Layton, Utah SQL Server Training -> Event: 07/16/2018 Layton, Utah Microsoft SQL Server Training Course 20775 (5 days) : Performing Data Engineering on Microsoft HD Insight starting on 07/16/2018 and spanning 5 days in Layton, Utah - In order to proceed with requesting this Dynamics Edge Microsoft Training Event, please select from one of the following options by clicking one of the following links and then filling out and submitting the request form:
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Event: 07/16/2018 Layton, Utah Microsoft SQL Server Training Course 20775 (5 days) : Performing Data Engineering on Microsoft HD Insight starting on 07/16/2018 and spanning 5 days in Layton, Utah - Please note that pricing for this SQL Server Training Event may vary from prices published elsewhere on our website (no guarantees, but the price may end up being lower for you requesting the Event through this page than through requesting or purchasing this course or similar from elsewhere on our website), may only be valid as long as you stay on this page and decide within a certain amount of time from when you first landed on this page (e.g within a few hours after you landed on this page), may depend on which of the above options you choose, and may also may be associated with a variety of other factors. Any pricing information for this Event will be provided to you later in this process and in some cases, after you have completed submitting the required form that is associated with the option you choose above. If you did not receive pricing and you have successfully submitted the request form associated with the option you chose above, we'll try to respond to you as soon as possible with pricing, any discounts applied (if applicable) and other pertinent information. If you are associated with a government agency please Contact Us Separately Using This Link instead because you may be eligible for a separate pricing tier. Please note that "group discounts" are on an as-available and case-by-case basis and are not guaranteed to always be issued. If a one-time special request code for additional discounts IN ADDITION TO group discounts is included with your SQL Server Training Event request, please note that these "additional discounts," as all discounts claimed here, are subject to availability and case-by-case review, and are not guaranteed to be issued. We have many locations, please note that only SOME, not all, of our locations' physical street addresses are listed on our Locations page. This Event page will likely list the City and State only for where this SQL Server Training will be held. The actual facility physical address location information for this Event may be delivered to you after we receive your registration for this Event, and at our discretion we might require additional information from you prior to delivering you the actual physical street address of the facility that is associated with this Event. Location availability is not guaranteed and requires registration using the process on this page to check the availability. If this Location is unavailable for this Event and you indicated on-site only, we may suggest to you alternate nearby locations (and alternate course numbers and alternate dates, if applicable) if the selected Location (and course number and date, if applicable) for this Event is unavailable for SQL Server Training for any reason. Please note that all information shown on this page about this event is subject to manual review on a case-by-case basis. This event shown on this page is not guaranteed to be delivered and you must submit a request first through this page and receive confirmation before making plans to attend this event. Any travel reservations you make at any time are your sole responsibility regardless of whether or not Dynamics Edge can deliver this event and whether or not you received confirmation. It is advisable you obtain refundable reservations or request either Live Virtual Training, or if you request on-site in-person you should request a location near where you are right now, or a location close to where you are going to be located on a certain date and for at least the entire duration of the course. Also see our Policies page for more information.
The main purpose of the course is to give students the ability plan and implement big data workflows on HDInsight.
The primary audience for this course is data engineers, data architects, data scientists, and data developers who plan to implement big data engineering workflows on HDInsight.
This module introduces Hadoop, the MapReduce paradigm, and HDInsight.
This module provides an overview of the Microsoft Azure HDInsight cluster types, in addition to the creation and maintenance of the HDInsight clusters. The module also demonstrates how to customize clusters by using script actions through the Azure Portal, Azure PowerShell, and the Azure command-line interface (CLI). This module includes labs that provide the steps to deploy and manage the clusters.
This module provides an overview of non-domain and domain-joined Microsoft HDInsight clusters, in addition to the creation and configuration of domain-joined HDInsight clusters. The module also demonstrates how to manage domain-joined clusters using the Ambari management UI and the Ranger Admin UI. This module includes the labs that will provide the steps to create and manage domain-joined clusters.
This module provides an introduction to loading data into Microsoft Azure Blob storage and Microsoft Azure Data Lake storage. At the end of this lesson, you will know how to use multiple tools to transfer data to an HDInsight cluster. You will also learn how to load and transform data to decrease your query run time..
In this module, you will learn how to interpret logs associated with the various services of Microsoft Azure HDInsight cluster to troubleshoot any issues you might have with these services. You will also learn about Operations Management Suite (OMS) and its capabilities.
In this module, you will look at implementing batch solutions in Microsoft Azure HDInsight by using Hive and Pig. You will also discuss the approaches for data pipeline operationalization that are available for big data workloads on an HDInsight stack.
This module provides an overview of Apache Spark, describing its main characteristics and key features. Before you start, it’s helpful to understand the basic architecture of Apache Spark and the different components that are available. The module also explains how to design batch Extract, Transform, Load (ETL) solutions for big data with Spark on HDInsight. The final lesson includes some guidelines to improve Spark performance.
This module describes how to analyze data by using Spark SQL. In it, you will be able to explain the differences between RDD, Datasets and Dataframes, identify the uses cases between Iterative and Interactive queries, and describe best practices for Caching, Partitioning and Persistence. You will also look at how to use Apache Zeppelin and Jupyter notebooks, carry out exploratory data analysis, then submit Spark jobs remotely to a Spark cluster.
In this module, you will learn about running interactive queries using Interactive Hive (also known as Hive LLAP or Live Long and Process) and Apache Phoenix. You will also learn about the various aspects of running interactive queries using Apache Phoenix with HBase as the underlying query engine.
The Microsoft Azure Stream Analytics service has some built-in features and capabilities that make it as easy to use as a flexible stream processing service in the cloud. You will see that there are a number of advantages to using Stream Analytics for your streaming solutions, which you will discuss in more detail. You will also compare features of Stream Analytics to other services available within the Microsoft Azure HDInsight stack, such as Apache Storm. You will learn how to deploy a Stream Analytics job, connect it to the Microsoft Azure Event Hub to ingest real-time data, and execute a Stream Analytics query to gain low-latency insights. After that, you will learn how Stream Analytics jobs can be monitored when deployed and used in production settings.
In this module, you will learn how to use Kafka to build streaming solutions. You will also see how to use Kafka to persist data to HDFS by using Apache HBase, and then query this data.
This module explains how to develop big data real-time processing solutions with Apache Storm.
This module describes Spark Streaming; explains how to use discretized streams (DStreams); and explains how to apply the concepts to develop Spark Streaming applications.
This course requires that you meet the following prerequisites:
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