Technical Assessment: Technical Assessment Data Platform Foundational
This module overviews SQL Server 2016 and it's mission-critical and performance capabilities with a focus on new features in the area of performance, security, availability and scalability.
This module overviews Microsoft Azure Data Services including Azure SQL Database, Azure DocumentDB, Azure Tables, Azure Blobs and the use of SQL Server in a Azure VM.
This module discusses the role of the modern data warehouse and draws the distinction between Traditional, Operational and Logical data warehouses. It then positions the main data warehouse options provided by Microsoft, specifically SQL Server 2016, Azure Data Warehouse and Azure Data Lake. The module finishes with more detail on Azure Data Warehouse.
This module discusses the Business Intelligence capabilities of SQL Server 2016. Most notably this includes Analysis Services, Reporting Services and Hybrid BI features.
This module overviews the objectives of "Enterprise Information Management" (EIM) and then goes into more detail on the three services by which SQL Server delivers EIM, specifically SQL Server Integration Services, SQL Server Master Data Services, and SQL Server Data Quality Services.
This module discusses how Power BI helps deliver a new generation of BI by extending Business Intelligence to everyone. The module overviews Power BI with a focus on what Data Sources are supported, and how Power BI delivers solutions for each of Business Users, Business Analysts, BI Professionals and Software Developers.
This module begins with a definition of Machine Learning, the typical Machine Learning Flow, Roles and challenges. It then goes on to introduce Azure Machine Learning and how it can be used in typical Business Scenarios.
This module defines the Azure Data Factory Service a cloud-based data integration service that orchestrates and automates the movement and transformation of data. It then talks in more detail how Azure Data Factory Service delivers Pipelines, Activities, Data Movement, Data Transformation and Linked Data Services. It then covers the important concepts of Datasets, the relationship between Data Factory entities and Data Flow Concepts.
This module starts with a definition of "R" as a open source statistics programming language and data visualization tool that is taught in most universities and is supported by a large community and Ecosystem of developers and free add-ons & applications. We then go on to discuss Microsoft "R" and how it can deliver greater parallelism, scale and speed, combined with simplicity and agility. The module ends with a look at he Predictive Analytics Process and the role of "R".
This module begins with a definition of Big Data and the business imperative it represents. It then goes on to introduce Apache Hadoop and it's components (HDFS, MapReduce, &Yarn). We then go on to discuss HDInsight which is a standard Apache Hadoop distribution offered as a managed service on Microsoft Azure. This is followed by a description of how HDInsight supports Hive (a SQL-like language which is subset of SQL), HBase (a NoSQL database on data in HDInsight), Mahout ( a machine learning library), Storm (Stream Analytics for near real-time processing), and Spark (In-memory processing on multiple workloads). Finally w look at how Visual Studio makes working with Hadoop easier and we look at some of the benefits of an Azure based approach to Big Data.
This module begins with a look at the two approaches to information management for analytics: Top-down and bottom-up. Azure Data lake uses a bottom-up approach and consists of Azure Data Lake Store and Azure Data Lake Analytics. Azure Data Lake Store is a highly scalable, distributed, parallel file system in the cloud specifically designed to work with multiple analytic frameworks. It is built from the ground up as a Hadoop file system, supports for file/folder objects and operations and integrates with HDInsight, Hortonworks, and Cloudera. Azure Data Lake Analytics is built on Apache YARN, scales dynamically with the turn of a dial, supports pay by the query & Azure Active Directory for access control, roles, and integration with on-premises identity systems. Further more it is built with U-SQL to unify the benefits of SQL with the power of C#.
This module discusses Apache Spark a unified framework for big data analytics. Apache Spark SQL includes Spark SQL ( for Interactive queries), Spark Streaming (for stream processing), Spark Mlib (Machine Learning Library) and GraphX (for graph computation).
Saving please wait...