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. But 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. 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.
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
Wipro has collaborated with MongoDB to develop a data modernization toolkit called DigiTrek, which leverages Informatica PowerCenter and Informatica Intelligent Cloud Suite to help businesses automate the migration of data workloads from legacy systems to MongoDB.
This modernization toolkit includes:
- Certified Informatica tools which help facilitate the movement of data from relational databases to MongoDB
- Application modernization guide to help customers and partners identify which of their existing legacy applications are good candidates for moving to MongoDB
- Best practices on moving from RDBMS to MongoDB
- A fully-managed database as a service through MongoDB Atlas that plays an important role in ensuring the success of an application, thereby reducing TCO and allowing developers to get what they need on demand. Atlas also provides organizations with unique features and flexibility which they don’t get elsewhere with multi-cloud portability.
- A general-purpose foundational database software that has significant market adoption and extends beyond the transactional database to search, analytics, and to the edge — delivering a consistent developer experience and eliminating the need for a sprawl of disparate and siloed data technologies to handle different workloads
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%. Read here about how we are helping organizations across the globe in their journey to become Intelligent Enterprises, leveraging data, analytics, and artificial intelligence (AI).