Complex setup process
Databand is a data observability platform designed for data engineers and operations teams who need to monitor and manage data pipelines. It helps users detect, troubleshoot, and resolve data quality issues, ensuring smooth data operations.
Why I picked Databand: It's tailored for monitoring data pipelines, making it essential for teams that need to maintain data integrity and reliability. Databand offers real-time alerts, so your team can quickly address any issues that arise. Its detailed insights into data flow help you understand where bottlenecks occur. The platform's user-friendly interface allows you to visualize data processes with ease.
Standout features & integrations:
Features include anomaly detection, which helps you spot irregular data patterns early. The platform provides historical data tracking, allowing you to analyze past pipeline performance. Its customizable dashboards give you the flexibility to focus on metrics that matter to your team.
Integrations include Apache Airflow, Apache Spark, Databricks, Google Cloud Platform, Amazon Web Services, Snowflake, Microsoft Azure, Kubernetes, Slack, and PagerDuty.
Pros and cons
Pros:
- Real-time monitoring capabilities
- Detailed data flow insights
- User-friendly interface
Cons:
- May need additional resources
- Steep learning curve
IBM Engineering Lifecycle Management is a comprehensive platform designed for engineering teams and project managers. It supports the entire product lifecycle, from requirements management to quality assurance, enabling teams to manage complex engineering projects efficiently.
Why I picked IBM Engineering Lifecycle Management: It's tailored for lifecycle management, making it ideal for teams that need to oversee every stage of product development. The platform provides integrated tools for managing requirements, changes, and quality, helping your team maintain control over projects. Its collaborative environment ensures that all stakeholders are aligned and informed. The traceability features allow you to track changes and decisions throughout the project lifecycle.
Standout features & integrations:
Features include requirements management that helps you capture and prioritize project needs efficiently. The platform offers change management tools, which are crucial for adapting to evolving project demands. Its quality management capabilities ensure that your products meet industry standards and customer expectations.
Integrations include IBM Rational DOORS, IBM Rational Team Concert, IBM Rational Quality Manager, Jira, GitHub, Jenkins, Microsoft Azure DevOps, Slack, Salesforce, and SAP.
Pros and cons
Pros:
- Comprehensive lifecycle coverage
- Strong traceability features
- Facilitates team collaboration
Cons:
- Initial setup can be time-consuming
- Limited customization options
ACL Robotics by Galvanize is an automation platform tailored for audit and compliance professionals. It helps teams automate repetitive tasks, analyze data, and ensure compliance with regulations, providing users with more efficient audit processes.
Why I picked ACL Robotics by Galvanize: It's focused on audit automation, which is crucial for teams looking to reduce manual work in compliance tasks. The platform allows you to schedule audits and run them automatically, freeing your team from routine checks. With its data analysis capabilities, you can quickly identify anomalies and potential risks. It also offers a centralized platform for managing audit trails and documentation, enhancing transparency in your processes.
Standout features & integrations:
Features include continuous monitoring, which keeps your audit processes up-to-date without manual intervention. The tool offers advanced analytics to help you spot trends and patterns in your data. It also provides customizable workflows, allowing you to tailor audit processes to your specific needs.
Integrations include SAP, Oracle, Microsoft Dynamics, Salesforce, Workday, NetSuite, QuickBooks, Google Sheets, Dropbox, and Box.
