databricks-solutions/lakebase-online-ml

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# lakebase-online-ml A full-stack retail application demonstrating low-latency model serving with Databricks Lakebase as the online feature/serving store. Built with [`apx`](https://github.com/databricks-solutions/apx) — Python + FastAPI backend, React + shadcn/ui frontend, with an auto-generated TypeScript API client from the OpenAPI schema. ## Video Overview Include a GIF overview of what your project does. Use a service like Quicktime, Zoom or Loom to create the video, then convert to a GIF. ## Installation ### Development Mode Start all development servers (backend, frontend, and OpenAPI watcher) in detached mode: ```bash apx dev start ``` This runs an apx development server, which in turn runs the backend, frontend, and OpenAPI watcher. All servers run in the background, with logs kept in-memory by the apx dev server. ### Monitoring & Logs ```bash # View all logs apx dev logs # Stream logs in real-time apx dev logs -f # Check server status apx dev status # Stop all servers apx dev stop ``` ### Code Quality Run type checking and linting for both TypeScript and Python: ```bash apx dev check ``` ### Build Create a production-ready build: ```bash apx build ``` ### Deployment Deploy to Databricks: ```bash databricks bundle deploy -p <your-profile> ``` ## How to get help Databricks support doesn't cover this content. For questions or bugs, please open a GitHub issue and the team will help on a best effort basis. ## License © 2025 Databricks, Inc. All rights reserved. The source in this notebook is provided subject to the Databricks License [https://databricks.com/db-license-source]. All included or referenced third party libraries are subject to the licenses set forth below. | library | description | license | source | |----------------------------------------|-------------------------|------------|-----------------------------------------------------|

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databricks-spark-declarative-pipelines

软件开发工程师

Creates, configures, and updates Databricks Lakeflow Spark Declarative Pipelines (SDP/LDP) using serverless compute. Handles streaming tables, materialized views, CDC, SCD Type 2, and Auto Loader ingestion patterns. Use when building data pipelines, working…

原文语言：英语

2026-03-09

databricks-synthetic-data-gen

软件开发工程师

Generate realistic synthetic data using Spark + Faker (strongly recommended). Supports serverless execution, multiple output formats (Parquet/JSON/CSV/Delta), and scales from thousands to millions of rows. For small datasets (<10K rows), can optionally…

原文语言：英语

2026-03-09

databricks-zerobus-ingest

软件开发工程师

Build Zerobus Ingest clients for near real-time data ingestion into Databricks Delta tables via gRPC. Use when creating producers that write directly to Unity Catalog tables without a message bus, working with the Zerobus Ingest SDK in…

原文语言：英语

2026-03-09

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