Pain point in the data warehouse

This post provides the list of issues that companies face when the size of the data grows exponentially in their data warehouse.

Our undertaking

One of the areas in Knowillence that we have built a good level of expertise in data warehouse migration to cloud. In this post I will share some details on what triggered the clients to initiate this data warehouse migration projects apart from financial reasons.

Transaction timeout

 The transactions executed from variety of applications started to put a big dent in the performance of transactions database. It is because the data getting into the database has a higher volume, velocity and variety compared to the past. This is often not tracked by the team and they upgrade the hardware and optimize the servers continuously until the law of diminishing returns kicks in. If you are getting timeout in transactions then it is high time you evaluate how to migrate your data warehouse cloud platform that provides auto-scaling (both upscaling and downscaling) and elasticity in processing power, memory usage and storage.

Reports timeout

The reports used by the business users and IT users starts to timeout. When a new project is launched with a new database then this database is used for both transactions and reporting. Over the years when the data grows exponentially then both transaction processing and report generation takes a big hit in performance. The main reason for this problem is that data in transaction database must be highly normalized for performance where are data in reporting database must be denormalized for performance. Hence using transaction database for reporting and reporting database for transaction will not yield good performance or good benefits.

Schedule driven processes

People don’t realize this as a problem until they start to think about real-time analytics. All the jobs in the database are driven by schedule and not by events. Companies start with schedule driven jobs because the are so few jobs and dependencies between then can be documented and easily tracked. When the number of jobs grow in the database server that’s when people lose track of dependencies and when they shuffle the schedule then they break the dependency between the jobs and it leads to chaos. It is better to build a decoupled system which is event driven using a powerful middleware or messaging system.

Next steps

If you have faced any of these issues and need help in migrating your data warehouse to cloud then reach out to us via the live chat or enter a comment and we will discuss possible solutions with you.


Founder & CEO

H.Thirukkumaran has over 20 years of experience in the IT industry. He worked in US for over 13 years for leading companies in various sectors like retail and ecommerce, investment banking, stock market, automobile and real estate He is the author of the book Learning Google BigQuery which explains how to build big data systems using Google BigQuery. He holds a masters in blockchain from Zigurat Innovation and Technology Business School from Barcelona Spain. He is also the India chapter lead for the Global Blockchain Initiative a non-profit from Germany that provides free education on blockchain. He currently lives in Chennai India.

Share This

Share This

Share this post with your friends!