Best Data Engineering Tools for Microsoft Power BI
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Compare the Top Data Engineering Tools that integrate with Microsoft Power BI as of October 2025
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This a list of Data Engineering tools that integrate with Microsoft Power BI. Use the filters on the left to add additional filters for products that have integrations with Microsoft Power BI. View the products that work with Microsoft Power BI in the table below.
What are Data Engineering Tools for Microsoft Power BI?
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 Microsoft Power BI currently available using the table below. This list is updated regularly.
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AnalyticsCreator
AnalyticsCreator
Streamline your data engineering workflows with AnalyticsCreator by automating the design and deployment of robust data pipelines for databases, warehouses, lakes, and cloud services. The faster pipeline deployment ensures seamless connectivity across your ecosystem, improving innovation with modern engineering practices. Integrate a wide range of data sources and targets effortlessly, ensuring seamless ecosystem connectivity. Improve development cycles with automated documentation, lineage tracking, and schema evolution. Support modern engineering practices such as CI/CD and agile methodologies to accelerate collaboration and innovation across teams. -
2
Sifflet
Sifflet
Automatically cover thousands of tables with ML-based anomaly detection and 50+ custom metrics. Comprehensive data and metadata monitoring. Exhaustive mapping of all dependencies between assets, from ingestion to BI. Enhanced productivity and collaboration between data engineers and data consumers. Sifflet seamlessly integrates into your data sources and preferred tools and can run on AWS, Google Cloud Platform, and Microsoft Azure. Keep an eye on the health of your data and alert the team when quality criteria aren’t met. Set up in a few clicks the fundamental coverage of all your tables. Configure the frequency of runs, their criticality, and even customized notifications at the same time. Leverage ML-based rules to detect any anomaly in your data. No need for an initial configuration. A unique model for each rule learns from historical data and from user feedback. Complement the automated rules with a library of 50+ templates that can be applied to any asset. -
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Microsoft Fabric
Microsoft
Reshape how everyone accesses, manages, and acts on data and insights by connecting every data source and analytics service together—on a single, AI-powered platform. All your data. All your teams. All in one place. Establish an open and lake-centric hub that helps data engineers connect and curate data from different sources—eliminating sprawl and creating custom views for everyone. Accelerate analysis by developing AI models on a single foundation without data movement—reducing the time data scientists need to deliver value. Innovate faster by helping every person in your organization act on insights from within Microsoft 365 apps, such as Microsoft Excel and Microsoft Teams. Responsibly connect people and data using an open and scalable solution that gives data stewards additional control with built-in security, governance, and compliance.Starting Price: $156.334/month/2CU -
4
Datameer
Datameer
Datameer revolutionizes data transformation with a low-code approach, trusted by top global enterprises. Craft, transform, and publish data seamlessly with no code and SQL, simplifying complex data engineering tasks. Empower your data teams to make informed decisions confidently while saving costs and ensuring responsible self-service analytics. Speed up your analytics workflow by transforming datasets to answer ad-hoc questions and support operational dashboards. Empower everyone on your team with our SQL or Drag-and-Drop to transform your data in an intuitive and collaborative workspace. And best of all, everything happens in Snowflake. Datameer is designed and optimized for Snowflake to reduce data movement and increase platform adoption. Some of the problems Datameer solves: - Analytics is not accessible - Drowning in backlog - Long development -
5
Ascend
Ascend
Ascend gives data teams a unified and automated platform to ingest, transform, and orchestrate their entire data engineering and analytics engineering workloads, 10X faster than ever before. Ascend helps gridlocked teams break through constraints to build, manage, and optimize the increasing number of data workloads required. Backed by DataAware intelligence, Ascend works continuously in the background to guarantee data integrity and optimize data workloads, reducing time spent on maintenance by up to 90%. Build, iterate on, and run data transformations easily with Ascend’s multi-language flex-code interface enabling the use of SQL, Python, Java, and, Scala interchangeably. Quickly view data lineage, data profiles, job and user logs, system health, and other critical workload metrics at a glance. Ascend delivers native connections to a growing library of common data sources with our Flex-Code data connectors.Starting Price: $0.98 per DFC -
6
Numbers Station
Numbers Station
Accelerating insights, eliminating barriers for data analysts. Intelligent data stack automation, get insights from your data 10x faster with AI. Pioneered at the Stanford AI lab and now available to your enterprise, intelligence for the modern data stack has arrived. Use natural language to get value from your messy, complex, and siloed data in minutes. Tell your data your desired output, and immediately generate code for execution. Customizable automation of complex data tasks that are specific to your organization and not captured by templated solutions. Empower anyone to securely automate data-intensive workflows on the modern data stack, free data engineers from an endless backlog of requests. Arrive at insights in minutes, not months. Uniquely designed for you, tuned for your organization’s needs. Integrated with upstream and downstream tools, Snowflake, Databricks, Redshift, BigQuery, and more coming, built on dbt. -
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Molecula
Molecula
Molecula is an enterprise feature store that simplifies, accelerates, and controls big data access to power machine-scale analytics and AI. Continuously extracting features, reducing the dimensionality of data at the source, and routing real-time feature changes into a central store enables millisecond queries, computation, and feature re-use across formats and locations without copying or moving raw data. The Molecula feature store provides data engineers, data scientists, and application developers a single access point to graduate from reporting and explaining with human-scale data to predicting and prescribing real-time business outcomes with all data. Enterprises spend a lot of money preparing, aggregating, and making numerous copies of their data for every project before they can make decisions with it. Molecula brings an entirely new paradigm for continuous, real-time data analysis to be used for all your mission-critical applications. -
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Datactics
Datactics
Profile, cleanse, match and deduplicate data in drag-and-drop rules studio. Lo-code UI means no programming skill required, putting power in the hands of subject matter experts. Add AI & machine learning to your existing data management processes In order to reduce manual effort and increase accuracy, providing full transparency on machine-led decisions with human-in-the-loop. Offering award-winning data quality and matching capabilities across multiple industries, our self-service solutions are rapidly configured within weeks with specialist assistance available from Datactics data engineers. With Datactics you can easily measure data to regulatory & industry standards, fix breaches in bulk and push into reporting tools, with full visibility and audit trail for Chief Risk Officers. Augment data matching into Legal Entity Masters for Client Lifecycle Management. -
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Dremio
Dremio
Dremio delivers lightning-fast queries and a self-service semantic layer directly on your data lake storage. No moving data to proprietary data warehouses, no cubes, no aggregation tables or extracts. Just flexibility and control for data architects, and self-service for data consumers. Dremio technologies like Data Reflections, Columnar Cloud Cache (C3) and Predictive Pipelining work alongside Apache Arrow to make queries on your data lake storage very, very fast. An abstraction layer enables IT to apply security and business meaning, while enabling analysts and data scientists to explore data and derive new virtual datasets. Dremio’s semantic layer is an integrated, searchable catalog that indexes all of your metadata, so business users can easily make sense of your data. Virtual datasets and spaces make up the semantic layer, and are all indexed and searchable.
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