Technical requirementsDesigning a batch processing solutionDeveloping batch processing solutions by using Data Factory, Data Lake, Spark, Azure Synapse Pipelines, PolyBase, and Azure DatabricksStorageData ingestionData preparation/data cleansingTransformationUsing PolyBase to ingest the data into the Analytics data storeUsing Power BI to display the insightsCreating data pipelines Integrating Jupyter/Python notebooks into a data pipelineDesigning and implementing incremental data loadsDesigning and developing slowly changing dimensionsHandling duplicate dataHandling missing dataHandling late-arriving data Handling late-arriving data in the ingestion/transformation stageHandling late-arriving data in the serving stageUpserting dataRegressing to a previous state Introducing Azure BatchRunning a sample Azure Batch jobConfiguring the batch sizeScaling resourcesAzure BatchAzure Databricks Synapse SparkSynapse SQLConfiguring batch retentionDesigning and configuring exception handling Types of errorsRemedial actionsHandling security and compliance requirements The Azure Security BenchmarkBest practices for Azure BatchSummary