Datum
09/12/2024 - 12/12/2024
Ganztägig
Kalender-Import: iCal
Veranstaltungsort
ETC Trainingscenter
Modecenterstraße 22, 1030 - Wien
Die Kenntnisse zu Cloud Computing und Kerndatenkonzepten sowie Berufserfahrung mit Datenlösungen sind notwendig
In diesem Training erfahren die Trainingsteilnehmer*innen, wie sie Datentechnikworkloads in Microsoft Azure implementieren und verwalten, indem sie unter anderem Azure-Dienste wie Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks verwenden. Der Training fokussiert sich auf allgemeine Datentechnikaufgaben wie das Orchestrieren von Datenübertragungs- und Transformationspipelines, das Arbeiten mit Datendateien in einem Data Lake, das Erstellen und Laden relationaler Data Warehouses, das Erfassen und Aggregieren von Datenströmen in Echtzeit sowie das Nachverfolgen von Datenressourcen und -herkunft.
Die Hauptzielgruppe für diesen Training sind Datenexpert*innen, Datenarchitekt*innen und Business Intelligence-Expert*innen, die sich über Datentechnik und das Erstellen von Analyselösungen mithilfe der Datenplattformtechnologien in Microsoft Azure informieren möchten.
Die sekundäre Zielgruppe für diesen Training umfasst Data Analyst*innen und wissenschaftliche Fachkräfte für Daten, die auf Microsoft Azure basierende Analyselösungen nutzen.
Introduction to data engineering on Azure
– What is data engineering
– Important data engineering concepts
– Data engineering in Microsoft Azure
Introduction to Azure Data Lake Storage Gen2
– Understand Azure Data Lake Storage Gen
– Enable Azure Data Lake Storage Gen2 in Azure Storage
– Compare Azure Data Lake Store to Azure Blob storage
– Understand the stages for processing big data
– Use Azure Data Lake Storage Gen2 in data analytics workloads
Introduction to Azure Synapse Analytics
– What is Azure Synapse Analytics
– How Azure Synapse Analytics works
– When to use Azure Synapse Analytics
Use Azure Synapse serverless SQL pool to query files in a data lake
– Understand Azure Synapse serverless SQL pool capabilities and use cases
– Query files using a serverless SQL pool
– Create external database objects
Use Azure Synapse serverless SQL pools to transform data in a data lake
– Transform data files with the CREATE EXTERNAL TABLE AS SELECT statement
– Encapsulate data transformations in a stored procedure
– Include a data transformation stored procedure in a pipeline
Create a lake database in Azure Synapse Analytics
– Understand lake database concepts
– Explore database templates
– Create a lake database
– Use a lake database
Analyze data with Apache Spark in Azure Synapse Analytics
– Get to know Apache Spark
– Use Spark in Azure Synapse Analytics
– Analyze data with Spark
– Visualize data with Spark
Transform data with Spark in Azure Synapse Analytics
– Modify and save dataframes
– Partition data files
– Transform data with SQL
Use Delta Lake in Azure Synapse Analytics
– Understand Delta Lake
– Create Delta Lake tables
– Create catalog tables
– Use Delta Lake with streaming data
– Use Delta Lake in a SQL pool
Analyze data in a relational data warehouse
Design a data warehouse schema
Create data warehouse tables
Load data warehouse tables
Query a data warehouse
Load data into a relational data warehouse
– Load staging tables
– Load dimension tables
– Load time dimension tables
– Load slowly changing dimensions
– Load fact tables
– Perform post load optimization
Build a data pipeline in Azure Synapse Analytics
– Understand pipelines in Azure Synapse Analytics
– Create a pipeline in Azure Synapse Studio
– Define data flows
– Run a pipeline
Use Spark Notebooks in an Azure Synapse Pipeline
– Understand Synapse Notebooks and Pipelines
– Use a Synapse notebook activity in a pipeline
– Use parameters in a notebook
Plan hybrid transactional and analytical processing using Azure Synapse Analytics
– Understand hybrid transactional and analytical processing patterns
– Describe Azure Synapse Link
Implement Azure Synapse Link with Azure Cosmos DB
– Enable Cosmos DB account to use Azure Synapse Link
– Create an analytical store enabled container
– Create a linked service for Cosmos DB
– Query Cosmos DB data with Spark
– Query Cosmos DB with Synapse SQL
Implement Azure Synapse Link for SQL
– What is Azure Synapse Link for SQL?
– Configure Azure Synapse Link for Azure SQL Database
– Configure Azure Synapse Link for SQL Server 2022
Get started with Azure Stream Analytics
– Understand data streams
– Understand event processing
– Understand window functions
Ingest streaming data using Azure Stream Analytics and Azure Synapse Analytics
– Stream ingestion scenarios
– Configure inputs and outputs
– Define a query to select, filter, and aggregate data
– Run a job to ingest data
Visualize real-time data with Azure Stream Analytics and Power BI
– Use a Power BI output in Azure Stream Analytics
– Create a query for real-time visualization
– Create real-time data visualizations in Power BI
Introduction to Microsoft Purview
– What is Microsoft Purview?
– How Microsoft Purview works
– When to use Microsoft Purview
Integrate Microsoft Purview and Azure Synapse Analytics
– Catalog Azure Synapse Analytics data assets in Microsoft Purview
– Connect Microsoft Purview to an Azure Synapse Analytics workspace
– Search a Purview catalog in Synapse Studio
– Track data lineage in pipelines
Explore Azure Databricks
– Get started with Azure Databricks
– Identify Azure Databricks workloads
– Understand key concepts
Use Apache Spark in Azure Databricks
– Get to know Spark
– Create a Spark cluster
– Use Spark in notebooks
– Use Spark to work with data files
– Visualize data
Run Azure Databricks Notebooks with Azure Data Factory
– Understand Azure Databricks notebooks and pipelines
– Create a linked service for Azure Databricks
– Use a Notebook activity in a pipeline
– Use parameters in a notebook
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