Study material for exam 70-776 Perform Big Data Engineering on Microsoft Cloud Services
By: Daniel Calbimonte | Updated: 2017-09-27 | Comments (1) | Related: More > Professional Development Certifications
I am working on passing exam Microsoft 70-776, Perform Big Data Engineering on Microsoft Cloud Services. What study materials exist for this exam?
We will talk about FAQs for certification 70-776. This certification is related to Azure Stream Analytics, Azure Data Lake, Azure SQL Data Warehouse, and Azure Data Factory.
Who should take this exam?
This exam is oriented to DBAs, Data Scientist, Data Architects, Data Analysists, Data Developers or professionals who want to learn or who want to be certified in Data Analysis. Specifically, Azure Stream Analytics, Azure Data Lake, Data Warehouse and Data Factory.
What is Azure Stream Analytics?
It is an event-process engine used to analyze streams of data. You can get data from IoT or non-IoT devices.
What is Azure Data Lake?
It is a special repository in Azure for a scalable repository for big data.
What is Azure Data Warehouse?
It is a scalable database that supports massively parallel processing designed to process Big Data.
What is Azure Data Factory?
It is a platform to provide cloud integration services.
Do I need to have an Azure subscription to study for this exam?
Yes, there are free versions, but they do require a credit card.
What Microsoft Certifications are related to this exam?
This exam is mandatory to get the MCSA in Data Engineering with Azure. You can also become a MCP (Microsoft Certified Professional) with this exam.
Is the exam difficult?
If you do not have previous experience with Azure, Data Warehouse, Data Factory and all the technologies required in the exam, it will be very difficult for you.
Which books would you recommend for this exam?
The following books may be useful:
- Mastering Azure Analytics: Architecting in the Cloud with Azure Data Lake, HDInsight, and Spark
- Pro Microsoft HDInsight: Hadoop on Windows
- Getting Started with Storm: Continuous Streaming Computation with Twitter's Cluster Technology
- Streaming Architecture: New Designs Using Apache Kafka and MapR Streams 1st Edition
- Getting started with Azure Data Factory Kindle Edition
Are there some courses for this exam?
Yes, the following courses will be useful:
- MVA Azure Data Lake courses
- MVA Data Warehouse
- MVA Azure Data Factory
- MVA Azure Stream Analytics
- MVA Microsoft Azure Directory
- edX: Data Lake
- edX: Data Factory
Can you provide some links to study, for this exam?
Yes, here are some useful links:
Design and Implement Complex Event Processing By Using Azure Stream Analytics
- Ingest data for real-time processing
- What is Stream Analytics?
- Get started with Azure Stream Analytics to process data from IoT devices
- Choosing a streaming analytics platform: comparing Apache Storm and Azure Stream Analytics
- Data connection: Learn about data stream inputs from events to Stream Analytics
- Event Processing Ordering Design Choices for Azure Stream Analytics
- Stream Analytics outputs: Options for storage, analysis
- Real-time Twitter sentiment analysis in Azure Stream Analytics
- Scale Azure Stream Analytics jobs to increase stream data processing throughput
- Optimize your job to use Streaming Units efficiently
- Using reference data or lookup tables in a Stream Analytics input stream
- Design and implement Azure Stream Analytics
- Machine Learning integration in Stream Analytics
- Streaming Events to AzureML Through Azure Stream Analytics
- Set up alerts for Azure Stream Analytics jobs
- Processing Configurable Threshold Based Rules in Azure Stream Analytics
- Create metric alerts in Azure Monitor for Azure services - Azure portal
- Performing sentiment analysis by using Azure Stream Analytics and Azure Machine Learning
- Machine Learning-based anomaly detection in Azure Stream Analytics
- Implement and manage the streaming pipeline
- Query real-time data by using the Azure Stream Analytics query language
- Stream Analytics Query Language Reference
- Query examples for common Stream Analytics usage patterns
- Deep Dive: Azure Stream Analytics Query Language
- Built-in Functions (Azure Stream Analytics)
- Data Types (Azure Stream Analytics)
- TIMESTAMP BY (Azure Stream Analytics)
- Event Delivery Guarantees (Azure Stream Analytics)
- How to achieve exactly-once delivery for SQL output
Design and Implement Analytics by Using Azure Data Lake
- Ingest data into Azure Data Lake Store
- Introduction to Azure Data Lake Store
- Get started with Azure Data Lake Store using the Azure portal
- Get started with Azure Data Lake Store using Azure PowerShell
- Get started with Azure Data Lake Store using .NET SDK
- Copy data to and from Data Lake Store by using Data Factory
- Copy data from Azure Storage Blobs to Data Lake Store
- Use Distcp to copy data between Azure Storage Blobs and Data Lake Store
- Copy data between Data Lake Store and Azure SQL database using Sqoop
- Use the Azure Import/Export service for offline copy of data to Data Lake Store
- Azure Data Lake - Security Essentials
- Securing Azure Data Lake Store
- The Intelligent Data Lake
- Tuning Azure Data Lake Store for performance
- Accessing diagnostic logs for Azure Data Lake Store
- Manage Azure Data Lake Analytics
- Overview of Microsoft Azure Data Lake Analytics
- Get started with Azure Data Lake Analytics using Azure portal
- Manage Azure Data Lake Analytics by using the Azure portal
- Manage Azure Data Lake Analytics using Azure Command-line Interface (CLI)
- Manage Azure Data Lake Analytics using Azure PowerShell
- Manage Azure Data Lake Analytics using Python
