"Azure Data Engineering Course in Hyderabad "

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Azure Data Engineering
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Table of content

• Introduction to Azure Data Engineering
• Azure Data Services Overview
• Azure Data Factory
• Azure Databricks
• Azure Synapse Analytics
• Azure Data Lake Storage
• Real-time Data Processing with Azure Stream
  Analytics
• Integration with Power BI

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Introduction to Azure Data Engineering

• Azure Data Engineering refers to the set of
  services and tools provided by Microsoft Azure
  for designing, implementing, and managing data
  solutions in the cloud. It encompasses various
  technologies and capabilities that allow
  organizations to process, store, and analyze
  large volumes of data efficiently. Whether
  dealing with structured or unstructured data,
  Azure Data Engineering provides a comprehensive
  suite of services to meet diverse business needs.
• As an Azure data engineer, you help stakeholders
  understand the data through exploration, and
  build and maintain secure and compliant data
  processing pipelines by using different tools and
  techniques. You use various Azure data services
  and frameworks to store and produce cleansed and
  enhanced datasets for analysis.

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Azure Data Services Overview

1. Azure SQL Database A fully managed relational
   database service that offers high-performance,
   scalability, and built-in security features. It
   supports popular database engines such as SQL
   Server, MySQL, and PostgreSQL.
2. Azure Cosmos DB A globally distributed,
   multi-model database service designed for
   building highly responsive and scalable
   applications. It supports multiple data models,
   including document, graph, key-value, table, and
   column-family.
3. Azure Synapse Analytics (formerly SQL Data
   Warehouse) An integrated analytics service that
   brings together big data and data warehousing. It
   allows users to query and analyze large datasets
   using both on-demand and provisioned resources.
4. Azure Data Lake Storage A scalable and secure
   data lake solution for big data analytics. It
   enables organizations to store and analyze
   massive amounts of data with features like
   hierarchical namespace and fine-grained access
   control.
5. Azure Blob Storage A massively scalable object
   storage service that is optimized for storing and
   serving large amounts of unstructured data, such
   as documents, images, and videos.
6. Azure Data Factory A cloud-based data
   integration service that allows organizations to
   create, schedule, and manage data pipelines,
   facilitating the movement and transformation of
   data across various sources and destinations.
7. Azure Databricks An Apache Spark-based analytics
   platform that provides a collaborative
   environment for big data analytics. It allows
   data engineers and data scientists to work
   together on large-scale data processing and
   machine learning tasks.
8. Azure HDInsight A fully managed cloud service
   that makes it easy to process large amounts of
   data using popular open-source frameworks such as
   Hadoop, Spark, Hive, HBase, and more.
9. Azure Stream Analytics A real-time analytics
   service that ingests, processes, and analyzes
   streaming data from various sources. It provides
   insights into trends and patterns as data is
   generated.
10. Azure Data Explorer A fast and highly scalable
    service designed for analyzing large volumes of
    data in real-time. It is particularly well-suited
    for log and telemetry data.
11. Azure Cache for Redis A fully managed,
    open-source, and in-memory data store service
    that provides sub-millisecond response times. It
    is commonly used for caching and accelerating
    data access.
12. Azure Data Box A family of devices designed to
    facilitate the secure and efficient transfer of
    large amounts of data to and from Azure. This is
    particularly useful for organizations dealing
    with massive datasets.
13. Azure Data Share A service that enables
    organizations to securely share data with other
    organizations in a governed and compliant manner.
    It simplifies the process of sharing data across
    Azure subscriptions and with external partners.
14. Azure Data Catalog A fully managed service that
    serves as a centralized repository for
    discovering, understanding, and managing data
    assets across an organization. It helps in
    maintaining a data catalog for better data
    governance

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Azure Data Factory

• Azure Data Factory (ADF) is a cloud-based data
  integration service provided by Microsoft Azure.
  It allows organizations to create, schedule, and
  manage data pipelines that can move data between
  supported on-premises and cloud-based data
  stores. Azure Data Factory simplifies the process
  of orchestrating and automating the movement and
  transformation of data, making it a fundamental
  component in modern data engineering workflows.
• Azure Data Factory is Azure's cloud ETL service
  for scale-out serverless data integration and
  data transformation. It offers a code-free UI for
  intuitive authoring and single-pane-of-glass
  monitoring and management. You can also lift and
  shift existing SSIS packages to Azure and run
  them with full compatibility in ADF.
• Azure Data Factory is a cloud-based data
  integration service provided by Microsoft. It
  allows you to create, schedule, and manage data
  pipelines that can move and transform data from
  various sources to different destinations.

