Best Data Engineering Tools for Windows

Composable is an enterprise-grade DataOps platform built for business users that want to architect data intelligence solutions and deliver operational data-driven products leveraging disparate data sources, live feeds, and event data regardless of the format or structure of the data. With a modern, intuitive dataflow visual designer, built-in services to facilitate data engineering, and a composable architecture that enables abstraction and integration of any software or analytical approach, Composable is the leading integrated development environment to discover, manage, transform and analyze enterprise data.

Consume data from any database, organize it into consistent metrics, and use it with every app. Build your Data and Reporting APIs faster with automated SQL generation, query optimization, access control, consistent metrics definitions, and API design. It takes only days to wrap any data source with a single reference Data API and simplify access to reporting and analytics data across your teams. Make it easy for data engineers and application developers to access the data from any source in a streamlined manner. - The single schema-less Data API endpoint - Review and configure metrics and dimensions in one place via UI - Data model visualization to make faster decisions - Data Export management scheduling AP Ready-to-use Report Builder and JavaScript components for charting libraries (Highcharts, BizCharts, Chart.js, etc.) makes it easy to embed data-rich functionality into your products. And you will not have to make custom report queries anymore!

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.

With ready access to the richest flexible semantic layer, explainable AI, and reusable data modeling, data engineers and scientists can be 95% more productive — create and expand semantic data models, understand any data interrelationship, and run federated queries to speed time to insight. Stardog offers the most advanced graph data virtualization and high-performance graph database — up to 57x better price/performance — to connect any data lakehouse, warehouse or enterprise data source without moving or copying data. Scale use cases and users at lower infrastructure cost. Stardog’s inference engine intelligently applies expert knowledge dynamically at query time to uncover hidden patterns or unexpected insights in relationships that enable better data-informed decisions and business outcomes.

DQOps is an open-source data quality platform designed for data quality and data engineering teams that makes data quality visible to business sponsors. The platform provides an efficient user interface to quickly add data sources, configure data quality checks, and manage issues. DQOps comes with over 150 built-in data quality checks, but you can also design custom checks to detect any business-relevant data quality issues. The platform supports incremental data quality monitoring to support analyzing data quality of very big tables. Track data quality KPI scores using our built-in or custom dashboards to show progress in improving data quality to business sponsors. DQOps is DevOps-friendly, allowing you to define data quality definitions in YAML files stored in Git, run data quality checks directly from your data pipelines, or automate any action with a Python Client. DQOps works locally or as a SaaS platform.

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.