Microsoft Fabric is a comprehensive and unified analytics platform that brings together various data services into a single ecosystem. It enables organizations to efficiently handle data engineering, analytics, machine learning, and business intelligence. Below, we explore the key terms and tools used in Microsoft Fabric to help you understand its capabilities. 1. Core Components Lakehouse A hybrid data storage architecture that merges the features of data lakes and data warehouses to provide scalable and flexible data management. Data Warehouse A structured data storage solution optimized for SQL-based analytics, supporting high-performance queries and data modeling. Data Factory A powerful ETL (Extract,… Read more
Tag: Data warehouse
SSAS Terminology
SSAS (SQL Server Analysis Services) is a Microsoft BI (Business Intelligence) technology used for online analytical processing (OLAP) and data mining. Here are some important SSAS terminology:
Data Warehouse Architecture & Components
Data warehouse architecture is the design and building blocks of the modern data warehouse. With the evolution of technology and demands of the data-driven economy, multi-cloud architecture allows for the portability to relocate data and workloads as the business expands, both geographically and among the major cloud vendors such as Amazon and Microsoft. There are three approaches to constructing a data warehouse: Bottom Tier The bottom tier or data warehouse server usually represents a relational database system. Back-end tools are used to cleanse, transform and feed data into this layer. Middle Tier The middle tier represents an OLAP server that can… Read more
Characteristics of Data Warehouse
Data Warehouse Concepts have the following characteristics: Subject-Oriented : A data warehouse is subject-oriented since it provides topic-wise information rather than the overall processes of a business. Such subjects may be sales, promotion, inventory, etc. For example, if you want to analyze your company’s sales data, you need to build a data warehouse that concentrates on sales. Such a warehouse would provide valuable information like ‘who was your best customer last year?’ or ‘who is likely to be your best customer in the coming year?’ Integrated : A data warehouse is developed by integrating data from varied sources into a… Read more
OLTP & OLAP
OLAP stands for Online Analytical Processing. OLAP systems have the capability to analyze database information of multiple systems at the current time. The primary goal of OLAP Service is data analysis and not data processing. OLTP stands for Online Transaction Processing. OLTP has the work to administer day-to-day transactions in any organization. The main goal of OLTP is data processing not data analysis. ONLINE TRANSACTION PROCESSING SYSTEM (OLTP) EXAMPLE: ONLINE ANALYTICAL PROCESSING SYSTEM (OLAP) EXAMPLE: DIFFERENCE BETWEEN OLTP AND OLAP Data: Transaction: Normalization: Queries: Integrity:
What is Data warehouse
A data warehouse is a centralized repository of digitally stored business information used to drive reporting and data analysis. It consists of massive quantities of data extracted from numerous sources, such as relational databases, internal applications, transactional systems, and external partner systems, aggregated into unified, well-structured historical data. Data warehouse is usually populated with data from an operational database, which contains transactions. The process of populating the data warehouse is called Extract, Transform, and Load (ETL). This process cleans, transforms, and loads the data into the data warehouse. Once the data is in the data warehouse, it can be used for reporting… Read more
Slowly Changing Dimensions (SCD) Types
Slowly Changing Dimensions in Data Warehouse is an important concept that is used to enable the historic aspect of data in an analytical system. As you know, the data warehouse is used to analyze historical data, it is essential to store the different states of data. In data warehousing, we have fact and dimension tables to store the data. Dimensional tables are used to analyze the measures in the fact tables. In a data environment, data is initiated at operational databases and data will be extracted-transformed-loaded (ETL) to the data warehouse to suit the analytical environment. Customer, Product are examples… Read more
Types of Dimensions in Data warehouse
What is Dimension? Dimension table contains the data about the business. The primary keys of the dimension tables are used in Fact tables with Foreign key relationship. And the remaining columns in the dimension is normal data which is the information about the Objects related to the business.Eg: Product,Customer,Orders,Company,Date etc. Slowly changing dimensions refer to how data in your data warehouse changes over time. Slowly changing dimensions have the same natural key but other data columns that may or may not change over time depending on the type of dimensions that it is. Slowly changing dimensions are important in data… Read more
Star Schema vs Snowflake Schema: differences
What is a star schema? A star schema is a database schema used to store data in a star format. This schema consists of a central table, called the “fact table,” and a number of directly connected other tables, called “dimension tables.” The fact table contains information about metrics or measures, while the dimension tables contain information about descriptive attributes. The star schema is very simple and easy to understand, making it ideal for cloud data warehousing and business intelligence applications. What is a snowflake schema? A snowflake schema is a type of database schema that is used to store data in a more… Read more