Databricks offers a wide range of tools for various data-related tasks, including:

Data Processing and Engineering:

• Spark: A powerful engine for large-scale data processing, enabling tasks like ETL (Extract, Transform, Load), data cleaning, and feature engineering.
• Delta Lake is an open-source storage layer that brings reliability and performance to data lakes, allowing ACID transactions and efficient data versioning.
• Data Pipelines: Tools for building and orchestrating complex data workflows, enabling scheduled and automated data processing.

Data Science and Machine Learning:

• MLflow: An open-source platform for managing the end-to-end machine learning lifecycle, from experimentation and model tracking to deployment and monitoring.
• AutoML: Tools for automating various aspects of machine learning, such as feature engineering, model selection, and hyperparameter tuning.
• Feature Store: A centralized repository for storing and managing features, making them reusable across different machine-learning models.

SQL Analytics and Business Intelligence:

• SQL Analytics: A collaborative SQL environment for running ad-hoc queries, building dashboards, and generating reports.
• Databricks SQL (formerly Redash): A tool for creating interactive dashboards and visualizations from various data sources.
• Delta Sharing: An open protocol for securely sharing data and tables with external organizations.

Developer Tools and Integrations:

• Notebooks: Interactive environments for writing and executing code in Python, R, Scala, and SQL.
• APIs and SDKs: Programmatic interfaces for interacting with Databricks from various programming languages.
• Visual Studio Code Extension: Integration with Visual Studio Code for seamless development and debugging.
• Partner Integrations: Connections with popular tools and platforms, such as Tableau, Power BI, and Fivetran.