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Tools for Data Mesh


Tools to implement Data Mesh

There are a variety of tools that can be used to implement and manage a data mesh architecture. Some of the most popular tools include:

  • Data catalogs: Data catalogs help you to discover and understand the different data products that are available. This can help you to identify the data that you need for your analytics and to understand how it is structured. Popular data catalogs include Confluent Schema Registry, Dataiku, and Collibra.

  • Data integration tools: Data integration tools help you to combine data from different sources into a single, unified view. This can make it easier to perform analytics on the data. Popular data integration tools include Talend Open Studio, Apache NiFi, and Matillion ETL.

  • Cloud data platforms: Cloud data platforms such as Google Cloud Platform and Amazon Web Services offer a variety of services that can help you to analyze data from different sources. These services include data lakes, data warehouses, and machine learning tools. Popular cloud data platforms include Google Cloud Platform, Amazon Web Services, and Microsoft Azure.

  • DataHub: DataHub is an open-source metadata search and discovery platform developed by LinkedIn. It plays a crucial role in data cataloging and metadata management within a Data Mesh. DataHub allows users to discover, understand, and access data products across different domains.

    • Key Features:

      • Centralized data catalog.

      • Integration with various data sources.

      • Data discovery and lineage tracking.

      • Collaboration features for data product teams.

    • Reference: DataHub - LinkedIn Engineering

  • LakeFS: LakeFS is an open-source data lake management platform that introduces version control and branching to data lakes. It ensures data quality and governance by enabling fine-grained control and auditing of changes to data within the lake.

    • Key Features:

      • Versioning and branching for data lakes.

      • Data quality monitoring.

      • Collaboration and access control.

      • Integration with various cloud storage providers.

    • Reference: LakeFS

  • Databand: Databand is a data pipeline observability platform that helps organizations monitor and manage data pipelines within a Data Mesh. It provides visibility into data flow, pipeline health, and data quality.

    • Key Features:

      • Pipeline observability and monitoring.

      • Data quality tracking.

      • Dependency tracking for data pipelines.

      • Integration with popular data processing frameworks.

    • Reference: Databand

  • Great Expectations: Great Expectations is an open-source framework for data quality validation, documentation, and profiling. It is a critical tool for ensuring data quality and consistency within a Data Mesh.

    • Key Features:

      • Data validation and quality checks.

      • Data documentation and profiling.

      • Integration with popular data processing frameworks.

      • Customizable data quality expectations.

    • Reference: Great Expectations

  • Amundsen:

    • Overview: Amundsen is an open-source data discovery and metadata platform that provides a central catalog for data assets and their associated metadata. It can help users discover and understand data products in a Data Mesh.

    • Key Features:

      • Data catalog with search and discovery capabilities.

      • Data lineage tracking.

      • Collaboration features for data users.

      • Integration with various data sources and storage systems.

    • Reference: Amundsen - Pinterest Engineering

  • Delta Lake: Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads. It's particularly useful for maintaining data quality and consistency within a Data Mesh.

    • Key Features:

      • ACID transactions for data lakes.

      • Schema enforcement and evolution.

      • Data versioning and rollback.

      • Data quality monitoring.

    • Reference: Delta Lake - Databricks

Other tools that can be used for data mesh include:

  • Data governance tools: Data governance tools help you to manage the access, usage, and quality of data. Popular data governance tools include Cloudera Data Catalog, IBM InfoSphere Information Governance Suite, and Collibra.

  • Data quality tools: Data quality tools help you to identify and correct errors and inconsistencies in data. Popular data quality tools include Informatica Data Quality, Talend Open Studio for Data Quality, and IBM InfoSphere Data Quality.

  • Data visualization tools: Data visualization tools help you to create charts and graphs that make it easier to understand and interpret data. Popular data visualization tools include Tableau, Power BI, and QlikView.

Here is a comparison of some of the most popular data mesh tools:



Data Mesh is a relatively new concept, and there are several tools and technologies emerging to support its implementation. These tools are designed to help organizations manage decentralized data, promote data ownership, and enable self-service data access.


Sash Barige

Oct/25/2022


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