AWS Certification Training Courses

AWS Cloud Practitioner certification demonstrates and validates your knowledge on the overall understanding of AWS Cloud Platfoliyo

Google Cloud Certifications

DevOps Certifications

<ul>
	<li>The Exam DP-900: Microsoft Azure Data Fundamentals is intended for beginners who work with data in the cloud to validate their fundamental skills in Azure data services.</li>
	<li>This DP-900 Certification course includes the best documentary video courses with 40 videos and 110+ Practice tests which help you pass your Microsoft Azure Data Fundamentals Exam on the first attempt.</li>
	<li>Obtaining the Microsoft Azure Data Fundamentals DP-900 Credential ensures your basic understanding of Core Data concepts and how they can be implemented through Azure Data services.</li>
</ul>

<h1>Lab Details</h1>

<ol>
	<li>
	<p>In this lab we will walk through the steps to create an Apache Spark Cluster in Azure HDInsight then create a Jupyter Notebook to run Spark SQL queries against Apache Hive tables.</p>
	</li>
	<li>
	<p>Duration:&nbsp;<strong>75 minutes</strong>.</p>
	</li>
</ol>

<h1>Introduction</h1>

<h2>What is Azure HDInsight?</h2>

<ul>
	<li>
	<p>Azure HDInsight is a managed, full-spectrum, open-source analytics service in the cloud for enterprises.</p>
	</li>
	<li>
	<p>With Azure HDInsight, you can use open-source frameworks such as Hadoop, ApacheSpark, Apache Hive and many more in the Azure environment.</p>
	</li>
</ul>

<h2>What is Apache Spark Cluster?</h2>

<ul>
	<li>
	<p>Apache Spark is a parallel processing framework which supports in-memory processing to boost the performance of big-data analytic applications.</p>
	</li>
	<li>
	<p>Apache Spark in Azure HDInsight is Microsoft&#39;s implementation of Apache Spark in Azure cloud.</p>
	</li>
</ul>

<h1>Architecture Diagram</h1>

<p><img src="https://play.whizlabs.com/frontend/web/media/task_id_469/create_an_apache_spark_cluster_in_azure_hdinsight.png" /></p>

<h1>Task Details</h1>

<ol>
	<li>
	<p>Launching Lab Environment</p>
	</li>
	<li>
	<p>Create an Apache Spark Cluster in HDInsight</p>
	</li>
	<li>
	<p>Create a Jupyter Notebook</p>
	</li>
	<li>
	<p>Run Apache Spark SQL queries</p>
	</li>
</ol>

<h1>Lab Details</h1>

<ol>
	<li>
	<p>In this lab, you will use the Azure portal to create a data factory and then, you will use the Copy Data tool to create a pipeline that copies data from a SQL Server database to Azure Blob storage.</p>
	</li>
	<li>
	<p>Duration:&nbsp;<strong>120 minutes</strong></p>
	</li>
</ol>

<h1>Introduction</h1>

<h2>What is Azure Data Factory?</h2>

<ul>
	<li>
	<p>Big data requires a service that can orchestrate and operationalize processes to refine these enormous stores of raw data into actionable business insights. Azure Data Factory is a managed cloud service that&#39;s built for these complex hybrid extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects.</p>
	</li>
	<li>
	<p>Azure Data Factory is a Microsoft cloud service offered by the Azure platform that allows data integration from many different sources. Azure Data Factory is a perfect solution when in need of building hybrid extract-transform-load (ETL), extract-load-transform (ELT) and data integration pipelines.</p>
	</li>
	<li>
	<p>Azure Data Factory is Azure&#39;s cloud ETL service for scale-out serverless data integration and data transformation.&nbsp;</p>
	</li>
	<li>
	<p>It offers a code-free UI for intuitive authoring and single-pane-of-glass monitoring and management.&nbsp;</p>
	</li>
	<li>
	<p>You can also lift and shift existing SSIS packages to Azure and run them with full compatibility in ADF. SSIS Integration Runtime offers a fully managed service, so you don&#39;t have to worry about infrastructure management.</p>
	</li>
</ul>

