From RDBMS to MongoDB
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The document discusses the evolution of databases from relational databases to modern databases like MongoDB. Some key points discussed are: - The volume, variety and velocity of data being collected is increasing rapidly each year, creating challenges for relational databases. - Modern databases like MongoDB are designed to handle massive amounts of data from multiple sources more efficiently through improved speed, capacity and accuracy. - Case studies show that companies like MetLife, Shutterfly and Telefonica were able to build applications faster, reduce costs and improve performance by switching to MongoDB from their relational database implementations.
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From RDBMS to MongoDB
- 1. Conclusion 99.99% decrease in cost of storage From RDBMS to MongoDB The Evolution of the Modern Database The Increasing Three V’s of Data Velocity, Variety, and Volume The Decreasing Storage cost Cost per 1GB of storage 90%of data is unstructured (IDC) This very second as you are reading this infographic, data is being collected from all around the world. Organizations that have depended solely on relational databases are now focusing on modernizing their databases; adjusting to the changes. With massive amounts of data flowing in from multiple sources, modern databases are evolving to improve their capacity, speed, and accuracy. Now Modern Database Then Relational Database C1 C2 C3 C4 C5 So, how well can MongoDB handle all this data? How can this financially benefit my business? How can this financially benefit my business? But wouldn’t all this be so costly? The Increasing cost of Development Resources Infrastructure vs. Developer cost But developers are so expensive to hire.... Want more information? www.mongodb.com © 2015 MongoDB, Inc. All Rights Reserved 2009 Worldwide Enterprise Data Growth 20142004 Unstructured data Structured data Data growing at 40%annually. (IDC) MetLife tried to consolidate 70 legacy systems into a single record for years. The project had no end in sight. Problem MetLife’s 360 degree view of their customers, the Wall, now allows them to minimize churn by improving customer service. Conclusion $437,000 1980’s $0.05 2014 $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ ¢ 99.99% decrease in storage cost MongoDB’s dynamic schema and ease of use increases developer productivity and improves time to market by 5x to 10x. Shutterfly’s hardware costs remained high with their RDBMS implementation, features took too long to build and site performance suffered. Problem Shutterfly was able to take advantage of commodity infrastructure to cut costs and improve performance. Conclusion SolutionWith MongoDB Telefonica tried to build a personalization server for millions of user profiles with 20 technologists for 15 months, but failed to meet new performance requirements. Problem Telefonica joins the long list of companies building applications better and faster with MongoDB. Conclusion SolutionWith MongoDB 3.5 mo Implementation took 1/4 of the time it originally took. 10 devs Telefonica was able to build a new version with half the number of developers they started off with. 80% Costs invested in data storage was reduced by 80% by scaling out on commodity servers. 9x Shutterfly were able to increase its performance 9 times better. SolutionWith MongoDB 90 days It took only a few months to take their app into production. 2 weeks MetLife built an app protype in just 14 days. 1985 2014 Developer cost Infrastructure cost Let’s see the break down of modern data x10 $$$ $