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The cloud requires to reconsider some of the choices made for on-premisses data handling. This module introduces the concept of a data lake and the data lakehouse. It also introduces the different services in Azure that can be used for data processing, and compares them to the traditional on-premisses data stack. Finally, it provides a brief intro in Azure and the use of the Azure portal.
Synapse Analytics is the cornerstone service for the data engineer. It encompasses pipelines to copy data, Spark and SQL to transform and query data, Data Explorer for near realtime analysis and data exploration and Power BI for reporting. This module provides a brief introduction into this service.
When the data is stored on-premisses, you typically use ETL tools such as SQL Server Integration Services for loading and transforming data. But what if the data is stored in the Azure cloud? Then you can use pipelines in Azure Synapse Analytics. This service is nearly identical to Azure Data Factory, the cloud-based ETL service. First we need to get used to the terminology, then we can start creating the proper objects in the portal.
With Data flows data can be transformed without the need to learn about another tool (such as Databricks or Spark). Both Data flows as well as the Power Query activity are covered.
Pipelines need integration runtimes to control where the code executes. This module walks you through the 3 types of Integration Runtimes: Azure, SSIS and self-hosted runtimes.
Once development has finished the pipelines need to be deployed and scheduled for execution. Monitoring the deployed pipelines for failure, errors or just performance is another crucial topic discussed in this module.
Once data has been loaded into the data lake, the next step is to cleanse the data, pre-aggregate the data and perform other steps to make the data accessible to reporting and analytical tools. Dependant on the transformations required and the skills of the data engineer, the SQL dialect common to the Microsoft data stack (T-SQL) could play an important role. This module first introduces the scenarios where the move from an Azure SQL Database into Synapse databases could be useful, introduces briefly the two different types of SQL databases, and then focusses more deeply on the Synapse Analytics Serverless databases.
Since Serverless SQL Pools don't store data in a proprietary format, they lack features such as indexes, update statements etc. This is where Provisioned SQL Pools in Azure Synapse Analytics (formerly known as Azure Data Warehouse) can come to the rescue.
Although SQL is a very powerful language to access and manipulate data, it has its limitations. Complex data wrangling, advanced statistics or machine learning are ill-suited tasks for SQL. For this purpose Spark is better suited. It's a divide-and-conquer framework for data access, transformation and querying which relies on programming languages such as Scala and Python. Spark can be used in Synapse Analytics as well as in Azure Databricks, a popular service which integrates with Synapse Analytics pipelines as well.
Spark doesn't have a proprietary data storage option, but consumes and produces regular files stored in Azure Storage. This module covers how to access and manipulate data stored in the Synapse Analytics data lake or other Azure storage locations.
Once the Databricks solution has been tested it need to be scheduled for execution. This can be done either with jobs in Azure Databricks or via Synapse Analytics Pipelines. In the latter case you need to be able to pass on variables. Azure Databricks widgets will make this possible.
Handling large volumes of data requires different skills: One must master storage options, tools to upload data performant, handling failed uploads, and convert data in a format appropriate for reporting and analysis. In the Microsoft Azure stack, Synapse Analytics is the cornerstone service for the data engineer. It encompasses pipelines to copy data, Spark and SQL to transform and query data, Data Explorer for near realtime analysis and data exploration, and Power BI for reporting.
This training teaches how to use Synapse Analytics to design, build and maintain a modern data lake architecture. The training also includes a few other Azure services which come in handy when working with Synapse Analytics, such as Azure Data Vault for handling authentication, Azure SQL Database for dealing with smaller datasets and Azure Databricks as an improved Spark engine.
This course focusses on developers and administrators who are considering migrating existing data solutions to the Microsoft Azure cloud, or start designing new data oriented solutions in the Azure cloud. Some familiarity with relational database systems such as SQL Server is handy. Prior knowledge of Azure is not required.