<h1>Architecture Diagram<br />
<img src="https://play.whizlabs.com/frontend/web/media/task_id_468/working_with_data_movement_in_azure_data_factory.png" /></h1>

<h1>Task Details</h1>

<ol>
	<li>
	<p>Launching Lab Environment</p>
	</li>
	<li>
	<p>Create a Storage Account</p>
	</li>
	<li>
	<p>Create a Container</p>
	</li>
	<li>
	<p>Create a SQL Server database</p>
	</li>
	<li>
	<p>Create a table in SQL Server database</p>
	</li>
	<li>
	<p>Create a Data Factory</p>
	</li>
	<li>
	<p>Create a Pipeline and Copy data</p>
	</li>
</ol>

<h1>Lab Details</h1>

<ol>
	<li>
	<p>In this lab, you will learn how to integrate pipelines and activities using Synapse Studio.</p>
	</li>
	<li>
	<p>Duration:&nbsp;<strong>30 minutes</strong></p>
	</li>
</ol>

<h1>Introduction</h1>

<h2>What is Azure Synapse Analytics?</h2>

<ul>
	<li>
	<p>Azure Synapse Analytics offers various analytics engines to help you ingest, transform, model, and analyze your data. A dedicated SQL pool offers T-SQL based compute and storage capabilities. After creating a dedicated SQL pool in your Synapse workspace, data can be loaded, modeled, processed, and delivered for faster analytic insight.</p>
	</li>
	<li>
	<p>Azure Synapse is an enterprise analytics service that accelerates time to insight across data warehouses and big data systems.&nbsp;</p>
	</li>
	<li>
	<p>Azure Synapse brings together the best of SQL technologies used in enterprise data warehousing, Spark technologies used for big data, Data Explorer for log and time series analytics, Pipelines for data integration and ETL/ELT, and deep integration with other Azure services such as Power BI, CosmosDB, and AzureML.</p>
	</li>
	<li>
	<p>Azure Synapse is a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless or dedicated resources&mdash;at scale.</p>
	</li>
</ul>

<h1>Architecture Diagram<br />
<img src="https://play.whizlabs.com/frontend/web/media/task_id_470/integrate_with_pipelines_in_azure_synapse_analytics.png" /></h1>

<h1>Task Details</h1>

<ol>
	<li>
	<p>Launching Lab Environment</p>
	</li>
	<li>
	<p>Create a Synapse Analytics workspace</p>
	</li>
	<li>
	<p>Create a pipeline in Synapse Studio</p>
	</li>
	<li>
	<p>Exploring pipeline activities</p>
	</li>
</ol>

<h1>Lab Details</h1>

<ol>
	<li>
	<p>In this lab, you will learn how to use the Azure portal and Synapse Studio to create an Apache Spark pool in a Synapse workspace.</p>
	</li>
	<li>
	<p>Duration:&nbsp;<strong>30 minutes</strong></p>
	</li>
</ol>

<h1>Introduction</h1>

<h2>What is Apache Spark Pool?</h2>

<ul>
	<li>
	<p>Azure Synapse is an enterprise analytics service that accelerates time to insight across data warehouses and big data systems.&nbsp;</p>
	</li>
	<li>
	<p>Azure Synapse Analytics offers various analytics engines to help you ingest, transform, model, and analyze your data. A dedicated SQL pool offers T-SQL based compute and storage capabilities. After creating a dedicated SQL pool in your Synapse workspace, data can be loaded, modeled, processed, and delivered for faster analytic insight.</p>
	</li>
	<li>
	<p>Azure Synapse Analytics offers various analytics engines to help you ingest, transform, model, analyze, and distribute your data.&nbsp;</p>
	</li>
	<li>
	<p>Apache Spark is a parallel processing framework that supports in-memory processing to boost the performance of big data analytic applications. Apache Spark in Azure Synapse Analytics is one of Microsoft&#39;s implementations of Apache Spark in the cloud.&nbsp;</p>
	</li>
	<li>
	<p>Azure Synapse makes it easy to create and configure a serverless Apache Spark pool in Azure. Spark pools in Azure Synapse are compatible with Azure Storage and Azure Data Lake Generation 2 Storage. So you can use Spark pools to process your data stored in Azure.</p>
	</li>
	<li>
	<p>An Apache Spark pool provides open-source big data compute capabilities. After you&#39;ve created an Apache Spark pool in your Synapse workspace, data can be loaded, modeled, processed, and distributed for faster analytic insight.</p>
	</li>
</ul>

