Compare the Top Data Engineering Tools for Mac as of January 2026

What are Data Engineering Tools for Mac?

Data engineering tools are designed to facilitate the process of preparing and managing large datasets for analysis. These tools support tasks like data extraction, transformation, and loading (ETL), allowing engineers to build efficient data pipelines that move and process data from various sources into storage systems. They help ensure data integrity and quality by providing features for validation, cleansing, and monitoring. Data engineering tools also often include capabilities for automation, scalability, and integration with big data platforms. By streamlining complex workflows, they enable organizations to handle large-scale data operations more efficiently and support advanced analytics and machine learning initiatives. Compare and read user reviews of the best Data Engineering tools for Mac currently available using the table below. This list is updated regularly.

  • 1
    Domo

    Domo

    Domo

    Domo puts data to work for everyone so they can multiply their impact on the business. Our cloud-native data experience platform goes beyond traditional business intelligence and analytics, making data visible and actionable with user-friendly dashboards and apps. Underpinned by a secure data foundation that connects with existing cloud and legacy systems, Domo helps companies optimize critical business processes at scale and in record time to spark the bold curiosity that powers exponential business results.
  • 2
    ClearML

    ClearML

    ClearML

    ClearML is the leading open source MLOps and AI platform that helps data science, ML engineering, and DevOps teams easily develop, orchestrate, and automate ML workflows at scale. Our frictionless, unified, end-to-end MLOps suite enables users and customers to focus on developing their ML code and automation. ClearML is used by more than 1,300 enterprise customers to develop a highly repeatable process for their end-to-end AI model lifecycle, from product feature exploration to model deployment and monitoring in production. Use all of our modules for a complete ecosystem or plug in and play with the tools you have. ClearML is trusted by more than 150,000 forward-thinking Data Scientists, Data Engineers, ML Engineers, DevOps, Product Managers and business unit decision makers at leading Fortune 500 companies, enterprises, academia, and innovative start-ups worldwide within industries such as gaming, biotech , defense, healthcare, CPG, retail, financial services, among others.
    Starting Price: $15
  • 3
    Peliqan

    Peliqan

    Peliqan

    Peliqan.io is an all-in-one data platform for business teams, startups, scale-ups and IT service companies - no data engineer needed. Easily connect to databases, data warehouses and SaaS business applications. Explore and combine data in a spreadsheet UI. Business users can combine data from multiple sources, clean the data, make edits in personal copies and apply transformations. Power users can use "SQL on anything" and developers can use low-code to build interactive data apps, implement writebacks and apply machine learning. Key Features: - Wide range of connectors: Integrates with over 250+ data sources and applications. Popular connectors: Odoo, Salesforce, Exact Online, Visma, NetSuite, Power BI. - Spreadsheet UI and magical SQL: Explore data in a rich spreadsheet UI. Use Magical SQL to combine and transform data. - Data Activation: Create data apps in minutes. Implement data alerts, distribute custom reports by email (PDF, Excel) , implement Reverse ETL flows.
    Starting Price: $199
  • 4
    Kestra

    Kestra

    Kestra

    Kestra is an open-source, event-driven orchestrator that simplifies data operations and improves collaboration between engineers and business users. By bringing Infrastructure as Code best practices to data pipelines, Kestra allows you to build reliable workflows and manage them with confidence. Thanks to the declarative YAML interface for defining orchestration logic, everyone who benefits from analytics can participate in the data pipeline creation process. The UI automatically adjusts the YAML definition any time you make changes to a workflow from the UI or via an API call. Therefore, the orchestration logic is defined declaratively in code, even if some workflow components are modified in other ways.
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