5 Ways to Become a Data Driven Organization With Databricks

  • Data access: Quickly access available data sets or connect to any data source, on-premises or in the cloud.
  • Multi-language support: Explore data using interactive notebooks with support for multiple programming languages within the same notebook, including R, Python, Scala, and SQL.
  • Interactive visualizations: Visualize insights through a wide assortment of point-and-click visualizations, or use powerful scriptable options like Matplotlib, ggplot, and D3.
  • Real-time co-authoring: Work on the same notebook in real-time while tracking changes with detailed revision history.
  • Automatic versioning: Automatic change-tracking and versioning help you pick up where you left off.
  • Git-based repos: Simplified Git-based collaboration, reproducibility, and CI/CD workflows.
  • Runs sidebar: Automatically log experiments, parameters, and results from notebooks directly to MLflow as runs, and quickly see and load previous runs and code versions from the sidebar.
  • Dashboards: Share insights with your colleagues and customers, or let them run interactive queries with Spark-powered dashboards.
  • Run notebooks as jobs: Turn notebooks or JARs into resilient production jobs with a click or an API call.
  • Jobs scheduler: Execute jobs for production pipelines on a specific schedule.
  • Notifications and logs: Set alerts and quickly access audit logs for easy monitoring and troubleshooting.
  • Data sources: Databricks can read data from and write data to a variety of data formats such as CSV, Delta Lake, JSON, Parquet, XML, and other formats, as well as data storage providers such as Amazon S3, Google BigQuery, and Cloud Storage, Snowflake, and other providers.
  • Developer tools: Databricks supports various developer tools such as DataGrip, IntelliJ, PyCharm, Visual Studio Code, and others that allow you to work with data through Databricks clusters and Databricks SQL warehouses by writing code.
  • Partner solutions: Databricks has validated integrations with third-party solutions such as Fivetran, Power BI, Tableau, and others. They allow you to work with data through Databricks clusters and SQL warehouses, with low-code and no-code experience in many cases. These solutions enable common scenarios such as data ingestion, data preparation and transformation, business intelligence (BI), and machine learning. Databricks also provides Partner Connect — a user interface that allows some of these validated solutions to integrate more quickly and easily with your Databricks clusters and SQL warehouses.

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