Description
Ronald Coase, a British economist and author, once said that you can make data say anything if you torture it enough. This quote takes on its full meaning in today's world where data control has become essential. This is why data engineering, a full-fledged data science discipline, is increasingly mentioned. Its objective is to select, sort, store, organize, test and secure data in order to guarantee their quality, availability and relevance for the teams who use them. This 4-day training allows participants to master data engineering based on Azure platform technologies. It also prepares for the Microsoft Certified Azure Data Engineer Associate certification.
Who is this training for ?
For whom ?
This training is aimed at data professionals, such as data architects and BI professionals, who want to deepen their knowledge of data engineering and creating analytical solutions using the technologies of the data platform. data available on Microsoft Azure. It is also suitable for data analysts and data scientists who work with analytical solutions based on Microsoft Azure.
Prerequisites
Have followed the training "Microsoft Azure - Fundamental notions" (MSAZ900)< /a> and "Microsoft Azure - Data Fundamentals " (MSDP900)or knowledge of cloud computing and basic data concepts and have hands-on experience with data solutions
Training objectives
Training program
- 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
- Working with data streams using Azure Stream Analytics
- Run interactive queries using Azure Synapse Analytics serverless SQL pools
- Explore the capabilities of Azure Synapse Serverless SQL Pools
- Query data in the lake using Azure Synapse Serverless SQL Pools
- Create objects metadata in Azure Synapse serverless SQL pools
- Secure data and manage users in Azure Synapse serverless SQL pools
- Data exploration and transformation in Azure Databricks
- Describe Azure Databricks
- Read and write data in Azure Databricks
- Use DataFrames in Azure Databricks
- Use advanced DataFrames methods in Azure Databricks
- Explore, transform, and load data into the data warehouse using Apache Spark
- Understand big data engineering with Apache Spark in Azure Synapse Analytics
- Integrate 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
- Integrate and load data into the data warehouse
- Use best practices for loading data into Azure Synapse Analytics
- Petabyte-scale ingestion with Azure Data Factory
- 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
- 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
- Orchestrate data movement and transformation in Azure Synapse Pipelines
- Orchestrate data movement and transformation in Azure Data Factory
- 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
- Support for 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
- Real-time stream processing with Stream Analytics
- Enable reliable messaging for big data applications using Azure Event Hubs
- Use data streams using Azure Stream Analytics
- Ingest data streams with Azure Stream Analytics
- Build a stream processing solution with Event Hubs and Azure Databricks
- Process streaming data with Azure Databricks structured streaming
