EDITORIAL TOPIC / EXAMTOPICS EDITORIAL
Artificial intelligence, machine learning, data systems and analytics.
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 articleCI/CD for data engineering is the practice of treating notebooks, Python packages, SQL, job definitions, pipeline resources, tests, and environment configuration as versioned…
Read articleApache Spark Structured Streaming brings incremental processing into the DataFrame model, allowing engineers to express many streaming transformations with familiar batch-style APIs. The…
Read articleLarge Spark workloads become slow for many different reasons: unnecessary data scans, poor join strategy, skew, shuffle, small files, oversized partitions, driver overload,…
Read articleData observability answers a different question from infrastructure monitoring. Instead of asking only whether compute is running, it asks whether data arrived, changed…
Read articleProduction data pipelines are judged by repeatability, recovery, observability, security, and cost—not by whether a notebook succeeded once with sample data. Databricks provides…
Read articleChange data capture is the point where a lakehouse stops behaving like a sequence of full reloads and starts behaving like a continuously…
Read articleA generative AI strategy is not a list of models to buy. It is a business plan for choosing valuable use cases, matching…
Read articleA Power BI semantic model is the layer that turns stored data into consistent business meaning. It defines relationships, measures, hierarchies, security, formatting,…
Read articleRow-level security controls which records a user can see without requiring a separate report or dataset for every audience. In Microsoft Fabric and…
Read articleOneLake gives Microsoft Fabric a unified storage foundation, but unified storage does not mean unrestricted storage. As more lakehouses, warehouses, semantic models, and…
Read articleMicrosoft Fabric capacity is the shared compute foundation behind workloads such as data engineering, warehouses, semantic models, data science, and reporting. That shared…
Read articleA Microsoft Fabric lakehouse is more than a place to put Parquet files. It is an analytical architecture that lets engineering, SQL analytics,…
Read articleMedallion architecture organizes analytical data into progressively refined layers, usually called bronze, silver, and gold. The labels are simple, but the value comes…
Read articleMicrosoft Fabric Data Factory offers several ways to move and transform data, but two components are frequently confused: pipelines and Dataflow Gen2. They…
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