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08:30 - 17:00

Perform Big Data Engineering on Microsoft Cloud Services 20776 (70-776)

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Kursusinfo

  • Dette kursus varer 4 dage
  • Der medfølger kursusmateriale til dette kursus
  • Dette kursus koster 5 klip på dit klippekort.
  • Fuld forplejning (Morgenmad, frokost, kage, kaffe og sodavand ad libitum)
  • Fuld beståelsesgaranti er inkluderet i prisen - Læs mere her
  • Eksamen er inkluderet i prisen
  • Med i prisen hører Practice Test
  • Du har i alt adgang til din kursus-pc i 3 uger

Varighed

Dette kursus varer 4 dage

Materialer

Der medfølger kursusmateriale til dette kursus

Klip på klippekort

Dette kursus koster 5 klip på dit klippekort.

Forplejning

Fuld forplejning (Morgenmad, frokost, eftermiddagskage samt kaffe og sodavand ad libitum)

Garanti for beståelse

Fuld beståelsesgaranti er inkluderet i prisen - Læs mere her

Eksamen

Alle eksamensforsøg er inkluderet i prisen

Practice Tests

Med i prisen hører Practice Test

Remote adgang

Du har i alt adgang til din kursus-pc i 3 uger

Microsoft Vouchers

5 vouchers + 4.950 kr. for Certificeringspakken.
Læs mere her

Dette er kurset for dig der gerne vil have en indføring ind I datawarehouse og analytics på Microsft Azure platformen. Her kommer vi bl.a. igennem design og implementering af Azure Stream Analytics, med et kig på streaming pipelines og Azure Stream Analytics query language, samt Azure Data Lake Store og U-SQL. Vi ser på hvordan vi med hjælp af Azure SQL Data Warehouse kan lave et parallel datawarehouse i Azure og optimerer dette for skew og sikre den bedst mulige performance på denne meget kraftige platform. Platformene er i sig selv ikke interessante uden data, så vi kommer selvfølgelig også igennem hvordan vi bedst får data ud og ind og mellem alle disse platforme.

Nedenstående er beskrivelsen af hvilket emner det testes til eksamen, da den endelige kursusbeskrivelse fra Microsoft endnu ikke er frigivet. Denne forventes at blive frigivet inden udgangen af september måned 2017. 

Design and Implement Complex Event Processing By Using Azure Stream Analytics (15-20%)
Ingest data for real-time processing
- Select appropriate data ingestion technology based on specific constraints; design partitioning scheme and select mechanism for partitioning; ingest and process data from a Twitter stream; connect to stream processing entities; estimate throughput, latency needs, and job footprint; design reference data streams
Design and implement Azure Stream Analytics
- Configure thresholds, use the Azure Machine Learning UDF, create alerts based on conditions, use a machine learning model for scoring, train a model for continuous learning, use common stream processing scenarios
Implement and manage the streaming pipeline
- Stream data to a live dashboard, archive data as a storage artifact for batch processing, enable consistency between stream processing and batch processing logic
Query real-time data by using the Azure Stream Analytics query language
- Use built-in functions, use data types, identify query language elements, control query windowing by using Time Management, guarantee event delivery

Design and Implement Analytics by Using Azure Data Lake (25-30%)
Ingest data into Azure Data Lake Store
- Create an Azure Data Lake Store (ADLS) account, copy data to ADLS, secure data within ADLS by using access control, leverage end-user or service-to-service authentication appropriately, tune the performance of ADLS, access diagnostic logs
Manage Azure Data Lake Analytics
- Create an Azure Data Lake Analytics (ADLA) account, manage users, manage data sources, manage, monitor, and troubleshoot jobs, access diagnostic logs, optimize jobs by using the vertex view, identify historical job information
Extract and transform data by using U-SQL
- Schematize data on read at scale; generate outputter files; use the U-SQL data types, use C# and U-SQL expression language; identify major differences between T-SQL and U-SQL; perform JOINS, PIVOT, UNPIVOT, CROSS APPLY, and Windowing functions in U-SQL; share data and code through U-SQL catalog; define benefits and use of structured data in U-SQL; manage and secure the Catalog
Extend U-SQL programmability
- Use user-defined functions, aggregators, and operators, scale out user-defined operators, call Python, R, and Cognitive capabilities, use U-SQL user-defined types, perform federated queries, share data and code across ADLA and ADLS
Integrate Azure Data Lake Analytics with other services
- Integrate with Azure Data Factory, Azure HDInsight, Azure Data Catalog, and Azure Event Hubs, ingest data from Azure SQL Data Warehouse

Design and Implement Azure SQL Data Warehouse Solutions (15-20%)
Design tables in Azure SQL Data Warehouse
- Choose the optimal type of distribution column to optimize workflows, select a table geometry, limit data skew and process skew through the appropriate selection of distributed columns, design columnstore indexes, identify when to scale compute nodes, calculate the number of distributions for a given workload
Query data in Azure SQL Data Warehouse
- Implement query labels, aggregate functions, create and manage statistics in distributed tables, monitor user queries to identify performance issues, change a user resource class
Integrate Azure SQL Data Warehouse with other services
- Ingest data into Azure SQL Data Warehouse by using AZCopy, Polybase, Bulk Copy Program (BCP), Azure Data Factory, SQL Server Integration Services (SSIS), Create-Table-As-Select (CTAS), and Create-External-Table-As-Select (CETAS); export data from Azure SQL Data Warehouse; provide connection information to access Azure SQL Data Warehouse from Azure Machine Learning; leverage Polybase to access a different distributed store; migrate data to Azure SQL Data Warehouse; select the appropriate ingestion method based on business needs

Design and Implement Cloud-Based Integration by using Azure Data Factory (15-20%)
Implement datasets and linked services
- Implement availability for the slice, create dataset policies, configure the appropriate linked service based on the activity and the dataset
Move, transform, and analyze data by using Azure Data Factory activities
- Copy data between on-premises and the cloud, create different activity types, extend the data factory by using custom processing steps, move data to and from Azure SQL Data Warehouse
Orchestrate data processing by using Azure Data Factory pipelines
- Identify data dependencies and chain multiple activities, model schedules based on data dependencies, provision and run data pipelines, design a data flow
Monitor and manage Azure Data Factory
- Identify failures and root causes, create alerts for specified conditions, perform a redeploy, use the Microsoft Azure Portal monitoring tool

Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics (20-25%)
Provision Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics
- Provision Azure SQL Data Warehouse, Azure Data Lake, and Azure Data Factory, implement Azure Stream Analytics
Implement authentication, authorization, and auditing
- Integrate services with Azure Active Directory (Azure AD), use the local security model in Azure SQL Data Warehouse, configure firewalls, implement auditing, integrate services with Azure Data Factory
Manage data recovery for Azure SQL Data Warehouse, Azure Data Lake, and Azure Data Factory, Azure Stream Analytics
- Backup and recover services, plan and implement geo-redundancy for Azure Storage, migrate from an on-premises data warehouse to Azure SQL Data Warehouse
Monitor Azure SQL Data Warehouse, Azure Data Lake, and Azure Stream Analytics
- Manage concurrency, manage elastic scale for Azure SQL Data Warehouse, monitor workloads by using Dynamic Management Views (DMVs) for Azure SQL Data Warehouse, troubleshoot Azure Data Lake performance by using the Vertex Execution View
Design and implement storage solutions for big data implementations
- Optimize storage to meet performance needs, select appropriate storage types based on business requirements, use AZCopy, Storage Explorer and Redgate Azure Explorer to migrate data, design cloud solutions that integrate with on-premises data

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