In this course, the student will learn about the data engineering patterns and practices as it pertains to working with batch and real-time analytical solutions using Azure data platform technologies. Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. They will then explore how to design an analytical serving layers and focus on data engineering considerations for working with source files. The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines. The students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how the data in an analytical system can be used to create dashboards or build predictive models in Azure Synapse Analytics.
- Price: $2,495.00
- Duration: 1 day
- Delivery Methods: Virtual
| Date | Time | Price | Option |
|---|---|---|---|
| Please contact us at info@toptalentlearning.com or 469-721-6100 for this course schedule. |
For questions call: (469) 721-6100
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1 – Introduction to data engineering on Azure
- What is data engineering
- Important data engineering concepts
- Data engineering in Microsoft Azure
2 – Introduction to Azure Data Lake Storage Gen2
- Understand Azure Data Lake Storage Gen2
- Enable Azure Data Lake Storage Gen2 in Azure Storage
- Compare Azure Data Lake Store to Azure Blob storage
- Understand the stages for processing big data
- Use Azure Data Lake Storage Gen2 in data analytics workloads
3 – Introduction to Azure Synapse Analytics
- What is Azure Synapse Analytics
- How Azure Synapse Analytics works
- When to use Azure Synapse Analytics
4 – Use Azure Synapse serverless SQL pool to query files in a data lake
- Understand Azure Synapse serverless SQL pool capabilities and use cases
- Query files using a serverless SQL pool
- Create external database objects
5 – Use Azure Synapse serverless SQL pools to transform data in a data lake
- Transform data files with the CREATE EXTERNAL TABLE AS SELECT statement
- Encapsulate data transformations in a stored procedure
- Include a data transformation stored procedure in a pipeline
6 – Create a lake database in Azure Synapse Analytics
- Understand lake database concepts
- Explore database templates
- Create a lake database
- Use a lake database
7 – Analyze data with Apache Spark in Azure Synapse Analytics
- Get to know Apache Spark
- Use Spark in Azure Synapse Analytics
- Analyze data with Spark
- Visualize data with Spark
8 – Transform data with Spark in Azure Synapse Analytics
- Modify and save dataframes
- Partition data files
- Transform data with SQL
9 – Use Delta Lake in Azure Synapse Analytics
- Understand Delta Lake
- Create Delta Lake tables
- Create catalog tables
- Use Delta Lake with streaming data
- Use Delta Lake in a SQL pool
10 – Analyze data in a relational data warehouse
- Design a data warehouse schema
- Create data warehouse tables
- Load data warehouse tables
- Query a data warehouse
11 – Load data into a relational data warehouse
- Load staging tables
- Load dimension tables
- Load time dimension tables
- Load slowly changing dimensions
- Load fact tables
- Perform post load optimization
12 – Build a data pipeline in Azure Synapse Analytics
- Understand pipelines in Azure Synapse Analytics
- Create a pipeline in Azure Synapse Studio
- Define data flows
- Run a pipeline
13 – Use Spark Notebooks in an Azure Synapse Pipeline
- Understand Synapse Notebooks and Pipelines
- Use a Synapse notebook activity in a pipeline
- Use parameters in a notebook
14 – Plan hybrid transactional and analytical processing using Azure Synapse Analytics
- Understand hybrid transactional and analytical processing patterns
- Describe Azure Synapse Link
15 – Implement Azure Synapse Link with Azure Cosmos DB
- Enable Cosmos DB account to use Azure Synapse Link
- Create an analytical store enabled container
- Create a linked service for Cosmos DB
- Query Cosmos DB data with Spark
- Query Cosmos DB with Synapse SQL
16 – Implement Azure Synapse Link for SQL
- What is Azure Synapse Link for SQL?
- Configure Azure Synapse Link for Azure SQL Database
- Configure Azure Synapse Link for SQL Server 2022
17 – Get started with Azure Stream Analytics
- Understand data streams
- Understand event processing
- Understand window functions
18 – Ingest streaming data using Azure Stream Analytics and Azure Synapse Analytics
- Stream ingestion scenarios
- Configure inputs and outputs
- Define a query to select, filter, and aggregate data
- Run a job to ingest data
19 – Visualize real-time data with Azure Stream Analytics and Power BI
- Use a Power BI output in Azure Stream Analytics
- Create a query for real-time visualization
- Create real-time data visualizations in Power BI
20 – Introduction to Microsoft Purview
- What is Microsoft Purview?
- How Microsoft Purview works
- When to use Microsoft Purview
21 – Integrate Microsoft Purview and Azure Synapse Analytics
- Catalog Azure Synapse Analytics data assets in Microsoft Purview
- Connect Microsoft Purview to an Azure Synapse Analytics workspace
- Search a Purview catalog in Synapse Studio
- Track data lineage in pipelines
22 – Explore Azure Databricks
- Get started with Azure Databricks
- Identify Azure Databricks workloads
- Understand key concepts
23 – Use Apache Spark in Azure Databricks
- Get to know Spark
- Create a Spark cluster
- Use Spark in notebooks
- Use Spark to work with data files
- Visualize data
24 – Run Azure Databricks Notebooks with Azure Data Factory
- Understand Azure Databricks notebooks and pipelines
- Create a linked service for Azure Databricks
- Use a Notebook activity in a pipeline
- Use parameters in a notebook
Learning Objectives
Explore compute and storage options for data engineering workloads in Azure
Design and Implement the serving layer
Understand data engineering considerations
Run interactive queries using serverless SQL pools
Explore, transform, and load data into the Data Warehouse using Apache Spark
Perform data Exploration and Transformation in Azure Databricks
Ingest and load Data into the Data Warehouse
Transform Data with Azure Data Factory or Azure Synapse Pipelines
Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines
Optimize Query Performance with Dedicated SQL Pools in Azure Synapse
Analyze and Optimize Data Warehouse Storage
Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
Perform end-to-end security with Azure Synapse Analytics
Perform real-time Stream Processing with Stream Analytics
Create a Stream Processing Solution with Event Hubs and Azure Databricks
Build reports using Power BI integration with Azure Synpase Analytics
Perform Integrated Machine Learning Processes in Azure Synapse Analytics
The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course includes data analysts and data scientists who work with analytical solutions built on Microsoft Azure.
Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions.
- AZ-900T00 Microsoft Azure Fundamentals
- DP-900T00 Microsoft Azure Data Fundamentals
