From the course: Microsoft Azure Data Engineer Associate (DP-203) Cert Prep by Microsoft Press
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Learning objectives - Azure Tutorial
From the course: Microsoft Azure Data Engineer Associate (DP-203) Cert Prep by Microsoft Press
Learning objectives
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- Welcome to Lesson 3, Design a Partition Strategy. You learn four data partitioning techniques in this lesson. You begin by partitioning files. You then partition analytical workloads. Next, you design a partition strategy for efficiency and performance. You work with Azure Synapse Analytics in a partitioning context. And then wrap up by identifying when partitioning is needed in Azure Data Lake Storage Gen2. We have a lot of ground to cover. Let's get to it.
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Contents
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Learning objectives36s
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Design a partition strategy for files3m 58s
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Design a partition strategy for analytical workloads1m 57s
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Design a partition strategy for efficiency and performance2m 56s
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Design a partition strategy for Azure Synapse Analytics2m 2s
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Identify when partitioning is needed in Azure Data Lake Storage Gen223m 46s
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Learning objectives40s
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Design star schemas3m 13s
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Design slowly changing dimensions2m 23s
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Design a dimensional hierarchy28s
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Design a solution for temporal data1m 16s
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Design for incremental loading51s
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Design analytical stores2m 18s
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Design metastores in Azure Synapse Analytics and Azure Databricks24m 10s
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Learning objectives29s
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Implement compression1m 42s
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Implement partitioning42s
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Implement sharding18s
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Implement different table geometries with Azure Synapse Analytics pools2m 3s
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Implement data redundancy3m 55s
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Implement distributions27s
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Implement data archiving13m 19s
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Learning objectives31s
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Transform data by using Apache Spark2m 29s
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Transform data by using Transact-SQL1m 5s
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Transform data by using Data Factory1m 35s
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Transform data by using Azure Synapse pipelines1m 24s
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Transform data by using Stream Analytics19m 50s
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Learning objectives46s
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Develop batch processing solutions by using Data Factory, Data Lake, Spark, Azure Synapse pipelines, PolyBase, and Azure Databricks1m 14s
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Create data pipelines2m
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Design and implement incremental data loads1m 20s
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Design and develop slowly changing dimensions36s
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Handle security and compliance requirements2m 35s
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Scale resources21m 11s
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Learning objectives37s
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Configure the batch size2m 26s
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Design and create tests for data pipelines3m 31s
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Integrate Jupyter and Python Notebooks into a data pipeline1m 15s
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Handle duplicate data23s
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Handle missing data36s
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Handle late-arriving data7m 39s
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Learning objectives39s
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Upsert data1m 52s
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Regress to a previous state2m 14s
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Design and configure exception handling1m 44s
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Configure batch retention1m 2s
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Revisit batch processing solution design1m 16s
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Debug Spark jobs by using the Spark UI24m 55s
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Learning objective46s
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Develop a stream processing solution by using Stream Analytics, Azure Databricks, and Azure Event Hubs1m 53s
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Process data by using Spark structured streaming1m 52s
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Monitor for performance and functional regressions1m 34s
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Design and create windowed aggregates1m 50s
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Handle schema drift21m 50s
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Learning objectives47s
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Process time series data1m 53s
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Process across partitions2m 9s
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Process within one partition1m
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Configure checkpoints and watermarking during processing1m 2s
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Scale resources1m 49s
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Design and create tests for data pipelines1m 20s
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Optimize pipelines for analytical or transactional purposes15m 26s
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Learning objectives34s
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Trigger batches1m 53s
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Handle failed batch loads1m 50s
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Validate batch loads45s
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Manage data pipelines in Data Factory and Synapse pipelines1m 16s
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Schedule data pipelines in Data Factory and Synapse pipelines22s
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Implement version control for pipeline artifacts56s
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Manage Spark jobs in a pipeline12m 1s
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Learning objectives40s
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Implement data masking1m 49s
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Encrypt data at rest and in motion1m 40s
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Implement row-level and column-level security18s
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Implement Azure RBAC1m 31s
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Implement POSIX-like ACLs for Data Lake Storage Gen254s
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Implement a data retention policy21s
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Implement a data auditing strategy15m 28s
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Learning objectives40s
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Manage identities, keys, and secrets across different data platforms2m 20s
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Implement secure endpoints: Private and public1m 38s
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Implement resource tokens in Azure Databricks1m 34s
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Load a DataFrame with sensitive information54s
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Write encrypted data to tables or Parquet files34s
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Manage sensitive information16m 51s
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Learning objectives30s
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Implement logging used by Azure Monitor1m 9s
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Configure monitoring services1m 15s
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Measure performance of data movement57s
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Monitor and update statistics about data across a system1m 12s
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Monitor data pipeline performance13s
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Measure query performance10m 15s
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Learning objectives32s
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Compact small files1m 9s
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Rewrite user-defined functions (UDFs)1m 26s
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Handle skew in data1m 50s
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Handle data spill1m 29s
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Tune shuffle partitions1m 7s
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Find shuffling in a pipeline21s
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Optimize resource management12m
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Learning objectives31s
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Tune queries by using indexers1m 53s
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Tune queries by using cache55s
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Optimize pipelines for analytical or transactional purposes1m 38s
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Optimize pipeline for descriptive versus analytical workloads1m 28s
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Troubleshoot failed Spark jobs30s
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Troubleshoot failed pipeline runs1m 14s
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