- Azure Data Lake Analytics Quota Limits
- Azure Data Lake Developer Tools
- Azure Data Lakes
- Troubleshoot Azure Data Lake Analytics jobs using Azure Portal
- Use Job Browser and Job View for Azure Data lake Analytics jobs
- Use the Vertex Execution View in Data Lake Tools for Visual Studio
- Extract and transform data by using U-SQL
- U-SQL programmability guide
- Stairway to U-SQL
- Data Types and Literals (U-SQL)
- Develop U-SQL user-defined operators (UDOs)
- Extending U-SQL Expressions with User-Code
- Introducing U-SQL – A Language that makes Big Data Processing Easy
- Stairway to U-SQL Level 8: Joining Tables in U-SQL
- Stairway to U-SQL Level 10: Table-Valued Functions and UDTs
- Stairway to U-SQL Level 16: The Azure Data Lake Catalog
- Get started with the U-SQL Catalog
- U-SQL Assemblies
- Extend U-SQL programmability
- Writing and Using Custom Code in U-SQL – User-Defined Functions
- Tutorial: Get started with extending U-SQL with Python
- Tutorial: Get started with extending U-SQL with R
- Tutorial: Get started with the Cognitive capabilities of U-SQL
- U-SQL Federated Distributed Queries (SQLBits 2016)
- Setup Azure Data Lake Analytics federated U-SQL queries to Azure SQL Database
- Integrate Azure Data Lake Analytics with other services
- Transform data by running U-SQL scripts on Azure Data Lake Analytics
- Azure Data Lake & Azure HDInsight Blog
- Creating big data pipelines using Azure Data Lake and Azure Data Factory
- Azure Data Lake now integrated with Azure Data Catalog
- Azure Data Lake and Azure Data Warehouse: Applying Modern Practices to Your App
- Directly store streaming data into Azure Data Lake with Azure Event Hubs Capture Provider
- Query Event Hubs Archive with Azure Data Lake Analytics and U-SQL
Design and Implement Azure SQL Data Warehouse Solutions
- Design tables in Azure SQL Data Warehouse
- Azure DWH part 1:Common questions about Azure SQL Data Warehouse
- Distributing tables in SQL Data Warehouse
- Overview of tables in SQL Data Warehouse
- Distributed data and distributed tables for Massively Parallel Processing (MPP)
- Clustered Columnstore Tables are the New Default in Azure SQL Data Warehouse
- Columnstore indexes - overview
- Query data in Azure SQL Data Warehouse
- Use labels to instrument queries in SQL Data Warehouse
- Aggregate Functions (Transact-SQL)
- Managing statistics on tables in SQL Data Warehouse
- Monitor user queries in Azure SQL Data Warehouse
- Monitor your workload using DMVs
- Maximizing rowgroup quality for columnstore
- Concurrency and workload management in SQL Data Warehouse
- Integrate Azure SQL Data Warehouse with other services
- Azure DWH part 15: PolyBase and Azure Data Lake
- Azure DWH part 14: PolyBase to access to Non relational data
- Azure DWH part 4: How to import data to Azure DWH using SSIS
- Load data into Azure SQL Data Warehouse
- Azure Machine Learning - Your first experiment
- Create Table As Select (CTAS) in SQL Data Warehouse
- CREATE EXTERNAL TABLE AS SELECT (Transact-SQL)
- Copy data to and from Azure SQL Data Warehouse using Azure Data Factory
- Migrate Your Data
Design and Implement Cloud-Based Integration by using Azure Data Factory
- Implement datasets and linked services.
- Move, transform, and analyze data by using Azure Data Factory activities
- Tutorial: Copy data from Blob Storage to SQL Database using Data Factory
- Copy data to and from an on-premises file system by using Azure Data Factory
- Pipelines and Activities in Azure Data Factory
- Process large-scale datasets using Data Factory and Batch
- Use Azure Data Factory with SQL Data Warehouse
- Orchestrate data processing by using Azure Data Factory pipelines
- Monitor and manage Azure Data Factory
- Monitor and manage Azure Data Factory pipelines by using the Azure portal and PowerShell
- Monitor and manage Azure Data Factory pipelines by using the Monitoring and Management app
- Troubleshoot Data Factory issues
- Troubleshoot issues with using Data Management Gateway
- Azure Data Factory - Frequently Asked Questions
Manage and Maintain Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics
- Provision Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, and Azure Stream Analytics
- Implement authentication, authorization, and auditing
- Use Azure Active Directory Authentication for authentication with SQL Database or SQL Data Warehouse
- Azure SQL Database server-level and database-level firewall rules
- Working with Azure Active Directory and Azure SQL Database
- What is Azure Active Directory?
- Secure a database in SQL Data Warehouse
- Auditing in Azure SQL Data Warehouse
- Debug Stream Analytics jobs using service and operation logs
- Troubleshooting guide for Azure Stream Analytics
- Azure Logging and Auditing
- Security in Azure Data Lake Store
- Manage data recovery for Azure SQL Data Warehouse, Azure Data Lake, and Azure Data Factory, Azure Stream Analytics
- Monitor Azure SQL Data Warehouse, Azure Data Lake, and Azure Stream Analytics
- Design and implement storage solutions for big data implementations.
Azure Data Lake, Azure Data Streaming Analytics, Azure Data Factory and Azure SQL Data Warehouse are modern and Powerful tools to handle Big Data in Azure. These new tools are helping solve the new problems in today’s world.
For more information about this exam, refer to these links:
- Exam 70-776
- Cert Exam Prep: Exam 70-776: Engineering Data with Microsoft Cloud Services
- 776: Performing Big Data Engineering on Microsoft Cloud Services Beta Exam Now Available
- New MCSA: Data Engineering with Azure Certification from Microsoft
Last Updated: 2017-09-27
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