EDITORIAL TOPIC / EXAMTOPICS EDITORIAL
Artificial intelligence, machine learning, data systems and analytics.
Machine learning development is iterative. Engineers change training data, features, parameters, algorithms, code, and environments, and each choice can affect performance. MLflow provides…
Read articleBatch and real-time inference solve different operational problems. Batch scoring processes many records on a schedule or as a dataset operation, while real-time…
Read articleEnterprise AI applications often need to find information by meaning rather than exact keywords. Vector retrieval represents text, images, or other content as…
Read articlePutting a generative AI model behind an endpoint is only the start of production operations. A reliable serving design must control identity, model…
Read articleGenerative AI evaluation is difficult because many important qualities are not captured by one accuracy number. A response can be fluent but unsupported,…
Read articleGenerative AI governance must cover more than model access. Production applications depend on prompts, evaluation datasets, vector indexes, tools, functions, model endpoints, traces,…
Read articleRetrieval-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 articleUnity Catalog is the governance layer that connects identity, permissions, lineage, auditing, discovery, and policy across data and AI assets in Databricks. The…
Read articleA slow Spark job is rarely fixed by immediately choosing a larger cluster. Performance problems usually come from a specific stage: unnecessary data…
Read articleSchema change is normal in data engineering. Sources add fields, rename concepts, change optionality, widen numeric ranges, and occasionally send records that do…
Read articlePySpark DataFrames are one of the main programming interfaces for transforming structured and semi-structured data on Databricks. They let engineers express column operations,…
Read articleLakeflow Jobs is Databricks’ workflow orchestration layer for coordinating notebooks, Python code, SQL, pipelines, and other tasks as dependable production workflows. Orchestration is…
Read articleWithin Databricks, incremental data processing is the practice of computing what changed instead of repeatedly rebuilding everything. At small scale, a full reload…
Read articleDelta Lake transactions and the medallion pattern solve two different problems that become more valuable when they are used together. Delta Lake provides…
Read articleDatabricks SQL warehouses provide managed compute for SQL analytics, dashboards, BI tools, and ad hoc queries over lakehouse data. The architectural questions are…
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