DP-600 Implement analytics solutions using Microsoft Fabric

Implement analytics solutions using Microsoft Fabric -koulutuksessa käydään läpi kattavasti ne Microsoft Fabricin komponentit (Lakehouse, Data Warehouse, DataFlows, Data Pipelines, Semantic Models, Security), joiden avulla toteutetaan analytiikkaratkaisut.

Huomioithan, että Microsoft Fabricista on myös toinen koulutus DP-700 Microsoft Fabric Data Engineer, joka on osin päällekkäinen tämän koulutuksen kanssa, mutta painottuu enemmän Apache Sparkin käyttöön ja reaaliaikaisen datan käsittelyyn.

Tavoite

Opi yritystason data-analytiikkaratkaisujen toteuttaminen Microsoft Fabricilla.

Kenelle

Koulutus on suunnattu Data Analytis ja Data Engineer -asiantuntijoille, arkkitehdeille ja kaikille, joiden tulee osata Microsoft Fabric -tekniikat.

Koulutukseen osallistujalla on suositeltavaa olla Power BI osaaminen (esim. PL-300 Microsoft Power BI Data Analyst -koulutus tai vastaava) sekä osaamista SQL:n ja/tai DAX:in perusteista.

Lisätiedot

Koulutus valmentaa Microsoftin viralliseen Microsoft Certified: Fabric Analytics Engineer Associate -sertifiontitestiin.

Koulutuksen  sisältö

Explore end-to-end analytics with Microsoft Fabric

• Describe end-to-end analytics in Microsoft Fabric.
• Understand data teams and roles that use Fabric.
• Describe how to enable and use Fabric.

Get started with Lakehouses in Microsoft Fabric

• Describe core features and capabilities of lakehouses in Microsoft Fabric
• Create a lakehouse
• Ingest data into files and tables in a lakehouse
• Query lakehouse tables with SQL

Ingest Data with Dataflows Gen2 in Microsoft Fabric

• Describe Dataflow (Gen2) capabilities in Microsoft Fabric
• Create Dataflow (Gen2) solutions to ingest and transform data
• Include a Dataflow (Gen2) in a pipeline

Use Data Factory pipelines in Microsoft Fabric

• Describe pipeline capabilities in Microsoft Fabric
• Use the Copy Data activity in a pipeline
• Create pipelines based on predefined templates
• Run and monitor pipelines

Use Apache Spark in Microsoft Fabric

• Prepare to use Apache Spark
• Work with Data using Spark notebooks
• Visualize data in Spark notebook

Get started with Data Warehouses in Microsoft Fabric

• Describe data warehouses in Fabric
• Understand a data warehouse vs a data Lakehouse
• Work with data warehouses in Fabric
• Create and manage datasets within a data warehouse

Load data into a Microsoft Fabric data warehouse

• Learn different strategies to load data into a data warehouse in Microsoft Fabric.
• Learn how to build a data pipeline to load a warehouse in Microsoft Fabric.
• Learn how to load data in a warehouse using T-SQL.
• Learn how to load and transform data with dataflow (Gen 2).

Query a data warehouse in Microsoft Fabric

• Use SQL query editor to query a data warehouse.
• Explore how visual query editor works.
• Learn how to connect and query a data warehouse using SQL Server Management Studio.

Monitor a Microsoft Fabric data warehouse

• Monitor capacity unit usage with the Microsoft Fabric Capacity Metrics app.
• Monitor current activity in the data warehouse with dynamic management views.
• Monitor querying trends with query insights views.

Secure a Microsoft Fabric data warehouse

• Dynamic data Masking.
• Row level security
• Column level security.

Add measures to Power BI semantic models

• Understand implicit and explicit measures.
• Create measures, calculated columns, and calculated tables.
• Identify when to use a measure or calculated column

Design scalable semantic models

• Choose appropriate storage modes for your semantic model.
• Enable large semantic model storage format and incremental refresh.
• Create relationships between tables in a semantic model.
• Design dynamic elements to extend calculations in a semantic model.

Optimize a model for performance in Power BI

• Review the performance of measures, relationships, and visuals.
• Improve performance by reducing cardinality levels.
• Optimize DirectQuery models with table level storage.
• Create and manage aggregations.

Create and manage Power BI assets

• Create core and specialized semantic models.
• Create Power BI Template and Power BI Project files.
• Use lineage view and endorse data assets in Power BI service.
• Use XMLA endpoint to connect semantic models.

Enforce Power BI model security

• Restrict access to Power BI model data with RLS.
• Restrict access to Power BI model objects with OLS.
• Apply good development practices to enforce Power BI model security.

Get started with Real-Time Intelligence with Microsoft Fabric

• Use basic syntax and of Kusto Query Language (KQL) and querysets.
• Describe how to execute T-SQL queries in the Queryset canvas.
• Copy, export, and share data results.

Secure data access in Microsoft Fabric

• Understand Fabric security model
• Configure workspace and item permissions
• Apply granular permissions.

Administer Microsoft Fabric

• Describe Fabric admin tasks
• Navigate the admin center
• Manage user access.

Avainsanat

Microsoft Fabric, Data Analytics, Power BI, Lakehouse, Dataflow, Data Factory, Data Warehouse