FME Databricks


                     FME Databricks

Here’s a breakdown of how FME and Databricks can be used together for powerful data workflows:

What is FME?

  • Data Integration Platform: FME (Feature Manipulation Engine) by Safe Software is a powerful data integration and transformation platform.
  • No-Code Approach: It offers a visual, drag-and-drop interface, enabling users to build complex data workflows without extensive coding expertise.
  • Broad Format Support: FME supports over 450 data formats and systems, making it exceptionally versatile for handling diverse data sources.

What is Databricks?

  • Unified Data and AI Platform: Databricks provides a cloud-based platform centered around a lakehouse, combining the best features of data warehouses and data lakes.
  • Spark, Delta Lake, MLflow: It is built upon technologies like Apache Spark (for fast data processing), Delta Lake (for reliable data storage and transactions), and MLflow (for machine learning model management).

How FME and Databricks Integrate

FME provides specialized components for seamless interaction with Databricks:

  1. Databricks Reader:
    • Read from Delta Lake: Fetches data directly from Databricks Delta Lake tables.
    • Support for Queries: Executes SQL queries against Databricks tables for customized data extraction.
  2. Databricks Writer:
    • Create and Update Delta Lake Tables: Enables the creation of new Delta Lake tables in Databricks or updates existing ones.
    • Flexible Table Handling: Supports overwriting, appending, or upserting data.

Use Cases

Here are common scenarios where FME and Databricks are used in conjunction:

  • Data Migration and ETL:
    • Move data from various sources (databases, files, APIs, etc.) into Databricks’ Delta Lake for centralized storage and analytics.
    • Transform and cleanse data during the migration process.
  • Data Preparation for ML:
    • Load raw data into Databricks.
    • Use FME’s rich data transformation tools to clean, format, and feature-engineer data in preparation for machine learning tasks within the Databricks environment.
  • Orchestrating Data Workflows:
    • Design complex data processing pipelines within FME that incorporate Databricks steps and transformations from other systems.
    • Automate these workflows using the FME Server to schedule and run them on demand.


Imagine a scenario where an organization gathers data from:

  • A CRM system (e.g., Salesforce)
  • A website analytics platform (e.g., Google Analytics)
  • An on-premise PostgreSQL database

An FME workspace might do the following:

  1. Connect: Use FME connectors to read from Salesforce, Google Analytics, and PostgreSQL.
  2. Transform: Cleanse, merge, and restructure the data into a format suitable for analysis.
  3. Write to Databricks: The Databricks Writer loads the transformed data into a unified Delta Lake table.
  4. Databricks Processing: Data scientists and analysts then use Databricks’ tools (Spark, MLflow, etc.) to perform advanced analytics and build machine learning models on the prepared data.

Getting Started

Databricks Training Demo Day 1 Video:

You can find more information about Databricks Training in this Dtabricks Docs Link



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