<h1>Architecture Diagram<br />
<img alt="" src="https://play.whizlabs.com/frontend/web/media/task_id_471/apache_spark_pool.png" /></h1>

<h1>Task Details</h1>

<ol>
	<li>
	<p>Launching Lab Environment</p>
	</li>
	<li>
	<p>Create a Synapse Analytics workspace</p>
	</li>
	<li>
	<p>Create an Apache Spark pool with Azure Portal&nbsp;</p>
	</li>
	<li>
	<p>Create an Apache Spark pool with Synapse Studio</p>
	</li>
</ol>

<h1>Lab Details</h1>

<ol>
	<li>
	<p>In this lab,&nbsp;you will learn how to create a Notebook and to connect to the Apache Spark pool and run Spark SQL queries against files and tables.</p>
	</li>
	<li>
	<p>Duration:&nbsp;<strong>45 minutes</strong></p>
	</li>
</ol>

<h1>Introduction</h1>

<h2>What is Apache Spark Pool?</h2>

<ul>
	<li>
	<p>Azure Synapse is an enterprise analytics service that accelerates time to insight across data warehouses and big data systems.&nbsp;</p>
	</li>
	<li>
	<p>Azure Synapse Analytics offers various analytics engines to help you ingest, transform, model, and analyze your data. A dedicated SQL pool offers T-SQL based compute and storage capabilities. After creating a dedicated SQL pool in your Synapse workspace, data can be loaded, modeled, processed, and delivered for faster analytic insight.</p>
	</li>
	<li>
	<p>Azure Synapse Analytics offers various analytics engines to help you ingest, transform, model, analyze, and distribute your data.&nbsp;</p>
	</li>
	<li>
	<p>Apache Spark is a parallel processing framework that supports in-memory processing to boost the performance of big data analytic applications. Apache Spark in Azure Synapse Analytics is one of Microsoft&#39;s implementations of Apache Spark in the cloud.&nbsp;</p>
	</li>
	<li>
	<p>Azure Synapse makes it easy to create and configure a serverless Apache Spark pool in Azure. Spark pools in Azure Synapse are compatible with Azure Storage and Azure Data Lake Generation 2 Storage. So you can use Spark pools to process your data stored in Azure.</p>
	</li>
	<li>
	<p>An Apache Spark pool provides open-source big data compute capabilities. After you&#39;ve created an Apache Spark pool in your Synapse workspace, data can be loaded, modeled, processed, and distributed for faster analytic insight.</p>
	</li>
</ul>

<h1>Architecture Diagram<br />
<img alt="" src="https://play.whizlabs.com/frontend/web/media/task_id_472/notebook_synapse.png" /></h1>

<h1>Task Details</h1>

<ol>
	<li>
	<p>Launching Lab Environment</p>
	</li>
	<li>
	<p>Create a Synapse Analytics workspace</p>
	</li>
	<li>
	<p>Create an Apache Spark pool with Azure Portal&nbsp;</p>
	</li>
	<li>
	<p>Create a Notebook</p>
	</li>
	<li>
	<p>Run Spark SQL queries</p>
	</li>
</ol>

<h1>Lab Details</h1>

<ol>
	<li>
	<p>In this lab, you will learn how to perform exploratory data analysis by using Azure Open Datasets and Apache Spark and then visualize the results in a Synapse Studio notebook in Azure Synapse Analytics.</p>
	</li>
	<li>
	<p>Duration:&nbsp;<strong>45 minutes</strong></p>
	</li>
</ol>

