DP-3012: Implementing a Data Analytics Solution with Azure Synapse Analytics
- Duration: 1 Day (8 Hours)
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Prerequisites
DP-3012: Implementing a Data Analytics Solution with Azure Synapse Analytics Course Overview
Master unified data analytics with DP-3012: leverage Azure Synapse Analytics for seamless data warehousing, big data processing, and powerful insights. Build efficient ETL pipelines, explore serverless options, and gain hands-on experience. Perfect for data engineers, analysts, architects & data enthusiasts – enroll now and unlock your data’s full potential!
Intended Audience
- Data Engineers
- Data Analysts
- Data Scientists
- Database Administrators
- Business Intelligence Developers
- Cloud Solution Architects
- IT Managers and Decision Makers
- Software Developers
- Data Warehouse Developers
- Anyone interested in learning about modern data analytics technologies and tools.
Learning Objectives of DP-3012: Implementing a Data Analytics Solution with Azure Synapse Analytics
- Master Azure Synapse Analytics architecture and key concepts.
- Build data pipelines with Synapse Pipelines.
- Leverage dedicated SQL pools & serverless Spark pools for data warehousing & big data analysis.
- Develop data models and perform SQL queries for analysis.
- Analyze data with Spark and Delta Lake.
- Visualize & report data using Power BI.
- Monitor & optimize data pipelines for performance.
- Design & build data warehouse models (star/snowflake schemas).
- Load data efficiently into dedicated SQL pools.
- Perform complex queries on large data sets.
- Manage & secure Synapse Analytics data warehouses.
- Process large data sets with serverless Spark pools.
- Utilize Spark SQL & DataFrames for data exploration & transformation.
- Implement Delta Lake for reliable data storage & version control.
- Work with streaming data using Synapse SQL Streaming.
- Integrate machine learning models with Spark MLlib & other frameworks.
- Preprocess & prepare data for machine learning tasks.
- Train & evaluate machine learning models within Synapse Analytics.
- Deploy & manage machine learning models in production.
- Understand the business value of data analytics & big data projects.
- Learn best practices for building & deploying data solutions with Synapse Analytics.
- Prepare for data engineer, analyst, & architect roles using Azure Synapse Analytics.
Introduction to Azure Synapse Analytics
- Identify the business problems that Azure Synapse Analytics addresses.
- Describe core capabilities of Azure Synapse Analytics.
- Determine when to use Azure Synapse Analytics.
Use Azure Synapse serverless SQL pool to query files in a data lake
- Identify capabilities and use cases for serverless SQL pools in Azure Synapse Analytics
- Query CSV, JSON, and Parquet files using a serverless SQL pool
- Create external database objects in a serverless SQL pool
Analyze data with Apache Spark in Azure Synapse Analytics
- Identify core features and capabilities of Apache Spark.
- Configure a Spark pool in Azure Synapse Analytics.
- Run code to load, analyze, and visualize data in a Spark notebook.
Use Delta Lake in Azure Synapse Analytics
- Describe core features and capabilities of Delta Lake.
- Create and use Delta Lake tables in a Synapse Analytics Spark pool.
- Create Spark catalog tables for Delta Lake data.
- Use Delta Lake tables for streaming data.
- Query Delta Lake tables from a Synapse Analytics SQL pool.
Analyze data in a relational data warehouse
- Design a schema for a relational data warehouse.
- Create fact, dimension, and staging tables.
- Use SQL to load data into data warehouse tables.
- Use SQL to query relational data warehouse tables.
DP-3012: Implementing a Data Analytics Solution with Azure Synapse Analytics Course Prerequisites
- Basic understanding of data warehousing and business intelligence concepts.
- Experience with SQL queries and relational databases.
- Familiarity with cloud computing concepts and Azure fundamentals.
- Some experience with data pipelines and ETL processes.
- (Optional, but beneficial)
- Experience with Apache Spark or similar big data processing frameworks.
- Familiarity with Python or another programming language.
- Knowledge of machine learning fundamentals.
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