Databricks feature store documentation. Databricks Feature Store Python API Databricks FeatureStoreClient Module that contains the FeatureStoreClient class. Features used in model training are automatically tracked with the model and, during model inference, the model itself retrieves them directly from the feature store. Simplify ETL, data warehousing, governance and AI on the Data Intelligence Platform. With Delta Universal Format aka UniForm, you can read now Delta tables with Iceberg and Hudi clients. With Databricks FeatureStoreClient class databricks. . Because Elasticsearch is a document-oriented, RESTful search engine, it has a variety of useful tools and can work with large, and otherwise intimidating, data sets. Elasticsearch benefits from being able to store all of your data in one database, with an elastic index container. Feb 11, 2026 · Databricks Online Feature Stores Databricks Online Feature Stores are a high-performance, scalable solution for serving feature data to online applications and real-time machine learning models. feature_store. client. Feature tables and models are registered in Unity Catalog, providing built-in governance, lineage, and cross-workspace feature sharing and discovery. With under 10 lines of code, you can connect to OpenAI, Anthropic, Google, and more. FeatureStoreClient(feature_store_uri: Optional [str] = None, model_registry_uri: Optional [str] = None) Bases: object Client for interacting with the Databricks Feature Store. Experience with workflows/orchestration and Databricks REST APIs. Subtasks: Standardize MLflow/Feature Store workflows Implement CI/CD for ML Improve model observability and drift monitoring Establish model documentation standards Implement Natural Language AI Analytics (Databricks Genie Enablement) Additional Resources DataFrames Documentation Building a Feature Store around Dataframes and Apache Spark Analyzing Your MLflow Data with DataFrames We would like to show you a description here but the site won’t allow us. This page explains what a feature store is and what benefits it provides, and the specific advantages of Databricks Feature Store. The Comprehensive Guide to Feature Stores Take your ML projects to the next level with feature engineering Eliminate hours of data transformation for your data scientists, data engineers and machine learning engineers. Create a Delta table Databricks FeatureStoreClient Defines the FeatureStoreClient class, which is used to interact with the Databricks Feature Store. Feb 10, 2026 · Databricks Feature Store This page is an overview of capabilities available when you use Databricks Feature Store with Unity Catalog. Build better AI with a data-centric approach. The Databricks Feature Store provides a central registry for features used in your AI and ML models. Feb 11, 2026 · Azure Databricks is a unified, open analytics platform for building, deploying, sharing, and maintaining enterprise-grade data, analytics, and AI solutions at scale. Featured speakers Data + AI Summit speakers include leading experts, researchers and open source contributors — from Databricks and across the data and AI community. How does feature engineering on Databricks work? The typical machine learning workflow using feature engineering on Databricks follows this path: Write code to convert raw data into features and create a Spark DataFrame containing the desired features. Delta Lake is an open-source storage framework that enables building a format agnostic Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, Hive, Snowflake, Google BigQuery, Athena, Redshift, Databricks, Azure Fabric and APIs for Scala, Java, Rust, and Python. The Databricks Data Intelligence Platform integrates with cloud storage and security in your cloud account, and manages and deploys cloud infrastructure for you. Defines the FeatureStoreClient class, which is used to interact with the Databricks Feature Store. LangChain is the easy way to start building completely custom agents and applications powered by LLMs. Online mode provides features at low latency for serving ML models or for the consumption of the same features in BI applications. Feature stores speed up your ML processes by helping feed your ML models with only the freshest, most relevant data. Jun 20, 2025 · Python API This page provides links to the Python API documentation of Databricks Feature Engineering and Databricks legacy Workspace Feature Store, and information about the client packages databricks-feature-engineering and databricks-feature-store. LangChain provides a prebuilt agent architecture and model integrations to help you get started quickly and seamlessly incorporate LLMs into your agents and applications. Deep understanding of Delta Lake features (ACID, OPTIMIZE, ZORDER, Auto Loader). Introduction to Data Lakes Data lakes provide a complete and authoritative data store that can power data analytics, business intelligence and machine learning Introduction to data lakes What is a data lake? A data lake is a central location that holds a large amount of data in its native, raw format. Dec 10, 2025 · Feature store overview and glossary This page explains how the Databricks Feature Store works and defines important terms. Databricks offers a unified platform for data, analytics and AI. hzvlp syab pffz iipnxt cjcmzc vuxho icfytz dkc ifsoxu rkzh