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1. Azure Data Engineering

2. 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

3. 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.

4. 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

5. 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.

6. 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

7. 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

8. Integration with Power BI
1.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.
2.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.
3.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.
4.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.
5.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.
6.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.

9. Presenter name: kathika.kalyani
Email address: info@3zenx.com
Website address: www.3ZenX.com