Design and implement loading patterns

• Design and implement full and incremental data loads

• Prepare data for loading into a dimensional model

• Design and implement a loading pattern for streaming data

Ingest and transform batch data

• Choose an appropriate data store

• Choose between dataflows, notebooks, KQL, and T-SQL for data transformation

• Create and manage shortcuts to data

• Implement mirroring

• Ingest data by using pipelines

• Transform data by using PySpark, SQL, and KQL

• Denormalize data

• Group and aggregate data

• Handle duplicate, missing, and late-arriving data

Ingest and transform streaming data

• Choose an appropriate streaming engine

• Choose between native storage, followed storage, or shortcuts in Real-Time Intelligence

• Process data by using eventstreams

• Process data by using Spark structured streaming

• Process data by using KQL

• Create windowing functions

Monitor Fabric items

• Monitor data ingestion

• Monitor data transformation

• Monitor semantic model refresh

• Configure alerts

Identify and resolve errors

• Identify and resolve pipeline errors

• Identify and resolve dataflow errors

• Identify and resolve notebook errors

• Identify and resolve eventhouse errors

• Identify and resolve eventstream errors

• Identify and resolve T-SQL errors

Optimize performance

• Optimize a lakehouse table

• Optimize a pipeline

• Optimize a data warehouse

• Optimize eventstreams and eventhouses

• Optimize Spark performance

• Optimize query performance