DP-203 Data Engineering on Microsoft Azure Training Course
DP203 Training Course
The DP 203 Data Engineering on Microsoft Azure is an instructor-led, hands-on training course. This training covers how to interactively explore data stored in files in the data lake.
DP-203 provides information on how to build a real-time analytical framework to create real-time analytical solutions.
Knowledge from the courses below will be helpful:
- AZ-900 – Azure Fundamentals
- DP-900 – Microsoft Azure Data Fundamentals
There is no certifcation exam associated with DP-203 Data Engineering on Microsoft Azure course
Logitrain course material for DP-203T00: Data Engineering on Microsoft Azure provided
- Explore compute and storage options for data engineering workloads in Azure
- Run interactive queries using server less SQL pools
- Perform data Exploration and Transformation in Azure Data bricks
- Explore, transform, and load data into the Data Warehouse using Apache Spark
- 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
- 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 Data bricks
- Data Professionals and Data Architects and Business intelligence Professionals
- Data Analysts and Data Scientists
- 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
- Combine streaming and batch processing with a single pipeline
- Organize the data lake into levels of file transformation
- Index data lake storage for query and workload acceleration
- 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
- Query Parquet data with serverless SQL pools
- Create external tables for Parquet and CSV files
- Create views with serverless SQL pools
- Secure access to data in a data lake when using serverless SQL pools
- Configure data lake security using Role-Based Access Control (RBAC) and Access Control List
- Describe Azure Databricks
- Read and write data in Azure Databricks
- Work with DataFrames in Azure Databricks
- Work with DataFrames advanced methods in Azure Databricks
- Use DataFrames in Azure Databricks to explore and filter data
- Cache a DataFrame for faster subsequent queries
- Remove duplicate data
- Manipulate date/time values
- Remove and rename DataFrame columns
- Aggregate data stored in a DataFrame
- 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
- Perform Data Exploration in Synapse Studio
- Ingest data with Spark notebooks in Azure Synapse Analytics
- Transform data with DataFrames in Spark pools in Azure Synapse Analytics
- Integrate SQL and Spark pools in Azure Synapse Analytics
- Use data loading best practices in Azure Synapse Analytics
- Petabyte-scale ingestion with Azure Data Factory
- Perform petabyte-scale ingestion with Azure Synapse Pipelines
- Import data with PolyBase and COPY using T-SQL
- Use data loading best practices in Azure Synapse Analytics
- Data integration with Azure Data Factory or Azure Synapse Pipelines
- Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines
- Execute code-free transformations at scale with Azure Synapse Pipelines
- Create data pipeline to import poorly formatted CSV files
- Create Mapping Data Flows
- Orchestrate data movement and transformation in Azure Data Factory
- Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines
- Secure a data warehouse in Azure Synapse Analytics
- Configure and manage secrets in Azure Key Vault
- Implement compliance controls for sensitive data
- Secure Azure Synapse Analytics supporting infrastructure
- Secure the Azure Synapse Analytics workspace and managed services
- Secure Azure Synapse Analytics workspace data
- 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
- Configure Azure Synapse Link with Azure Cosmos DB
- Query Azure Cosmos DB with Apache Spark for Synapse Analytics
- Query Azure Cosmos DB with serverless SQL pool for Azure Synapse Analytics
- nable 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
- Use Stream Analytics to process real-time data from Event Hubs
- Use Stream Analytics windowing functions to build aggregates and output to Synapse Analytics
- Scale the Azure Stream Analytics job to increase throughput through partitioning
- Repartition the stream input to optimize parallelization
- Process streaming data with Azure Databricks structured streaming
- Explore key features and uses of Structured Streaming
- Stream data from a file and write it out to a distributed file system
- Use sliding windows to aggregate over chunks of data rather than all data
- Apply watermarking to remove stale data
- Connect to Event Hubs read and write streams
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