<h1>Introduction</h1>

<h2>What is Apache Spark Pool?</h2>

<ul>
	<li>
	<p>Azure Synapse is an enterprise analytics service that accelerates time to insight across data warehouses and big data systems.&nbsp;</p>
	</li>
	<li>
	<p>Azure Synapse Analytics offers various analytics engines to help you ingest, transform, model, and analyze your data. A dedicated SQL pool offers T-SQL based compute and storage capabilities. After creating a dedicated SQL pool in your Synapse workspace, data can be loaded, modeled, processed, and delivered for faster analytic insight.</p>
	</li>
	<li>
	<p>Azure Synapse Analytics offers various analytics engines to help you ingest, transform, model, analyze, and distribute your data.&nbsp;</p>
	</li>
	<li>
	<p>Apache Spark is a parallel processing framework that supports in-memory processing to boost the performance of big data analytic applications. Apache Spark in Azure Synapse Analytics is one of Microsoft&#39;s implementations of Apache Spark in the cloud.&nbsp;</p>
	</li>
	<li>
	<p>Azure Synapse makes it easy to create and configure a serverless Apache Spark pool in Azure. Spark pools in Azure Synapse are compatible with Azure Storage and Azure Data Lake Generation 2 Storage. So you can use Spark pools to process your data stored in Azure.</p>
	</li>
	<li>
	<p>An Apache Spark pool provides open-source big data compute capabilities. After you&#39;ve created an Apache Spark pool in your Synapse workspace, data can be loaded, modeled, processed, and distributed for faster analytic insight.</p>
	</li>
</ul>

<h1>Architecture Diagram<br />
<img alt="" src="https://play.whizlabs.com/frontend/web/media/task_id_472/notebook_synapse.png" /></h1>

<h1>Task Details</h1>

<ol>
	<li>
	<p>Launching Lab Environment</p>
	</li>
	<li>
	<p>Create a Synapse Analytics workspace</p>
	</li>
	<li>
	<p>Create an Apache Spark pool with Azure Portal&nbsp;</p>
	</li>
	<li>
	<p>Create a Notebook</p>
	</li>
	<li>
	<p>Download and prepare the data</p>
	</li>
	<li>
	<p>Analyze &amp; Visualize data</p>
	</li>
</ol>

<h1>Lab Details</h1>

<ol>
	<li>
	<p>In this lab, you will learn how to copy a sample machine learning notebook from the Synapse Analytics gallery into your workspace, modify it, and run it.</p>
	</li>
	<li>
	<p>Duration:&nbsp;<strong>45 minutes</strong></p>
	</li>
</ol>

<h1>Introduction</h1>

<h2>What is Azure Synapse Analytics?</h2>

<ul>
	<li>
	<p>Azure Synapse Analytics offers various analytics engines to help you ingest, transform, model, and analyze your data. A dedicated SQL pool offers T-SQL based compute and storage capabilities. After creating a dedicated SQL pool in your Synapse workspace, data can be loaded, modeled, processed, and delivered for faster analytic insight.</p>
	</li>
	<li>
	<p>Azure Synapse is an enterprise analytics service that accelerates time to insight across data warehouses and big data systems.&nbsp;</p>
	</li>
	<li>
	<p>Azure Synapse brings together the best of SQL technologies used in enterprise data warehousing, Spark technologies used for big data, Data Explorer for log and time series analytics, Pipelines for data integration and ETL/ELT, and deep integration with other Azure services such as Power BI, CosmosDB, and AzureML.</p>
	</li>
	<li>
	<p>Azure Synapse is a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless or dedicated resources&mdash;at scale.</p>
	</li>
</ul>

<h2>What is Machine Learning?</h2>

<ul>
	<li>
	<p>Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.</p>
	</li>
	<li>
	<p>Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today&#39;s leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.</p>
	</li>
</ul>

<h1>Architecture Diagram<br />
<img src="https://play.whizlabs.com/frontend/web/media/task_id_481/copy_sample_ml_notebook.png" /></h1>

