
Trino
Fast distributed SQL query engine for big data analytics.

A unified platform for data, AI, and governance that brings AI to your data.

The Databricks Data Intelligence Platform provides a unified environment for data engineering, data science, machine learning, and AI application development. It leverages a lakehouse architecture to combine the best aspects of data warehouses and data lakes, offering reliable data management and governance with the flexibility to work with diverse data types. The platform supports generative AI model development, deployment, and monitoring, ensuring data privacy and control. Key capabilities include intelligent data processing for batch and real-time data, unified governance across data and AI assets, and open data sharing for collaboration. Databricks enables organizations to democratize insights, drive down costs, and accelerate AI initiatives by bringing AI directly to their data.
The Databricks Data Intelligence Platform provides a unified environment for data engineering, data science, machine learning, and AI application development.
Explore all tools that specialize in data warehousing. This domain focus ensures Databricks Data Intelligence Platform delivers optimized results for this specific requirement.
Explore all tools that specialize in develop machine learning models. This domain focus ensures Databricks Data Intelligence Platform delivers optimized results for this specific requirement.
Provides ACID transactions, scalable metadata handling, and unified streaming and batch data processing on cloud storage.
An open-source platform to manage the ML lifecycle, including experiment tracking, model packaging, and deployment.
A unified governance solution for data and AI, providing a single place to manage data access control, auditing, and lineage.
A vectorized query engine that provides significant performance improvements for SQL and data processing workloads.
A platform for sharing and monetizing data assets, models, and applications.
1. Set up a Databricks workspace.
2. Connect to your data sources (e.g., cloud storage, databases).
3. Configure data pipelines using Databricks Delta Lake for reliable data ingestion.
4. Explore and transform data using Spark SQL and Python.
5. Build machine learning models using MLflow for experiment tracking.
6. Deploy models as REST endpoints for real-time predictions.
7. Implement data governance policies using Unity Catalog.
8. Monitor model performance and data quality using Databricks monitoring tools.
All Set
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Fast distributed SQL query engine for big data analytics.

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