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Data Engineering on Microsoft Azure [DP-203T00]

Teknologisk Institut
Startdatoer
Taastrup
27-09-2021  
20.999 DKK
27-10-2021  
20.999 DKK
Århus
17-11-2021  
20.999 DKK

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+110 års erfaring med kurser og uddannelse

+17.000 kursusdeltagere om året

4,4 ud af 5 stjerner i kundetilfredshed

Kursusbeskrivelse

Lær hvordan du designer, udvikler og sikre dataløsninger i Azure og få en grundlæggende viden om de teknikker, der bruges til processering og lagring. Du lærer også om extract, transform og load af data ved hjælp af Apache Spark funktionen, der findes i Azure Synapse Analytics, Azure Databricks, Azure Data Factory eller i Azure Synapse pipelines.

Kursets varighed har vi sat til 5 dage for at give mere tid til demonstration af emnerne og fordybelse i øvelserne.

Forudsætninger

Du forventes at have et grundlæggende kendskab til arbejdet med datamodellering samt have deltaget på DP-900 Microsoft Azure Data Fundamentals eller have tilsvarende viden.

Deltagerprofil

Kurset er for dig, som arbejder med data og datamodellering, og som gerne vil lære at designe og udvikle analytiske dataløsninger på Microsoft Azure Data platform.

Udbytte

  • Udforske indstillinger for processering og lagring af data engineering workloads i Azure
  • Overvejelser i forbindelse med dataudvikling
  • Design og implementer “the serving layer”
  • Køre interaktive forespørgsler ved hjælp af serverløse SQL-grupper
  • Extract, transform og load ved hjælp af Apache Spark
  • Udføre Data Exploration og -Transformation i Azure Databricks
  • Importere og indlæse data i datalageret
  • Transformer data med Azure Data Factory- eller Azure Synapse-pipelines
  • Integrere data fra Notebooks med Azure Data Factory eller Azure Synapse Pipelines
  • Optimere Query performance med dedikerede SQL-grupper i Azure Synapse
  • Analysere og optimere data warehouse storage
  • Understøttelse af hybrid transaktionsanalysebehandling (HTAP) med Azure Synapse Link
  • Udføre end-to-end-sikkerhed med Azure Synapse Analytics
  • Udfør Stream Processing i realtid med Stream Analytics
  • Oprette en Stream Process Solution med Event hubs og Azure Databricks
  • Oprette rapporter ved hjælp af Power BI-integration med Azure Synpase Analytics
  • Udføre integrerede machine learning processer i Azure Synapse Analytics

Indhold

Modul 1: Explore compute and storage options for data engineering workloads
  • This module provides an overview of the Azure compute and storage technology options that are available to data engineers building analytical workloads. This module teaches ways to structure the data lake, and to optimize the files for exploration, streaming, and batch workloads. The student will learn how to organize the data lake into levels of data refinement as they transform files through batch and stream processing. Then they will learn how to create indexes on their datasets, such as CSV, JSON, and Parquet files, and use them for potential query and workload acceleration.

    • Introduction to Azure Synapse Analytics
    • Describe Azure Databricks
    • Introduction to Azure Data Lake storage
    • Describe Delta Lake architecture
    • Work with data streams by using Azure Stream Analytics
Modul 2: Design and implement the serving layer
  • This module teaches how to design and implement data stores in a modern data warehouse to optimize analytical workloads. The student will learn how to design a multidimensional schema to store fact and dimension data. Then the student will learn how to populate slowly changing dimensions through incremental data loading from Azure Data Factory.

    • Design a multidimensional schema to optimize analytical workloads
    • Code-free transformation at scale with Azure Data Factory
    • Populate slowly changing dimensions in Azure Synapse Analytics pipelines
Modul 3: Data engineering considerations for source files
  • This module explores data engineering considerations that are common when loading data into a modern data warehouse analytical from files stored in an Azure Data Lake, and understanding the security consideration associated with storing files stored in the data lake.

