Course

Implementing Data Analytics with Azure Synapse – Intensive Training («DP312»)

This course covers SQL dedicated and serverless Spark poolsand providing instructions of data wrangling and the ELT process with Synapse Pipelines, similar to Azure Data Factory (ADF), for moving data into the Synapse dedicated pool database.
Vendor code

DP-3012

Duration 1 day
Price 900.–
Course documents Official Microsoft Courseware on Microsoft Learn

Course facts

Key Learnings
  • Understanding Azure Synapse Analytics as a cloud platform for big data processing and analysis
  • Using serverless SQL pools to query and transform data directly from a data lake without loading it into a database
  • Using Spark in Azure Synapse Analytics to analyze and visualize large volumes of data in a data lake
  • Using Azure Synpas Analytics to implement security to protect your data
  • Using Spark Notebooks in an Azure Synapse Analytics pipeline to build integrated data solutions that extract, transform, and load data across diverse systems
Content

1 Implement a Data Analytics Solution with Azure Synapse Analytics

Module 1: Introduction to Azure Synapse Analytics
Learn about the features and capabilities of Azure Synapse Analytics - a cloud-based platform for big data processing and analysis.

Module 2: Use Azure Synapse serverless SQL pool to query files in a data lake
With Azure Synapse serverless SQL pool, you can leverage your SQL skills to explore and analyze data in files, without the need to load the data into a relational database.

Module 3: Analyze data with Apache Spark in Azure Synapse Analytics
Apache Spark is a core technology for large-scale data analytics. Learn how to use Spark in Azure Synapse Analytics to analyze and visualize data in a data lake.

Module 4: Use Delta Lake in Azure Synapse Analytics
Delta Lake is an open source relational storage area for Spark that you can use to implement a data lakehouse architecture in Azure Synapse Analytics.

Module 5: Analyze data in a relational data warehouse
Relational data warehouses are a core element of most enterprise Business Intelligence (BI) solutions, and are used as the basis for data models, reports, and analysis.

Module 6: Build a data pipeline in Azure Synapse Analytics
Pipelines are the lifeblood of a data analytics solution. Learn how to use Azure Synapse Analytics pipelines to build integrated data solutions that extract, transform, and load data across diverse systems.

2 Build data analytics solutions using Azure Synapse serverless SQL pools

Module 7: Use Azure Synapse serverless SQL pool to query files in a data lake
With Azure Synapse serverless SQL pool, you can leverage your SQL skills to explore and analyze data in files, without the need to load the data into a relational database.

Module 8: Use Azure Synapse serverless SQL pools to transform data in a data lake
By using a serverless SQL pool in Azure Synapse Analytics, you can use the ubiquitous SQL language to transform data in files in a data lake.

Module 9: Create a lake database in Azure Synapse Analytics
Why choose between working with files in a data lake or a relational database schema? With lake databases in Azure Synapse Analytics, you can combine the benefits of both.

Module 10: Secure data and manage users in Azure Synapse serverless SQL pools
Learn how you can set up security when using Azure Synapse serverless SQL pools

3 Perform data engineering with Azure Synapse Apache Spark Pools

Module 11: Analyze data with Apache Spark in Azure Synapse Analytics
Apache Spark is a core technology for large-scale data analytics. Learn how to use Spark in Azure Synapse Analytics to analyze and visualize data in a data lake.

Module 12: Transform data with Spark in Azure Synapse Analytics
Data engineers commonly need to transform large volumes of data. Apache Spark pools in Azure Synapse Analytics provide a distributed processing platform that they can use to accomplish this goal.

Module 13: Use Delta Lake in Azure Synapse Analytics
Delta Lake is an open source relational storage area for Spark that you can use to implement a data lakehouse architecture in Azure Synapse Analytics.

4  Work with Data Warehouses using Azure Synapse Analytics

Module 14: Analyze data in a relational data warehouse
Relational data warehouses are a core element of most enterprise Business Intelligence (BI) solutions, and are used as the basis for data models, reports, and analysis.

Module 15: Load data into a relational data warehouse
A core responsibility for a data engineer is to implement a data ingestion solution that loads new data into a relational data warehouse.

Module 16: Manage and monitor data warehouse activities in Azure Synapse Analytics
Learn how to manage and monitor Azure Synapse Analytics.

Module 17: Secure a data warehouse in Azure Synapse Analytics
Learn how to approach and implement security to protect your data with Azure Synapse Analytics.

5 Transfer and transform data with Azure Synapse Analytics pipelines

Module 18: Build a data pipeline in Azure Synapse Analytics
Pipelines are the lifeblood of a data analytics solution. Learn how to use Azure Synapse Analytics pipelines to build integrated data solutions that extract, transform, and load data across diverse systems.

Module 19: Use Spark Notebooks in an Azure Synapse Pipeline
Apache Spark provides data engineers with a scalable, distributed data processing platform, which can be integrated into an Azure Synapse Analytics pipeline.

Target audience

This course is designed for administrators and data Engineers.

Requirements

The Audience should have familiarity with notebooks that use different languages and a Spark engine, such as Databricks, Jupyter Notebooks, Zeppelin notebooks and more. They should also have some experience with SQL, Python, and Azure tools, such as Data Factory.

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