The Databricks platform integrates storage, compute, and collaborative tooling for building data pipelines, analyzing large datasets, and developing machine learning models. Workflows include ingestion, transformation, feature engineering, and model operations, supported by notebooks, jobs, and governance features designed to standardize access and lineage.

Data preparation capabilities address integration, quality, and transformation steps required to ready data for analysis. For data science teams, the platform supports popular libraries and frameworks to train and evaluate models at scale. Visualization and query tools provide access for analysts and business users to explore data under role-based controls.

Operational features include workload orchestration, monitoring, cost management, and security controls aligned to enterprise requirements. The platform’s lakehouse architecture is designed to consolidate analytics and machine learning on a single system while integrating with external data sources and BI applications.

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