    • Design a Modern Data Warehouse using Azure Synapse Analytics
    • Secure a data warehouse in Azure Synapse Analytics
Modul 4: Run interactive queries using Azure Synapse Analytics serverless SQL pools
  • In this module, students will learn how to work with files stored in the data lake and external file sources, through T-SQL statements executed by a serverless SQL pool in Azure Synapse Analytics. Students will query Parquet files stored in a data lake, as well as CSV files stored in an external data store. Next, they will create Azure Active Directory security groups and enforce access to files in the data lake through Role-Based Access Control (RBAC) and Access Control Lists (ACLs).

    • Explore Azure Synapse serverless SQL pools capabilities
    • Query data in the lake using Azure Synapse serverless SQL pools
    • Create metadata objects in Azure Synapse serverless SQL pools
    • Secure data and manage users in Azure Synapse serverless SQL pools
    • Configure data lake security using Role-Based Access Control (RBAC) and Access Control List
Modul 5: Explore, transform, and load data into the Data Warehouse using Apache Spark
  • This module teaches how to explore data stored in a data lake, transform the data, and load data into a relational data store. The student will explore Parquet and JSON files and use techniques to query and transform JSON files with hierarchical structures. Then the student will use Apache Spark to load data into the data warehouse and join Parquet data in the data lake with data in the dedicated SQL pool.

    • Understand big data engineering with Apache Spark in Azure Synapse Analytics
    • Ingest data with Apache Spark notebooks in Azure Synapse Analytics
    • Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics
    • Integrate SQL and Apache Spark pools in Azure Synapse Analytics
Modul 6: Data exploration and transformation in Azure Databricks
  • This module teaches how to use various Apache Spark DataFrame methods to explore and transform data in Azure Databricks. The student will learn how to perform standard DataFrame methods to explore and transform data. They will also learn how to perform more advanced tasks, such as removing duplicate data, manipulate date/time values, rename columns, and aggregate data.

    • Describe Azure Databricks
    • Read and write data in Azure Databricks
    • Work with DataFrames in Azure Databricks
    • Work with DataFrames advanced methods in Azure Databricks
Modul 7: Ingest and load data into the data warehouse
  • This module teaches students how to ingest data into the data warehouse through T-SQL scripts and Synapse Analytics integration pipelines. The student will learn how to load data into Synapse dedicated SQL pools with PolyBase and COPY using T-SQL. The student will also learn how to use workload management along with a Copy activity in a Azure Synapse pipeline for petabyte-scale data ingestion.

    • Use data loading best practices in Azure Synapse Analytics
    • Petabyte-scale ingestion with Azure Data Factory
Modul 8: Transform data with Azure Data Factory or Azure Synapse Pipelines
  • This module teaches students how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flowss, and perform data movement into one or more data sinks.

    • Data integration with Azure Data Factory or Azure Synapse Pipelines
    • Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines
Modul 9: Orchestrate data movement and transformation in Azure Synapse Pipelines
  • In this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines.

    • Orchestrate data movement and transformation in Azure Data Factory
Modul 10: Optimize query performance with dedicated SQL pools in Azure Synapse
  • In this module, students will learn strategies to optimize data storage and processing when using dedicated SQL pools in Azure Synapse Analytics. The student will know how to use developer features, such as windowing and HyperLogLog functions, use data loading best practices, and optimize and improve query performance.

    • Optimize data warehouse query performance in Azure Synapse Analytics
    • Understand data warehouse developer features of Azure Synapse Analytics
Modul 11: Analyze and Optimize Data Warehouse Storage
  • In this module, students will learn how to analyze then optimize the data storage of the Azure Synapse dedicated SQL pools. The student will know techniques to understand table space usage and column store storage details. Next the student will know how to compare storage requirements between identical tables that use different data types. Finally, the student will observe the impact materialized views have when executed in place of complex queries and learn how to avoid extensive logging by optimizing delete operations.

    • Analyze and optimize data warehouse storage in Azure Synapse Analytics
Modul 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
  • In this module, students will learn how Azure Synapse Link enables seamless connectivity of an Azure Cosmos DB account to a Synapse workspace. The student will understand how to enable and configure Synapse link, then how to query the Azure Cosmos DB analytical store using Apache Spark and SQL serverless.

