[PL] Data Engineering on Microsoft Azure DP-203 + voucher
Description
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.
For whom?
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 data analysts and data scientists who work with analytical solutions built on Microsoft Azure.
Developed skills
- 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
Learning form
Classroom training in Elitmind Academy office (Grzybowska 87, 00-844 Warsaw)
Duration
4 days
Exam
This training prepares for the exam: DP-203: Data Engineering on Microsoft Azure
The price includes a voucher for the above exam
Agenda
Module 1: Explore compute and storage options for data engineering workloads
- Introduction to Azure Synapse Analytics
- Describe Azure Databricks
- Introduction to Azure Data Lake storage
- Describe Delta Lake architecture
- Work with data streams by using Azure Stream Analytics
- Lab: Explore compute and storage options for data engineering workloads
Module 2: Design and implement the serving layer
- Design a multidimensional schema to optimize analytical workloads
- Code-free transformation at scale with Azure Data Factory
- Populate slowly changing dimensions in Azure Synapse Analytics pipelines
- Lab: Designing and Implementing the Serving Layer
Module 3: Data engineering considerations for source files
- Design a Modern Data Warehouse using Azure Synapse Analytics
- Secure a data warehouse in Azure Synapse Analytics
- Lab: Data engineering considerations
Module 4: Run interactive queries using Azure Synapse Analytics serverless SQL pools
- Explore Azure Synapse serverless SQL pools capabilities
- Query data in the lake using Azure Synapse serverless SQL pools
- Create metadata objects in Azure Synapse serverless SQL pools
- Secure data and manage users in Azure Synapse serverless SQL pools
- Lab: Run interactive queries using serverless SQL pools
Module 5: Explore, transform, and load data into the Data Warehouse using Apache Spark
- Understand big data engineering with Apache Spark in Azure Synapse Analytics
- Ingest data with Apache Spark notebooks in Azure Synapse Analytics
- Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics
- Integrate SQL and Apache Spark pools in Azure Synapse Analytics
- Lab: Explore, transform, and load data into the Data Warehouse using Apache Spark
Module 6: Data exploration and transformation in Azure Databricks
- Describe Azure Databricks
- Read and write data in Azure Databricks
- Work with DataFrames in Azure Databricks
- Work with DataFrames advanced methods in Azure Databricks
- Lab: Data Exploration and Transformation in Azure Databricks
Module 7: Ingest and load data into the data warehouse
- Use data loading best practices in Azure Synapse Analytics
- Petabyte-scale ingestion with Azure Data Factory
- Lab: Ingest and load Data into the Data Warehouse
Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines
- Data integration with Azure Data Factory or Azure Synapse Pipelines
- Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines
- Lab: Transform Data with Azure Data Factory or Azure Synapse Pipelines
Module 9: Orchestrate data movement and transformation in Azure Synapse Pipelines
- Orchestrate data movement and transformation in Azure Data Factory
- Lab: Orchestrate data movement and transformation in Azure Synapse Pipelines
Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse
- Optimize data warehouse query performance in Azure Synapse Analytics
- Understand data warehouse developer features of Azure Synapse Analytics
- Lab: Optimize Query Performance with Dedicated SQL Pools in Azure Synapse
Module 11: Analyze and Optimize Data Warehouse Storage
- Analyze and optimize data warehouse storage in Azure Synapse Analytics
- Lab: Analyze and Optimize Data Warehouse Storage
Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
- Design hybrid transactional and analytical processing using Azure Synapse Analytics
- Configure Azure Synapse Link with Azure Cosmos DB
- Query Azure Cosmos DB with Apache Spark pools
- Query Azure Cosmos DB with serverless SQL pools
- Lab: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
Module 13: End-to-end security with Azure Synapse Analytics
- Secure a data warehouse in Azure Synapse Analytics
- Configure and manage secrets in Azure Key Vault
- Implement compliance controls for sensitive data
- Lab: End-to-end security with Azure Synapse Analytics
Module 14: Real-time Stream Processing with Stream Analytics
- Enable reliable messaging for Big Data applications using Azure Event Hubs
- Work with data streams by using Azure Stream Analytics
- Ingest data streams with Azure Stream Analytics
- Lab: Real-time Stream Processing with Stream Analytics
Module 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks
- Process streaming data with Azure Databricks structured streaming
- Lab: Create a Stream Processing Solution with Event Hubs and Azure Databricks
Module 16: Build reports using Power BI integration with Azure Synpase Analytics
- Create reports with Power BI using its integration with Azure Synapse Analytics
- Lab: Build reports using Power BI integration with Azure Synpase Analytics
Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics
- Use the integrated machine learning process in Azure Synapse Analytics
- Lab: Perform Integrated Machine Learning Processes in Azure Synapse Analytics