Enterprise data warehousing system for Financial Industry – Overview
- A data warehouse is a central repository of data that can be searched and examined to provide decision-makers with more accurate information. Business intelligence (BI) operations are supposed to be facilitated and supported by that type of data management system referred to as a “data store.”
- Data warehouses are solely designed to be utilized for queries and analysis, but frequently include a large amount of historical data.
- Transaction scripts and program log files are two common sources used by data warehouses.
- A data warehouse consolidates and consolidates large amounts of data from several sources. Organizations can leverage their analytical capabilities to enhance decision-making and obtain profitable business insights from their data.
- Throughout time, it generates a historical record that data scientists and business analysts may learn from. Because of these characteristics, a data warehouse may well be termed the “one point of information” for an enterprise.
The operation of a data warehouse
- A data warehouse could contain many datasets. In each database, data is organised into tables and columns.
- Each column can have a data description specified as a number, a data field, or a string. Databases that are clustered in schema, which you could think of as files, may include them.
- The many tables that the schema describes are where the data that is ingested is placed. The query tools choose which data tables to access and evaluate according on the schema.
Benefits of using a data warehouse
- Making wise decisions
- data compiled from a variety of sources
- analysis of historical data
- Data correctness, reliability, and quality
How we helped mortgage firm with our data warehousing system?
- Details about the mortgage data of the loan buyers. This data is from the end-user who got a mortgage loan from the bank.
- Since the data files are huge and have micro-level data, it was not possible to take the report of the loan details and processing.
- Downloaded end-user loan details using a scheduled job using SQL Runner Scripts tool.
- Built a pipeline to extract the downloaded file and upload it into multiple tables. Uploaded data files are moved to an archive folder.
- Designed Dimensions, HUB, and SAT tables using the Liquibase tool.
- Data uploaded to base tables are aggregated and validated using AWS Lambda functions.
- Build and deploy changes using AWS Code Commit and Code pipeline.
Market size: Data warehousing
The global data warehousing market size was valued at $21.18 billion in 2019, and is projected to reach $51.18 billion by 2028, growing at a CAGR of 10.7% from 2020 to 2028.