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: