Data & Analytics
Data and analytics guides covering data engineering, lakehouse systems, analytics platforms, semantic models, business intelligence, pipelines, data governance, SQL, and production data operations.
275 Data & Analytics guides4 editorial pillar paths
Data & Analytics Editorial Pillars
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Data & AI on Google CloudBigQuery, Dataflow, Dataproc, Vertex AI, governance, data quality, and production Google Cloud data systems.Databricks Lakehouse EngineeringDelta Lake, Lakeflow, PySpark, performance, governance, CI/CD, and production data engineering.Microsoft Data Platform EngineeringMicrosoft Fabric, semantic models, SQL, pipelines, analytics engineering, and governance.Production ML on AWSSageMaker, MLOps, feature engineering, deployment, monitoring, cost control, and responsible ML.
Latest Data & Analytics Guides
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Google Professional Machine Learning Engineer: Model Deployment Patterns
A recent Data & Analytics guide focused on model deployment patterns and its practical technology context.
Data & AnalyticsGoogle Professional Machine Learning Engineer: Model Monitoring
A recent Data & Analytics guide focused on model monitoring and its practical technology context.
Data & AnalyticsGoogle Professional Machine Learning Engineer: MLOps Pipelines on Vertex AI
A recent Data & Analytics guide focused on mlops pipelines on vertex ai and its practical technology context.
Data & AnalyticsGoogle Professional Machine Learning Engineer: Feature Management
A recent Data & Analytics guide focused on feature management and its practical technology context.
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