Pros and cons
Pros:
- Automates audit processes
- Advanced data analytics
- Continuous monitoring features
Cons:
- Limited offline access
- Complex configuration options
Other Data Engineering Tools
Here are some additional data engineering tools options that didn’t make it onto my shortlist, but are still worth checking out:
- Pecan
For predictive analytics automation
Data Engineering Tool Selection Criteria
When selecting the best data engineering tools to include in this list, I considered common buyer needs and pain points like data integration complexity and scalability. I also used the following framework to keep my evaluation structured and fair:
Core Functionality (25% of total score)
To be considered for inclusion in this list, each solution had to fulfill these common use cases:
- Data integration
- Data transformation
- Data pipeline automation
- Data visualization
- Data storage management
Additional Standout Features (25% of total score)
To help further narrow down the competition, I also looked for unique features, such as:
- Real-time data processing
- Anomaly detection
- Predictive analytics
- Customizable workflows
- Advanced security features
Usability (10% of total score)
To get a sense of the usability of each system, I considered the following:
- Intuitive interface design
- Ease of navigation
- Minimal learning curve
- Responsiveness of the system
- Customization options
Onboarding (10% of total score)
To evaluate the onboarding experience for each platform, I considered the following:
- Availability of training videos
- Interactive product tours
- Access to templates
- Presence of chatbots for support
- Webinars for new users
Customer Support (10% of total score)
To assess each software provider’s customer support services, I considered the following:
- Availability of 24/7 support
- Responsiveness of support team
- Access to a knowledge base
- Community forums for users
- Variety of support channels
Value For Money (10% of total score)
To evaluate the value for money of each platform, I considered the following:
- Competitive pricing
- Features offered at the price point
- Cost of add-ons or extras
- Flexibility of pricing plans
- Satisfaction with return on investment
Customer Reviews (10% of total score)
To get a sense of overall customer satisfaction, I considered the following when reading customer reviews:
- Overall satisfaction ratings
- Commonly mentioned strengths
- Consistently reported weaknesses
- Feedback on customer support
- User recommendations
How to Choose Data Engineering Tools
It’s easy to get bogged down in long feature lists and complex pricing structures. To help you stay focused as you work through your unique software selection process, here’s a checklist of factors to keep in mind:
| Factor | What to Consider |
| Scalability | Ensure the tool can grow with your data needs. Consider future data volume increases and whether the tool can handle large datasets without performance issues. |
| Integrations | Look for tools that integrate with your existing software stack, such as databases and analytics platforms, to streamline data flow and avoid compatibility issues. |
| Customizability | Check if the tool allows customization to fit your specific data processes. You may need to adjust workflows or data handling methods to suit your team's needs. |
| Ease of Use | Evaluate the user interface and learning curve. Your team should be able to adopt the tool quickly without requiring extensive training or technical expertise. |
| Budget | Review the pricing plans and ensure they align with your financial constraints. Consider both upfront costs and any ongoing fees that may apply. |
| Security Safeguards | Ensure the tool offers robust security features to protect your data. Look for encryption, access controls, and compliance with industry standards. |
| Support | Consider the availability and quality of customer support. You may need assistance with setup or troubleshooting, so responsive support can be crucial. |
| Performance | Assess the tool's performance metrics, such as processing speed and reliability. Your team needs a tool that operates efficiently under various workloads. |
Trends in Data Engineering Tools
In my research, I sourced countless product updates, press releases, and release logs from different data engineering tools vendors. Here are some of the emerging trends I’m keeping an eye on:
- Real-time data processing: Businesses demand quicker insights, leading to a surge in real-time data processing capabilities. Tools are evolving to handle data streams and provide instant analytics, helping teams make faster decisions. Vendors like Apache Kafka are leading in this space.
- Data democratization: More tools are focusing on making data accessible to non-technical users. This trend empowers all team members to leverage data insights without needing a data science background. Platforms like Tableau are enhancing user-friendly interfaces to support this shift.
- Data observability: Ensuring data quality and reliability is becoming critical. Tools now offer features to monitor and alert on data anomalies, helping teams maintain trust in their data. Databand is a notable vendor providing strong observability solutions.
- Privacy and compliance: With increasing regulations, tools are incorporating features to ensure data privacy and compliance with laws like GDPR. This trend is crucial for businesses to avoid legal pitfalls, and vendors like Informatica are integrating compliance checks.
- Cloud-native architectures: There's a growing shift towards cloud-native data engineering tools, allowing for better scalability and flexibility. These tools are optimized for cloud environments, providing businesses with more adaptable data solutions. Snowflake exemplifies this trend with its cloud-first approach.
What Are Data Engineering Tools?
Data engineering tools are software solutions designed to help manage, process, and transform data within an organization. These tools are typically used by data engineers, data analysts, and IT professionals who need to handle large volumes of data efficiently. Features like data integration, real-time processing, and data observability help with maintaining data quality, providing quick insights, and ensuring compliance. Overall, these tools enable businesses to turn raw data into valuable insights that drive informed decision-making.
