Data Warehouse
A Data warehouse is a database that is used for data analysis and reporting. It is a central repository for all the data that is needed for reporting and analysis. In this course you will learn everything about the Data warehouse. Data and analytics have become indispensable to businesses to stay competitive. Business users rely on reports, dashboards, and analytics tools to extract insights from their data, monitor business performance, and support decision making. Data warehouses power these reports, dashboards, and analytics tools by storing data efficiently to minimize the input and output (I/O) of data and deliver query results quickly to hundreds and thousands of users concurrently.
What you’ll learn
- We really hope this course will help you with your career immensely and take you to the next level.
- Assessment questionnaires are included at appropriate points to test your knowledge.
- Important sessions of this course are Transactional vs Analytical Processing, ETL.
- Data Warehouse Architecture, Data Mart, Data Warehouse Schema and SCDs.
Course Content
- Introduction –> 30 lectures • 2hr 13min.
Requirements
A Data warehouse is a database that is used for data analysis and reporting. It is a central repository for all the data that is needed for reporting and analysis. In this course you will learn everything about the Data warehouse. Data and analytics have become indispensable to businesses to stay competitive. Business users rely on reports, dashboards, and analytics tools to extract insights from their data, monitor business performance, and support decision making. Data warehouses power these reports, dashboards, and analytics tools by storing data efficiently to minimize the input and output (I/O) of data and deliver query results quickly to hundreds and thousands of users concurrently.
A data warehouse architecture is made up of tiers. The top tier is the front-end client that presents results through reporting, analysis, and data mining tools. The middle tier consists of the analytics engine that is used to access and analyze the data. The bottom tier of the architecture is the database server, where data is loaded and stored. Data is stored in two different types of ways: 1) data that is accessed frequently is stored in very fast storage (like SSD drives) and 2) data that is infrequently accessed is stored in a cheap object store, like Amazon S3. The data warehouse will automatically make sure that frequently accessed data is moved into the “fast” storage so query speed is optimized.
A data warehouse may contain multiple databases. Within each database, data is organized into tables and columns. Within each column, you can define a description of the data, such as integer, data field, or string. Tables can be organized inside of schemas, which you can think of as folders. When data is ingested, it is stored in various tables described by the schema. Query tools use the schema to determine which data tables to access and analyze.
A data warehouse is specially designed for data analytics, which involves reading large amounts of data to understand relationships and trends across the data. A database is used to capture and store data, such as recording details of a transaction.
Here is what you’ll learn:
- Data Warehouse Basics
- History of Data Warehouse
- OLTP & OLAP
- Introduction to ETL
- Extraction, Transformation and Load data in ETL
- Types of Transformation
- Data Warehouse Architecture
- Data Warehouse Architectures based on layers and Tires
- Data Mart
- Storing Data in Data Warehouse
- Dimension Tables and Fact Tables
- Dimension and Fact table with Example
- DWH Schema
- Star Schema
- Snowflake Schema
- Fact Constellation Schema
- SCD Introduction
- SCD Type 0 and 1
- SCD Type 2
- SCD Type 3
- SCD type 4
- SCD Type6
- ETL Tools
- Scheduling the Tools
- Reporting Tools