Big Data Analytics on Microsoft AZURE

Explore Azure stream analytics, an on-demand real-time analytics service that processes high-volume IoT and other data streams with SQL-like queries, enabling pattern discovery, alerts, and real-time dashboards.

Explore HDInsight, a managed Azure service for Hadoop-based big data analytics, enabling on-demand, pay-as-you-go processing with open source tools and machine learning.

Explore lake analytics, an on-demand analytics job service for big data on your data lake, offering dynamic scaling, pay-per-use pricing, and secure governance with Active Directory integration.

Explore Data Explorer, a fast, fully managed analytics service for real-time analysis of streaming data from applications and websites, enabling rapid insights.

Explore Azure Databricks for fast, collaborative data analytics on the cloud with interactive workspaces, Python support, deep learning frameworks, and scalable clusters for real-time analytics and machine learning.

Learn to create and run big data analytics jobs with Azure Data Lake Analytics, including creating a data analytics account, writing U-SQL scripts, and examining job outputs.

Create an HDInsight cluster on Azure by configuring settings, selecting a cluster type, enabling Spark and Hive, then deploy and monitor via notebooks.

Explore the HDInsight dashboard to monitor cluster insights, run Hive queries, and visualize metrics with heat maps and resource manager indicators.

Learn how to create a spark cluster on HDInsight, configure storage, set cluster name and admin credentials, and deploy the cluster with access through the resource group and notebooks.

Use Jupyter and Zeppelin notebooks in HDInsight to connect to Spark clusters, run queries, and visualize big data on Azure.

Learn to use Zeppelin notebooks in an HDInsight cluster to run Apache Spark jobs, load sample data, execute Spark queries, and create interactive charts and dashboards.

Create a stream analytics job, add input sources such as event hub, IoT hub, or blob storage, and configure streaming units and the query output.

Connect your blob storage input to a stream analytics job by creating a container named input, configuring encoding and date format, and explore output options for future json data analysis.

Learn how to connect a blob storage output to a Stream Analytics job by adding an output, configuring alias and container, setting the path pattern, and choosing JSON input format.

Write and test a transformation query in Azure Stream Analytics, using a SQL-like language, to transform input blob data in a container into an output stream.

Upload your test data via the three dots menu, specify the input file, and run the stream analytics job to upload the input stream and retrieve the results.

Explore how three things merge into one stream, with input and their first output, and understand true energy objects.

Discover data lake storage, a massively scalable, secure Azure blob storage for big data analytics. Ingest, cleanse, and annotate diverse data, then run analytics with Hadoop and lake analytics.