Features of Data Engineering Tools
When selecting data engineering tools, keep an eye out for the following key features:
- Data integration tools: This feature allows you to connect and consolidate data from multiple sources, making it easier to analyze and draw insights.
- Real-time processing: Enables the handling of data as it arrives, providing instant insights and supporting faster decision-making.
- Data visualization: Offers tools to create charts and graphs, helping users easily interpret complex data sets.
- Scalability: Ensures the tool can handle growing data volumes without compromising performance, crucial for expanding businesses.
- Customizable workflows: Allows teams to tailor processes to fit specific data handling needs, enhancing efficiency.
- Data observability: Provides monitoring and alert features to ensure data quality and reliability are maintained.
- Security safeguards: Includes encryption and access controls to protect sensitive data and comply with regulations.
- Predictive analytics: Uses machine learning models to forecast trends and outcomes, aiding in proactive planning.
- Automation capabilities: Reduces manual tasks by automating repetitive processes, increasing productivity.
- User-friendly interface: Ensures the tool is easy to navigate, minimizing the learning curve for new users.
Benefits of Data Engineering Tools
Implementing data engineering tools provides several benefits for your team and your business. Here are a few you can look forward to:
- Improved data quality: Data observability features ensure that your data remains accurate and reliable, reducing errors in analysis.
- Faster decision-making: Real-time processing and instant data insights help your team make informed decisions quickly.
- Increased efficiency: Automation capabilities eliminate repetitive tasks, freeing up time for more strategic activities.
- Enhanced collaboration: User-friendly interfaces and customizable workflows support teamwork by allowing everyone to access and work with data easily.
- Scalability: The ability to handle growing data volumes means your tools can grow alongside your business without performance issues.
- Better compliance: Security safeguards like encryption and access controls help ensure your data practices meet industry regulations.
- Proactive planning: Predictive analytics provide forecasts that allow your team to anticipate trends and plan accordingly.
Costs and Pricing of Data Engineering Tools
Selecting data engineering tools requires an understanding of the various pricing models and plans available. Costs vary based on features, team size, add-ons, and more. The table below summarizes common plans, their average prices, and typical features included in data engineering tools solutions:
Plan Comparison Table for Data Engineering Tools
| Plan Type | Average Price | Common Features |
| Free Plan | $0 | Basic data integration, limited data storage, and community support. |
| Personal Plan | $10-$50 /user /month | Advanced data visualization, real-time processing, and email support. |
| Business Plan | $50-$150 /user /month | Customizable workflows, enhanced security features, and priority customer support. |
| Enterprise Plan | $150-$500/user /month | Full data observability, predictive analytics, and dedicated account management. |
Data Engineering Tools FAQs
Here are some answers to common questions about data engineering tools:
Which tool is used for data engineering?
Data engineering tools vary depending on your needs, but common ones include Apache Spark, Hadoop, and AWS for handling big data. These tools help you process, store, and analyze large datasets efficiently. Your choice should depend on your specific data requirements and existing big data software.
What questions should I ask a data engineer?
When interviewing a data engineer, ask about their experience with ETL tools, data modeling, and data orchestration. You might also want to know about their familiarity with cloud platforms and their ability to handle large datasets. These questions will help you understand their technical skills and problem-solving abilities.
Which platform is best for data engineering?
The best platform depends on your specific needs, but popular choices include Snowflake for cloud-based analytics and Amazon Redshift for data warehousing. Both offer powerful capabilities for handling data at scale. Consider factors like integration with your existing systems and cost when choosing a platform.
What are ETL tools in data engineering?
ETL tools are used to extract, transform, and load data from various sources into a data warehouse. This process cleans and organizes raw data, making it ready for analysis and machine learning. Common ETL tools include Informatica, Talend, and Apache NiFi, each with unique features.
What is data orchestration?
Data orchestration involves automating the processes of data collection, transformation, and loading. Tools like Apache Airflow and Prefect help manage and schedule these tasks, ensuring data flows smoothly across systems. This is crucial for maintaining data consistency and reliability.
How do I choose the right data engineering tool?
Choosing the right tool involves assessing your data volume, integration needs, and budget. Consider tools that offer scalability and support for real-time processing if your data needs are large and fast-moving. Evaluate the tool’s ease of use and support options to ensure it fits your team’s capabilities.
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