Best Data Management Software for Mode - Page 2

Compare the Top Data Management Software that integrates with Mode as of November 2025 - Page 2

This a list of Data Management software that integrates with Mode. Use the filters on the left to add additional filters for products that have integrations with Mode. View the products that work with Mode in the table below.

  • 1
    Integrate.io

    Integrate.io

    Integrate.io

    Unify Your Data Stack: Experience the first no-code data pipeline platform and power enlightened decision making. Integrate.io is the only complete set of data solutions & connectors for easy building and managing of clean, secure data pipelines. Increase your data team's output with all of the simple, powerful tools & connectors you’ll ever need in one no-code data integration platform. Empower any size team to consistently deliver projects on-time & under budget. We ensure your success by partnering with you to truly understand your needs & desired outcomes. Our only goal is to help you overachieve yours. Integrate.io's Platform includes: -No-Code ETL & Reverse ETL: Drag & drop no-code data pipelines with 220+ out-of-the-box data transformations -Easy ELT & CDC :The Fastest Data Replication On The Market -Automated API Generation: Build Automated, Secure APIs in Minutes - Data Warehouse Monitoring: Finally Understand Your Warehouse Spend - FREE Data Observability: Custom
  • 2
    Meltano

    Meltano

    Meltano

    Meltano provides the ultimate flexibility in deployment options. Own your data stack, end to end. Ever growing connector library of 300+ connectors have been running in production for years. Run workflows in isolated environments, execute end-to-end tests, and version control everything. Open source gives you the power to build your ideal data stack. Define your entire project as code and collaborate confidently with your team. The Meltano CLI enables you to rapidly create your project, making it easy to start replicating data. Meltano is designed to be the best way to run dbt to manage your transformations. Your entire data stack is defined in your project, making it simple to deploy it to production. Validate your changes in development before moving to CI, and in staging before moving to production.
  • 3
    MariaDB

    MariaDB

    MariaDB

    MariaDB Platform is a complete enterprise open source database solution. It has the versatility to support transactional, analytical and hybrid workloads as well as relational, JSON and hybrid data models. And it has the scalability to grow from standalone databases and data warehouses to fully distributed SQL for executing millions of transactions per second and performing interactive, ad hoc analytics on billions of rows. MariaDB can be deployed on prem on commodity hardware, is available on all major public clouds and through MariaDB SkySQL as a fully managed cloud database. To learn more, visit mariadb.com.
  • 4
    Singer

    Singer

    Singer

    Singer describes how data extraction scripts called “taps” and data loading scripts called “targets” should communicate, allowing them to be used in any combination to move data from any source to any destination. Send data between databases, web APIs, files, queues, and just about anything else you can think of. Singer taps and targets are simple applications composed with pipes—no daemons or complicated plugins needed. Singer applications communicate with JSON, making them easy to work with and implement in any programming language. Singer also supports JSON Schema to provide rich data types and rigid structure when needed. Singer makes it easy to maintain state between invocations to support incremental extraction.
  • 5
    Apache Airflow

    Apache Airflow

    The Apache Software Foundation

    Airflow is a platform created by the community to programmatically author, schedule and monitor workflows. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity. Airflow pipelines are defined in Python, allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically. Easily define your own operators and extend libraries to fit the level of abstraction that suits your environment. Airflow pipelines are lean and explicit. Parametrization is built into its core using the powerful Jinja templating engine. No more command-line or XML black-magic! Use standard Python features to create your workflows, including date time formats for scheduling and loops to dynamically generate tasks. This allows you to maintain full flexibility when building your workflows.