This Azure training course is designed to equip students with the knowledge need to process, store and analyze data for making informed business decisions. Through this Azure course, the student will understand what big data is along with the importance of big data analytics, which will improve the students mathematical and programming skills. Students will learn the most effective method of using essential analytical tools such as Python, R, and Apache Spark.
oferecido por
Data Processing with Azure
LearnQuestInformações sobre o curso
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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 13 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
O que você vai aprender
Configure batch processing with Databricks and Data Factory on Azure
Use ETL and ELT to load and transform data
Create linked services and identify pipelines for data stored within Data Factory
Explain Data Virtualization in PolyBase
Habilidades que você terá
Data AnalysisMicrosoft AzureBig DataData LakeData Factory
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 13 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
oferecido por
LearnQuest
LearnQuest is the preferred training partner to the world’s leading companies, organizations, and government agencies. Our team boasts 20+ years of experience designing, developing and delivering a full suite industry-leading technology education classes and training solutions across the globe. Our trainers, equipped with expert industry experience and an unparalleled commitment to quality, facilitate classes that are offered in various delivery formats so our clients can obtain the training they need when and where they need it.
Programa - O que você aprenderá com este curso
7 minutos para concluir
Introduction
This Azure training course is designed to equip the students with the knowledge need to process, store and analyze data for making informed business decisions. Through this Azure course, the student will understand what big data is along with the importance of big data analytics, which will improve the students mathematical and programming skills. Students will learn the most effective method of using essential analytical tools such as R, and Apache Spark.
7 minutos para concluir
1 vídeo (Total 7 mín.)
1 vídeos
2 horas para concluir
Section 1 - Batch Processing with Databricks and Data Factory on Azure
One of the primary benefits of Azure Databricks is its ability to integrate with many other data environments to pull data through an ETL or ELT process. In module course, we examine each of the E, L, and T to learn how Azure Databricks can help ease us into a cloud solution.
2 horas para concluir
5 vídeos (Total 40 mín.), 3 leituras, 1 teste
5 videos
1.2 - ELT Processing using Azure9min
1.3 - Databricks and Azure Spark7min
1.4 Transform Data using Databricks in ADF8min
1.5 Use Case: ADF and Spark7min
3 leituras
Azure Databricks and Apache Spark10min
Exercise 1 - Use Batch Processing with Databricks and Data Factory on Azure10min
Exercise 2 - Intro to Databricks and Data Factory Page30min
1 exercício prático
Module 1 Quiz30min
2 horas para concluir
Section 2 - Creating Pipelines and Activities
Processing big data in real-time is now an operational necessity for many businesses. Azure Stream Analytics is Microsoft’s serverless real-time analytics offering for complex event processing. In this section we examine how customers unlock valuable insights and gain competitive advantage by harnessing the power of big data.
2 horas para concluir
4 vídeos (Total 46 mín.), 3 leituras, 1 teste
4 videos
Processing using a Pipeline7min
Analyzing Logs for an HDInsight Cluster9min
Using Azure Blob Storage within HDInsight7min
3 leituras
Using Azure Blob Storage with HDInsight5min
Exercise 1 - Pipeline Activities & Usage in Azure Data Factory15min
Exercise 2 - Examine Logs within the HDInsight/Blob Storage15min
1 exercício prático
Module 2 Quiz30min
2 horas para concluir
Section 3 - Link Services and Datasets
A data factory can have one or more pipelines. A pipeline is a logical grouping of activities that together perform a task. The activities in a pipeline define actions to perform on your data. Before you create a dataset, you must create a linked service to link your data store to the data factory. This section deals with linked services and data sets within Azure Blob Storage.
2 horas para concluir
5 vídeos (Total 38 mín.), 1 leitura, 1 teste
5 videos
Identifying Pipelines for a Data Factory9min
Data Stores and Azure Blob Storage5min
Linked Service and Connecting Data Factory to External Resources3min
Processing Input Blobs with Azure Data Factory5min
1 leituras
Exercise 1 - Link Data within Datasets in Azure Storage10min
1 exercício prático
Module 3 Quiz30min
1 hora para concluir
Section 4 - Schedules and Triggers
Azure Data Factory is a fully managed, cloud-based data orchestration service that enables data movement and transformation. In this section, we explore scheduling triggers for Azure Data Factory to automate your pipeline execution.
1 hora para concluir
5 vídeos (Total 17 mín.), 1 leitura, 1 teste
5 videos
Creating a Trigger that Runs a Pipeline on a Schedule2min
Scheduling a Trigger in Azure Data Factory3min
Pipeline Execution and Triggers in ADF3min
Use Case: Azure Schedule, Trigger, and Events2min
1 leituras
Pipeline Execution and Triggers in ADF5min
1 exercício prático
Module 4 Quiz30min
2 horas para concluir
Section 5 - Selecting Windowing Functions
In time-streaming scenarios, performing operations on the data contained in temporal windows is a common pattern. Stream Analytics has native support for windowing functions, enabling developers to author complex stream processing jobs with minimal effort. In this section, we study windowing functions related to in-stream analytics.
2 horas para concluir
5 vídeos (Total 30 mín.), 1 leitura, 1 teste
5 videos
How Stream Analytics Support Native Windowing Functions 9min
Temporal Windows5min
Using Window Functions in the GROUP BY Clause2min
Aggregating Events over Multiple Windows using WindowsQ6min
1 leituras
Understanding Stream Analytics Windowing Functions10min
1 exercício prático
Module 5 Quiz30min
2 horas para concluir
Section 6 - Configuring Input and Output for Streaming Data Solutions
This section teaches how to analyze phone call data using Azure Stream Analytics. The phone call data, generated by a client application, contains some fraudulent calls, which will be filtered by the Stream Analytics job.
2 horas para concluir
7 vídeos (Total 33 mín.), 1 leitura, 1 teste
7 videos
Generate Sample Call Data and Send it to Event Hubs10min
Creating a Stream Analytics Job 7min
Configuring Job Input and Output 3min
Define a Query to Filter Fraudulent Calls 2min
Test and Start the Job4min
Visualize Results in Power BI2min
1 leituras
Output Real-Time Stream Analytics Data to a Power BI Dashboard10min
1 exercício prático
Module 6 Quiz30min
1 hora para concluir
Section 7 - ELT versus ETL in Polybase
Traditional SMP data warehouses use an Extract, Transform and Load (ETL) process for loading data. Azure SQL Data Warehouse is a massively parallel processing (MPP) architecture that takes advantage of the scalability and flexibility of compute and storage resources. Utilizing an Extract, Load, and Transform (ELT) process can take advantage of MPP and eliminate resources needed to transform the data prior to loading.
1 hora para concluir
4 vídeos (Total 21 mín.), 1 leitura, 1 teste
4 videos
How SQL Data Warehouse in Microsoft Offers ELT Solutions 3min
Loading Methods using Non-PolyBase Options 3min
Use Case: A Deeper Dive into ETL Processing 3min
1 leituras
Video: Ingesting Data using Polybase | Azure SQL Data Warehouse10min
1 exercício prático
Module 7 Quiz30min
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