Databricks Generative AI Engineer Associate: Serving and Monitoring GenAI Models
Putting a generative AI model behind an endpoint is only the start of production operations. A reliable serving design must control identity, model…
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Databricks Generative AI Engineer Associate: MLflow for Generative AI Evaluation
Generative AI evaluation is difficult because many important qualities are not captured by one accuracy number. A response can be fluent but unsupported,…
Read articleDatabricks Generative AI Engineer Associate: Unity Catalog for GenAI Governance
Generative AI governance must cover more than model access. Production applications depend on prompts, evaluation datasets, vector indexes, tools, functions, model endpoints, traces,…
Read articleDatabricks Generative AI Engineer Associate: Building RAG Systems
Retrieval-augmented generation, or RAG, improves a generative AI application by retrieving relevant enterprise information and placing that information into the model’s context before…
Read articleDatabricks Data Engineer Associate: Unity Catalog Governance & Access
Unity Catalog is the governance layer that connects identity, permissions, lineage, auditing, discovery, and policy across data and AI assets in Databricks. The…
Read articleDatabricks Data Engineer Associate: Troubleshooting Slow Spark Jobs
A slow Spark job is rarely fixed by immediately choosing a larger cluster. Performance problems usually come from a specific stage: unnecessary data…
Read articleDatabricks Data Engineer Associate: Schema Evolution and Data Quality
Schema change is normal in data engineering. Sources add fields, rename concepts, change optionality, widen numeric ranges, and occasionally send records that do…
Read articleDatabricks Data Engineer Associate: PySpark DataFrames
PySpark DataFrames are one of the main programming interfaces for transforming structured and semi-structured data on Databricks. They let engineers express column operations,…
Read articleDatabricks Data Engineer Associate: Lakeflow Jobs and Orchestration
Lakeflow Jobs is Databricks’ workflow orchestration layer for coordinating notebooks, Python code, SQL, pipelines, and other tasks as dependable production workflows. Orchestration is…
Read articleDatabricks Data Engineer Associate: Incremental Data Processing with Delta Lake
Within Databricks, incremental data processing is the practice of computing what changed instead of repeatedly rebuilding everything. At small scale, a full reload…
Read articleDatabricks Data Engineer Associate: Delta Lake Transactions and the Medallion Pattern
Delta Lake transactions and the medallion pattern solve two different problems that become more valuable when they are used together. Delta Lake provides…
Read articleDatabricks Data Engineer Associate: SQL Warehouses for Analytics
Databricks SQL warehouses provide managed compute for SQL analytics, dashboards, BI tools, and ad hoc queries over lakehouse data. The architectural questions are…
Read articleDatabricks Data Engineer Associate: Lakehouse Architecture
A lakehouse architecture combines data-lake scale and openness with warehouse-style reliability, governance, and analytics. On Databricks, the architecture is not one product feature;…
Read articleDatabricks Data Engineer Associate: CI/CD for Data Engineering
CI/CD for data engineering is the practice of treating notebooks, Python packages, SQL, job definitions, pipeline resources, tests, and environment configuration as versioned…
Read articleDatabricks Data Engineer Professional: Streaming Pipelines with Structured Streaming
Apache Spark Structured Streaming brings incremental processing into the DataFrame model, allowing engineers to express many streaming transformations with familiar batch-style APIs. The…
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