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
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
2.7 Why Partitioning Data?
2.8 Consistency Levels in AzureCosmos DB
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
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
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)?
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
9.1 What is Azure Monitor?
9.2 What data does Azure Monitor collect?
9.3 What can you Monitor?
9.4 Alerts in Azure
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
Hands-on:
1. Azure SQL Database Auditing
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Fetch the list of videos from the attached dataset of YouTube channel with the highest views and likes to promote advertisements on the channel which has maximum traffic. Through this project, you will acquire a better understanding of Azure Data Factory, Azure Data Lake, Triggers, SQL, Power BI, etc.
Create an automated solution, using which a company will be able to see a live dashboard of the lead count. You will make use of Power BI Heat maps, Azure SQL instead of On-Premise SQL, and Data Factory to automate the data lifecycle from SQL to the BI tool.
In this project, you will be asked to get the real-time list of maximum traffic fetching videos from YouTube channels. Automate the transformation of the real-time video list from YouTube channels on a weekly basis. The traffic can be analyzed on various parameters on a particular day. You can get publicly available data from the YouTube API.
This case study will check your knowledge of non-relational databases: categories and where to use them. You will be working closely with NoSQL or Non-Relational Database, Azure Data Lake Storage, and its key components.