Data Modernization Essential Toolkit

Big data is driving the development of applications in today’s connected world. Organizations now need immediate support for instream processing of data using modern analytics platforms to develop use cases like fraud detection, health care services, and weather forecasts, among others. Earlier, the requirements were not demanding and the app development process was reactive as fewer sources generated data, which was then analyzed and processed to take actions.

Traditional technologies like relational databases and methodologies like waterfall development have been the default ways to build apps for many decades. However, these techniques are being pushed beyond their limits to keep up with the growth in data sources and user loads, coupled with the way applications are built and run today. The business needs to go faster — running in real-time — and today's demands exceed what is possible with 30+ year old technology. Relational databases do not support horizontal scaling and lack of performance in a distributed environment.

The need for data modernization

To meet the new data modernization requirements, applications should be able to process both structured and unstructured data from various sources and address the four Vs of data – Volume, Velocity, Variety, and Veracity. 

Digital-native competitors are disrupting established markets and out-innovating the incumbents by doing away with legacy processes and technology.

Tom Cruise

Forward-looking organizations are moving towards NoSQL database environment to be able to process large volumes of data even in a distributed hybrid cloud environment.

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Modernization procedures include the following steps:

• Scope identification - Identify the applications, data objects, and data for each object that needs to be modernized
• Data mapping – Map the data from source to target objects. If the source and target have different data models, transformation and mapping are essential for migration
• Migration - Perform data migration to the destination system using the selected criteria
• Validation – Perform audits, validations and acceptance tests to validate and certify data at a destination

A toolkit to make the data modernization task easier

An effective data modernization toolkit will help project teams to migrate from relational databases to NoSQL databases like MongoDB.

This modernization toolkit should have the following capabilities:

• Supports as-is and de-normalized database migration, thus preserving data integrity
• Defines object relationship during migration
• Enriches UI experience with almost zero manual intervention
• Enables faster migration with parallel processing
• Extends to any NoSQL database environment

We have adopted MongoDB to migrate few of our clients RDBMS systems. Modernizing with MongoDB, enterprises are becoming more and more intelligent by building new business functionality 3-5 times faster, scaling to millions of users wherever they are, and cutting costs by at least 70%.