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Azure Databricks

• Azure Databricks is a cloud-based big data
  analytics platform provided by Microsoft in
  collaboration with Databricks. It is built on
  Apache Spark and designed for data engineering,
  data science, and machine learning. Azure
  Databricks simplifies the process of building and
  managing Apache Spark-based big data and machine
  learning solutions by providing an integrated,
  collaborative environment for data scientists,
  data engineers, and business analysts.
• Azure Databricks is a fully managed first-party
  service that enables an open data lakehouse in
  Azure. With a lakehouse built on top of an open
  data lake, quickly light up a variety of
  analytical workloads while allowing for common
  governance across your entire data estate.
• Databricks is an industry-leading, cloud-based
  data engineering tool used for processing and
  transforming massive quantities of data and
  exploring the data through machine learning
  models. Recently added to Azure, it's the latest
  big data tool for the Microsoft cloud

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• Azure Synapse Analytics
• Azure Synapse Analytics, formerly known as Azure
  SQL Data Warehouse, is a cloud-based analytics
  service provided by Microsoft Azure. It is
  designed to enable organizations to analyze and
  query large volumes of data with high performance
  and scalability. Azure Synapse Analytics
  integrates both data warehousing and big data
  analytics capabilities, providing a unified
  platform for processing and analyzing diverse
  datasets.
• Azure Data Lake Storage
• Azure Data Lake Storage (ADLS) is a scalable and
  secure cloud-based data lake solution provided by
  Microsoft Azure. It is designed to handle large
  volumes of data for big data analytics and data
  science applications. Azure Data Lake Storage is
  built to support both structured and unstructured
  data, allowing organizations to store and analyze
  diverse datasets with high throughput and
  low-latency access.
• Real-time Data Processing with Azure Stream
  Analytics
• Azure Stream Analytics is a real-time analytics
  service provided by Microsoft Azure that allows
  organizations to process and analyze streaming
  data in real-time. It enables the extraction of
  insights and actionable information from
  continuous streams of data generated by various
  sources, such as IoT devices, social media,
  applications, and more. Azure Stream Analytics
  supports a wide range of scenarios, including
  real-time monitoring, anomaly detection, and
  event-driven applications

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Integration with Power BI

• Configure Power BI Output in Azure Stream
  Analytics In the Azure Stream Analytics job
  definition, users can configure Power BI as an
  output sink. This is done by specifying the Power
  BI output settings, including the Power BI
  workspace, dataset, and table to which the
  streaming data will be sent.
• Define Query Logic Users define the query logic
  in Azure Stream Analytics using the SQL-like
  query language. This query defines how the
  incoming streaming data is processed, filtered,
  and transformed before being sent to Power BI.
  The query can include various operations to
  extract meaningful information from the data.
• Specify Output Schema Users need to specify the
  output schema that aligns with the structure
  expected by the Power BI dataset. This includes
  defining the data types and structure of the
  fields that will be sent to Power BI.
• Establish Authentication To enable Azure Stream
  Analytics to push data to Power BI, users need to
  establish authentication. This typically involves
  providing the necessary credentials or using
  Azure Active Directory authentication to ensure
  secure communication between Azure Stream
  Analytics and Power BI.
• Start the Stream Analytics Job Once the
  configuration is complete, users start the Azure
  Stream Analytics job. This initiates the
  real-time processing of streaming data based on
  the defined query logic. As the data is
  processed, the results are continuously sent to
  the specified Power BI workspace and dataset.
• Visualize Real-Time Data in Power BI In Power
  BI, users can connect to the configured dataset
  and create real-time dashboards and reports. The
  streaming data from Azure Stream Analytics is
  visualized in Power BI, providing users with
  up-to-the-moment insights into their data.

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• Presenter name kathika.kalyani
• Email address info_at_3zenx.com
• Website address www.3ZenX.com