<h1>Task Details</h1>

<ol>
	<li>
	<p>Launching Lab Environment</p>
	</li>
	<li>
	<p>Create a Synapse Analytics workspace</p>
	</li>
	<li>
	<p>Create an Apache Spark pool with Azure Portal&nbsp;</p>
	</li>
	<li>
	<p>Copy the notebook to workspace</p>
	</li>
	<li>
	<p>Run the notebook</p>
	</li>
	<li>
	<p>Make a copy of the notebook</p>
	</li>
</ol>

<h1>Lab Details</h1>

<ol>
	<li>
	<p>In this lab, you will learn how to load and analyze data with Data Explorer for Azure Synapse.</p>
	</li>
	<li>
	<p>Duration:&nbsp;<strong>60 minutes</strong></p>
	</li>
</ol>

<h1>Introduction</h1>

<h2>What is Azure Synapse Analytics?</h2>

<ul>
	<li>
	<p>Azure Synapse Analytics offers various analytics engines to help you ingest, transform, model, and analyze your data. A dedicated SQL pool offers T-SQL based compute and storage capabilities. After creating a dedicated SQL pool in your Synapse workspace, data can be loaded, modeled, processed, and delivered for faster analytic insight.</p>
	</li>
	<li>
	<p>Azure Synapse brings together the best of SQL technologies used in enterprise data warehousing, Spark technologies used for big data, Data Explorer for log and time series analytics, Pipelines for data integration and ETL/ELT, and deep integration with other Azure services such as Power BI, CosmosDB, and AzureML.</p>
	</li>
</ul>

<h2>What is Azure Data Explorer?</h2>

<ul>
	<li>
	<p>Azure Data Explorer is a fully managed, high-performance, big data analytics platform that makes it easy to analyze high volumes of data in near real time. The Azure Data Explorer toolbox gives you an end-to-end solution for data ingestion, query, visualization, and management.</p>
	</li>
</ul>

<ul>
	<li>
	<p>With Azure Data Explorer, you can ingest terabytes of data in minutes in batch or streaming mode. You can query petabytes of data, with results returned within milliseconds to seconds. Azure Data Explorer provides high velocity (millions of events per second), low latency (seconds), and linear scale ingestion of raw data.&nbsp;</p>
	</li>
</ul>

<h1>Architecture Diagram<br />
<img src="https://play.whizlabs.com/frontend/web/media/task_id_485/analyze_data_with_data_explorer.png" /></h1>

<h1>Task Details</h1>

<ol>
	<li>
	<p>Launching Lab Environment</p>
	</li>
	<li>
	<p>Create a Synapse Analytics workspace</p>
	</li>
	<li>
	<p>Create a Data Explorer pool</p>
	</li>
	<li>
	<p>Create a Data Explorer database</p>
	</li>
	<li>
	<p>Ingest and Analyze data</p>
	</li>
</ol>

<h1>Lab Details</h1>

<ol>
	<li>
	<p>In this lab, you will learn how to create an Azure Data Explorer cluster and database.</p>
	</li>
	<li>
	<p>Duration:&nbsp;<strong>45 minutes</strong></p>
	</li>
</ol>

<h1>Introduction</h1>

<h2>What is Azure Data Explorer?</h2>

<ul>
	<li>
	<p>Azure Data Explorer is a fully managed, high-performance, big data analytics platform that makes it easy to analyze high volumes of data in near real time. The Azure Data Explorer toolbox gives you an end-to-end solution for data ingestion, query, visualization, and management.</p>
	</li>
</ul>

<ul>
	<li>
	<p>With Azure Data Explorer, you can ingest terabytes of data in minutes in batch or streaming mode. You can query petabytes of data, with results returned within milliseconds to seconds. Azure Data Explorer provides high velocity (millions of events per second), low latency (seconds), and linear scale ingestion of raw data.&nbsp;</p>
	</li>
</ul>

<p>Architecture Diagram<br />
<img src="https://play.whizlabs.com/frontend/web/media/task_id_486/working_with_data_explorer.png" /></p>

<h1>Task Details</h1>

<ol>
	<li>
	<p>Launching Lab Environment</p>
	</li>
	<li>
	<p>Create a cluster</p>
	</li>
	<li>
	<p>Create a database</p>
	</li>
	<li>
	<p>Run basic commands</p>
	</li>
</ol>

