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Explore essential resources to kickstart your journey with Databricks. Access tutorials, guides, and...
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Unlocking High-Frequency Workloads with Nanosecond-Precision Timestamps
Enterprises store timestamps to understand when events occur, establish their sequence, and measure the time between them. For most analytical workloads, microsecond precision is sufficient. However, in fields like finance, manufacturing, and scienti...
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PDFs to Production
Databricks just solved a huge problem - unlocking the value from unstructured data. One of the biggest challenges enterprises face when scaling agents is access to unstructured data. Nearly 80% of enterprise knowledge is trapped in PDFs, reports, and...
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Solution Accelerator Series | IOC Matching and Multicloud Query Federation
Cybersecurity in multicloud, multiregion environments can be complicated by fragmented security controls, data dispersion and compliance requirements. The IOC Matching and Multicloud Query Federation Solution Accelerator shows how a lakehouse platfor...
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Announcement | Lakebase Search: State-of-the-art full-text and vector search for Postgres
Lakebase Postgres now includes built-in full-text and vector search, generally available on AWS and Azure. With lakebase_vector for approximate nearest-neighbor search and lakebase_text for BM25 search, teams can run semantic, keyword, and hybrid ret...
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Rebuilding an 800-Line PySpark Pipeline in 200 Lines of Databricks SQL
End-to-End DBSQL ETL Use Case For years, a production ETL pipeline on Databricks meant a pile of PySpark. SCD Type 2 dimensions, cost-controlled materialized views, idempotent reloads of late data: each one was code you wrote and maintained. That ha...
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Turning P&C Claim Documents into Structured Data with Databricks AI Functions
A claim document may contain a policy number, loss date, estimated amount, and a description spread across paragraphs and form fields. Extracting those values is useful but we also need to know where they came from and whether they are suitable for d...
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From Idea to Code: Building with the Databricks SDK for Python
Databricks Python SDK launchSix months ago Databricks announced the release of the Databricks SDK for Python to much fanfare. Since then it has been adopted by over 1,000 customers and is used in several open source tools such as Datahub. Over the pa...
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The practical use of the Python SDK here is really helpful, especially the way the debugger and type hints make it easier to explore the API without constantly switching back to the documentation. The job-monitoring example is a useful real-world app...
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[PARTNER BLOG] Building an Agent-Native Data Quality Manager with Databricks Apps and DQX
Introduction: The Data Quality ParadoxWhat We BuiltThe Technology StackArchitecture Deep DiveThe Four-Layer ArchitectureLayer 1: UsersLayer 2: Databricks Apps PlatformLayer 3: Compute LayerLayer 4: Data LayerWhy This Design?The User Experience: A Com...
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A useful point is the separation between OBO for user-level data access and service principals for controlled background jobs. The versioning approach in Lakebase also looks practical for keeping data-quality rules auditable as requirements change. ...
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TokenMaxing to ValueMaxing with Databricks
TL;DR: Teams are TokenMaxing, measuring success by tokens consumed, and two things go wrong at once: spend climbs and visibility does not. Both are solvable on Databricks. Route every model and agent call through the Unity Gateway and each call becom...
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CUSTOMER STORY | PicPay automates fraud triage with Databricks Genie Agents
“Genie Agents made our risk management more agile and efficient, helping us to identify and prioritize potentially fraudulent transactions.” - Flávio Arena, Tech Manager, PicPay PicPay, one of Brazil’s leading digital banks, uses statistical anomaly...
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Build Your First Databricks App with Codex: A Practical Setup Guide
Build Your First Databricks App with Codex: A Practical Setup Guide AI coding agents can help data practitioners move from an idea to a working application without spending days learning every detail of a new Python or JavaScript framework, deploymen...
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How to choose the right AI agent path on Databricks
There is no single one-size-fits-all “Databricks agent” experience, because agents can be designed to solve any number of business challenges. You may want a governed agent that understands a specific business domain, a highly customized agentic appl...
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Ingest data from Microsoft Outlook
Databricks recently released the Microsoft Outlook connector in Lakeflow Connect, making it easier to ingest Outlook email data directly into Azure Databricks for analytics and AI workloads. The connector supports managed ingestion pipelines, increme...
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Built a live Helsinki tram tracker on Databricks with Lakeflow, Lakebase, Genie and Apps
Hi everyone! I built a near-real-time app that follows every tram and metro train in Helsinki using only Databricks: no Kafka, no extra servers, and the whole thing deploys from one repository.What the app doesA live map of every tram and metro train...
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Solution Accelerator Series | Toxicity Detection in Gaming
Build Healthier Communities With Real-Time AI! This Solution Accelerator helps you detect toxic in-game chat in real time so you can protect players, reduce churn and keep your gaming communities engaged and healthy. It shows you how to use a lakehou...
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Great initiative by Databricks! Real-time toxicity detection can play a key role in creating safer, healthier gaming communities while helping moderation teams scale effectively. Excited to see how AI and lakehouse architecture can come together to ...
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