1.1 Document data stores
1.2 Columnar data stores
1.3 Key/value data stores
1.4 Graph data stores
1.5 Time series data stores
1.6 Object data stores
1.7 External index
1.8 Why NoSQL or Non-Relational DB?
1.9 When to Choose NoSQL or Non-Relational DB?
1.10 Azure Data Lake Storage
- Definition
- Azure Data Lake-Key Components
- How it stores data?
- Azure Data Lake Storage Gen2
- Why Data Lake?
- Data Lake Architecture
2.1 Data Lake Key Concepts
2.2 Azure Cosmos DB
2.3 Why Azure Cosmos DB?
2.4 Azure Blob Storage
2.5 Why Azure Blob Storage?
2.6 Data Partitioning
- Horizontal partitioning
- Vertical partitioning
- Functional partitioning
2.7 Why Partitioning Data?
2.8 Consistency Levels in AzureCosmos DB
- Semantics of the five-consistency level
Hands-on:
1. Load Data fromAmazonS3 to ADLS Gen2 with Data Factory
2. Working with Azure Cosmos DB
3.1 Introduction to Relational Data Stores
3.2 Azure SQL Database
- Deployment Models
- Service Tiers
Hands-on:
- 1. Create a Single Database Using Azure Portal
- 2. Create a managed instance
- 3. Create an elastic pool
3.3 Why SQL Database Elastic Pool?
Hands-on:
1. Create a SQL virtual machine
2. Configure active geo-replication for Azure SQL Database in the Azure portal and initiate failover.
4.1 Azure SQL Security Capabilities
4.2 High-Availability and Azure SQL Database
- Standard Availability Model
- Premium Availability Model
- 4.3 Azure Database for MySQL
Hands-on:
1. Design an Azure Database for MySQL database using the Azure portal
2. Connect using MySQL Workbench
4.4 Azure Database for PostgreSQL
Hands-on:
- 1. Design an Azure Database for PostgreSQL – Single Server
4.5 Azure Database For MariaDB
Hands-on:
- 1. Create an Azure Database for MariaDB server by using the Azure portal
4.6 What is PolyBase?
4.7 What is Azure Synapse Analytics (formerly SQL DW)?
- SQL Analytics and SQL pool in Azure Synapse
- Key component of a big data solution
- SQL Analytics MPP architecture components
Hands-on:
- 1. Import Data From Blob Storage to Azure Synapse Analytics by Using PolyBase
5.1 What is Azure Batch?
5.2 Intrinsically Parallel Workloads
5.3 Tightly Coupled Workloads
5.4 Additional Batch Capabilities
5.5 Working of Azure Batch
Hands-on:
1. Run a batch job using Azure Portal
2. Parallel File Processing with Azure Bath using the .NET API
3. Render a Blender Scene using Batch Explorer
4. Parallel R Simulation with Azure Batch
6.1 Flow Process of Data Factory
6.2 Why Azure Data Factory
6.3 Integration Runtime in Azure Data Factory
6.4 Mapping Data Flows
Hands-on:
1. Transform data using Mapping data flows
7.1 What is Azure Databricks?
7.2 Azure Spark-based Analytics Platform
7.3 Apache Spark in Azure Databricks
Hands-on:
1. Run a Spark Job on Azure Databricks using the Azure portal
2. ETL Operation by using Azure Databricks
3. Stream data into Azure Databricks using Event Hubs
8.1 Working of Stream Analytics
8.2 Key capabilities and benefits
Hands-on:
1. Analyse phone call data with stream analytics and visualize results in Power BI dashboard
8.3 Stream Analytics Windowing Functions
- Tumbling window
- Hopping Window
- Sliding Window
- Session Window
9.1 What is Azure Monitor?
- Metrics
- Logs
- Metrics Vs Logs
9.2 What data does Azure Monitor collect?
9.3 What can you Monitor?
- Insights and Core Solutions
9.4 Alerts in Azure
- Flow of Alerts
- Key Attributes of an Alert Rule
- What can you set alert on?
- Manage alerts
- Alert States
- How to create an alert?
Hands-on:
1. Create, View, and Manage Metric alerts using Azure Monitor
2. Monitor your Azure Data Factory Pipelines proactively with Alerts
9.5 Azure Security Logging & Auditing