    • Design hybrid transactional and analytical processing using Azure Synapse Analytics
    • Configure Azure Synapse Link with Azure Cosmos DB
    • Query Azure Cosmos DB with Apache Spark pools
    • Query Azure Cosmos DB with serverless SQL pools
Modul 13: End-to-end security with Azure Synapse Analytics
  • In this module, students will learn how to secure a Synapse Analytics workspace and its supporting infrastructure. The student will observe the SQL Active Directory Admin, manage IP firewall rules, manage secrets with Azure Key Vault and access those secrets through a Key Vault linked service and pipeline activities. The student will understand how to implement column-level security, row-level security, and dynamic data masking when using dedicated SQL pools.

    • Secure a data warehouse in Azure Synapse Analytics
    • Configure and manage secrets in Azure Key Vault
    • Implement compliance controls for sensitive data
Modul 14: Real-time Stream Processing with Stream Analytics
  • In this module, students will learn how to process streaming data with Azure Stream Analytics. The student will ingest vehicle telemetry data into Event Hubs, then process that data in real time, using various windowing functions in Azure Stream Analytics. They will output the data to Azure Synapse Analytics. Finally, the student will learn how to scale the Stream Analytics job to increase throughput.

    • Enable reliable messaging for Big Data applications using Azure Event Hubs
    • Work with data streams by using Azure Stream Analytics
    • Ingest data streams with Azure Stream Analytics

Modul 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks
  • In this module, students will learn how to ingest and process streaming data at scale with Event Hubs and Spark Structured Streaming in Azure Databricks. The student will learn the key features and uses of Structured Streaming. The student will implement sliding windows to aggregate over chunks of data and apply watermarking to remove stale data. Finally, the student will connect to Event Hubs to read and write streams.

    • Process streaming data with Azure Databricks structured streaming
Modul 16: Build reports using Power BI integration with Azure Synpase Analytics
  • In this module, the student will learn how to integrate Power BI with their Synapse workspace to build reports in Power BI. The student will create a new data source and Power BI report in Synapse Studio. Then the student will learn how to improve query performance with materialized views and result-set caching. Finally, the student will explore the data lake with serverless SQL pools and create visualizations against that data in Power BI.

    • Create reports with Power BI using its integration with Azure Synapse Analytics
Modul 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics
  • This module explores the integrated, end-to-end Azure Machine Learning and Azure Cognitive Services experience in Azure Synapse Analytics. You will learn how to connect an Azure Synapse Analytics workspace to an Azure Machine Learning workspace using a Linked Service and then trigger an Automated ML experiment that uses data from a Spark table. You will also learn how to use trained models from Azure Machine Learning or Azure Cognitive Services to enrich data in a SQL pool table and then serve prediction results using Power BI.

    • Use the integrated machine learning process in Azure Synapse Analytics

Certificering

Dette kursus leder hen mod eksamen DP-203 Data Engineering on Microsoft Azure. Eksamen bestilles og betales særskilt. Ved beståelse opnås certificeringen Micrsosoft Certified Data Engineer, Associate.

Microsoft skriver om denne eksamen
  • A candidate for the Azure Data Engineer Associate certification should have subject matter expertise integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions.
  • Responsibilities for this role include helping stakeholders understand the data through exploration, building and maintaining secure and compliant data processing pipelines by using different tools and techniques. This professional uses various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis.
  • An Azure Data Engineer also helps ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a specific set of business requirements and constraints. This professional deals with unanticipated issues swiftly and minimizes data loss. An Azure Data Engineer also designs, implements, monitors, and optimizes data platforms to meet the data pipeline needs.
  • A candidate for this certification must have solid knowledge of data processing languages, such as SQL, Python, or Scala, and they need to understand parallel processing and data architecture patterns.

Læs mere om IT-certificering her.

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Pris

20999 DKK

Om udbyderen

+1000 kurser inden for bl.a. IT, projektledelse, ledelse, kommunikation, personlig udvikling mm. Teknologisk Institut har leveret uddannelse til det danske erhvervsliv siden 1906, og har derfor over 110 års erfaring med kurser og uddannelser. Hvert år deltager flere end 17.000...


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Kontaktinfo

Teknologisk Institut

Gregersensvej 3
2630 Taastrup


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