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Mainframes: A Corporate Mainstay

Mainframes have been a mainstay of banking, insurance, healthcare, and retail industries as well as the public sector where heavy-duty, low latency transaction processing is a critical requirement for 50 years. Mainframes are designed to provide mission-critical applications with high availability, where downtime would be extremely costly or simply unacceptable.

Migration Considerations

Mainframe applications are being left behind as businesses strive to transform themselves using distributed computing and public clouds. In large part, this is due to the perception that it is virtually impossible to modernize a mainframe application which often leads to companies rewriting mainframe applications on modern technologies which is expensive, time-consuming, and risky.

If your current IT infrastructure runs on a mainframe environment and your organizational charter includes building a new operational application or modernizing an existing one to migrate to the cloud, take advantage of new data sources and capabilities such as artificial intelligence and machine learning, then you should give a closer look at certain factors.

Key Migration Factors

Cost of Ownership

Mainframes represent significant upfront capital expenditures as well as high licensing and maintenance costs

Modern
Features

It’s challenging for mainframe-based environments to support modern AI and ML functionalities

Vendor Lock-In

A majority of mainframes are proprietary in nature which can lead to vendor lock-in

Trained Workforce

Personnel trained in COBOL, PL/1, CICS, VSAM, IMS is increasingly challenging to recruit


Migrating applications built for mainframe environments enable enterprises to keep pace with or even leapfrog the upstart competitors whose business is originally built on data and AI. When companies replace their mainframe infrastructure to a converged platform they instantly enhance the performance of their applications and give them the ability to simultaneously manage operational and analytical workloads. Companies are now in a position to take automated actions in real-time based on in-database machine learning.

Why Splice Machine

Splice Machine is a scalable SQL database that enables companies to modernize their legacy and custom applications to be agile, data-rich, and intelligent – all without re-writes. Splice Machine not only reduces database licensing costs but also enable the applications to add new data sources at a massive scale. Splice Machine enables enterprises to consolidate analytics and machine learning that used to be on separate platforms to be native to the application thereby reducing ETL latency and infrastructure costs.

Mainframe applications no longer are tied to existing legacy RDBMS systems like Oracle and IBM Db2. With Splice Machine now there is an opportunity to leapfrog the legacy database and modernize the application on a scale-out, HTAP database. By making Splice Machine your target database, you can easily add ML and AI to your mainframe application in a matter of months. Moreover you are also cloud independent as Splice works on any public cloud. If it is early on your cloud journey, Splice also works on-premises in a modern Kubernetes architecture.

Customer Example: Leading Financial Services Company

A leading financial services company migrated its global claim, client, & policy applications from an on-premise legacy environment to the Splice Machine platform in the Cloud. On a traditional RDBMS, it took too long to open up a new operating entity due to data center build-outs and the company was not able to use AI/ML models in the real-time application.

With Splice Machine, the financial services company will be able to expand into new geographical markets in record time without cloud vendor-lock-in and will be able to take intelligent predictive actions to lower fraud, AML, and litigation.

Ready for a Free Trial?

Try Splice Machine today for free for fourteen days. With this trial, you get an ACID SQL RDBMS with full analytical capabilities, integrated Jupyter notebooks, MLFlow experimentation tracking, Apache Spark, a variety of Machine Learning libraries pre-integrated like SciKit, MLlib, H20, and Keras, and affordable, elastic scalability.