Nowadays, we’re living in the era of Big Data. It’s challenging to operate a business without accumulating and analyzing vast amounts of information. As a result, our lives are increasingly reliant on data storage and processing.
According to the research conducted by The Conversation, there are more than 59 zettabytes of data created, shared, and stored worldwide. One zettabyte (ZB) equals a trillion gigabytes. Yet, the experts expect the amount of data to increase three times by 2025. And all this information has to be stored and analyzed to get helpful insights.
In this post, we’ll take a deep dive into the realm of information. You will define a data mart, discover how it operates, and learn more about its essential characteristics.
GICT Certified Business Analytics course covers the concept of business analytics and its strategic importance to any organization.
It deals with principles, concepts, techniques and tools used in business analytics landscape which includes data mining, data warehouse, data mart and business intelligence.
Also, this training covers different types of business analytics with real life use cases including text analytics and web analytics.
Participants will get good picture of all these concepts and how they all are interconnected to each other in organizational context.
Data warehouse (DWH or DW) is a reliable technique used for data analysis and reporting.
Below enlisted are the most popular commercial and open source Data Warehousing Tools and Techniques:
Panoply is the only smart DW that simplifies and automates data management, data integration, and query performance optimization.
You can ingest data in minutes with only a few clicks from any source, thus, you need not depend on IT/Data engineering for the ETL process.
Panoply learns while it’s used, the queries are cached, saved and continuously optimized which saves time across all your reporting tasks of data analytics.
It is a simple, cost-effective, well-managed and fast DW tool that analyses data using the existing BI tools and standard SQL.