Understanding Azure Data Lake Exploring Synapse Studio
Module 2: Consideration For Your Compute Environment
Technical requirements Introducing SQL Pool
Creating SQL Pool
Understanding Synapse SQL Pool
Architecture and component
Examining DWUs
Understanding distribution in Synapse SQL Pool
Understanding portions in Synapse SQL Pool
Using temporary table in Synapse SQL Pool
Discovering the benefits of Synapse SQL Pool
Understanding Synapse SQL on_demand
SQL on-demand architecture and components
Learning about the benefits of Synapse SQL on-demand
Module 3: Bringing Your Data To Azure Synapse
Technical requirements
Using Synapse pipelines to import data
Bringing data to your Synapse SQL Pool using Copy Data tool
Using Azure Data Factory to import data
Using SQL Server integration Services to import data
Module 4: Using Synapse Pipelines To Orchestarte Your Data
Technical requirements
Introducing synapse pipe lines
Integration runtime
Activities
Pipelines
Triggers
Creating linked services
Defining source and target
Using various activities in synapse pipelines
Scheduling synapse pipelines
Creating pipelines using samples
Module 5: Using Synapse With Azure Cosmos Db
Technical requirements
Enabling the analytical store in Cosmos DB
Data storage
Transactional store
Analytical store
Querying the Cosmos DB analytical store
Querying with Azure Synapse Spark
Querying with Azure Synapse SQL Serverless
Module 6: Working With T-sql In Azure Synapse
Technical requirements
Supporting T-SQL language elements in a Synapse SQL pool
CTEs
SELECT – OVER clause
Using dynamic SQL in Synapse SQL
Learning GROUP BY options in Synapse SQL
Using T – SQL loops in Synapse SQL
Stored procedures
Views
Optimizing transactions in Synapse SQL
Supporting system views in a Synapse SQL Pool
Using T – SQL queries on semi-structured and unstructured data
Reading Parquet files
Reading JSON documents
External tables
Module 7: Working With R, Python, .net, And Spark Sql In Azure
Technical requirements
Using Azure Open Datasets
Using Sample Scripts
Pyspark (Python)
Spark (Scala)
.NET Spark (C#)
Module 8 : Integrating A Power Bi Workspace With Azure Synapse
Technical requirements
Connecting to a Power BI Workspace
Creating your own dashboard on Azure Synapse
Creating new Power BI datasets
Creating Power BI reports
Connecting Azure Synapse Data to Power BI Desktop
Connecting to a Synapse-dedicated SQL Pool
Connecting to a Synapse Serverless SQL Pool
Module 9: Perform Real-time Analytics On Streaming Data
Technical requirements
Understanding various Architecture and Components
Bringing data to Azure Synapse
Using Azure stream analytics
Using Azure Databricks
Implementation of real-time analytics on streaming data
Ingest data to Cosmos DB
Accessing data from the Azure Cosmos DB analytics store in Azure Synapse
Loading data to a Spark Data Frame
Creating Visualizations
Module 10: Generating Powerful Insights On Azure Using Azure Ml
Technical requirements
Preparing the environment
Creating a Text Analytics resource in the Azure portal
Creating an Anomaly Detector resource in the Azure portal
Creating an Azure key vault
Creating an Azure ML linked Service in Azure Synapse
Machine learning capabilities in Azure Synapse
Data ingestion and orchestration
Data preparation and exploration
Training machine learning models
Use cases with Cognitive Services
Sentiment analytics
Anomaly detection
Module 11: Performing Backup And Restore In Azure Synapse
Analytics
Technical requirements
Creating restore points
Automatic restore points
User-defined restore points
Geo-backup and disaster recovery
Geo-redundant restore through the Azure portal
Geo-redundant restore through powerShell
Cross-subscription restore
Module 12: Securing Data On Azure Synapse
Implementing network security
Managed workspace virtual network
Private endpoint for SQL on-demand
IP firewall rules
SQL authorization
Azure Active Directory authorization
Implementing RBAC in a Synapse SQL Pool
Enabling threat protection
Azure SQL auditing
Azure Defender for SQL
Understanding information protection
Module 13: Managing And Monitoring Synapse Workloads
Technical requirements
Managing Synapse resources
Analytical Pools
External Connections
Integration
Security
Source control
Monitoring Synapse workloads
Integration
Activities
Analytics Pools
Managing maintenance schedules
Creating alerts for Azure Synapse Analytics
Module 14: Coding Best Practices
Technical requirements
Implementing best practice for a Synapse dedicated SQL pool
Maintaining statics
Using correct distribution for your tables
Using partitioning
Using an adequate column size
Advantage of using a minimum transaction size
Using PolyBase to load data
Reorganization rebuilding indexes
Materialization views
Using an appropriate resource class
Implementing best practice for a Synapse serverless SQL pool
Selecting the region to create a serverless SQl pool
File for querying
Using CETAS to enhance query performance
Implementing best practice for a Synapse Spark pool
Configuring the Auto-pause setting
Enhance Apache Spark performance
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