<h1>Lab Details</h1>

<ol>
	<li>
	<p>In this lab, you will learn how to load sample data into an Azure Data Explorer database.</p>
	</li>
	<li>
	<p>Duration:&nbsp;<strong>45 minutes</strong></p>
	</li>
</ol>

<h1>Introduction</h1>

<h2>What is Azure Data Explorer?</h2>

<ul>
	<li>
	<p>Azure Data Explorer is a fully managed, high-performance, big data analytics platform that makes it easy to analyze high volumes of data in near real time. The Azure Data Explorer toolbox gives you an end-to-end solution for data ingestion, query, visualization, and management.</p>
	</li>
</ul>

<ul>
	<li>
	<p>With Azure Data Explorer, you can ingest terabytes of data in minutes in batch or streaming mode. You can query petabytes of data, with results returned within milliseconds to seconds. Azure Data Explorer provides high velocity (millions of events per second), low latency (seconds), and linear scale ingestion of raw data.&nbsp;</p>
	</li>
</ul>

<h1>Architecture Diagram<br />
<img src="https://play.whizlabs.com/frontend/web/media/task_id_490/ingest_data_into_azure_data_explorer.png" /></h1>

<h1>Task Details</h1>

<ol>
	<li>
	<p>Launching Lab Environment</p>
	</li>
	<li>
	<p>Create a cluster</p>
	</li>
	<li>
	<p>Create a database</p>
	</li>
	<li>
	<p>Ingest the sample data</p>
	</li>
</ol>

<h1>Lab Details</h1>

<ol>
	<li>
	<p>In this lab, you&#39;ll be working in the stand-alone Azure Data Explorer web UI to query the data.</p>
	</li>
	<li>
	<p>Duration:&nbsp;<strong>60 minutes</strong></p>
	</li>
</ol>

<h1>Introduction</h1>

<h2>What is Azure Data Explorer?</h2>

<ul>
	<li>
	<p>Azure Data Explorer is a fully managed, high-performance, big data analytics platform that makes it easy to analyze high volumes of data in near real time. The Azure Data Explorer toolbox gives you an end-to-end solution for data ingestion, query, visualization, and management.</p>
	</li>
</ul>

<ul>
	<li>
	<p>With Azure Data Explorer, you can ingest terabytes of data in minutes in batch or streaming mode. You can query petabytes of data, with results returned within milliseconds to seconds. Azure Data Explorer provides high velocity (millions of events per second), low latency (seconds), and linear scale ingestion of raw data.&nbsp;</p>
	</li>
</ul>

<h1>Architecture Diagram<br />
<img src="https://play.whizlabs.com/frontend/web/media/task_id_491/query_data_in_azure_data_explorer.png" /></h1>

<h1>Task Details</h1>

<ol>
	<li>
	<p>Launching Lab Environment</p>
	</li>
	<li>
	<p>Create a cluster</p>
	</li>
	<li>
	<p>Create a database</p>
	</li>
	<li>
	<p>Ingest the sample data</p>
	</li>
	<li>
	<p>Run queries</p>
	</li>
	<li>
	<p>Explore the results grid</p>
	</li>
</ol>

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This DP-900 course ensures your core skills of database concepts in a cloud environment to help you pass Microsoft Azure Data Fundamentals on first attempt.

Microsoft Azure Data Fundamentals (DP-900) - Training Course

<ul>
<li>The Exam DP-900: Microsoft Azure Data Fundamentals is intended for beginners who work with data in the cloud to validate their fundamental skills in Azure data services.</li>
<li>This DP-900 Certification course includes the best documentary video courses with 40 videos and 110+ Practice tests which help you pass your Microsoft Azure Data Fundamentals Exam on the first attempt.</li>
<li>Obtaining the Microsoft Azure Data Fundamentals DP-900 Credential ensures your basic understanding of Core Data concepts and how they can be implemented through Azure Data services.</li>
</ul>

Practice Exams and Video Lectures

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