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Db2: A Classic Database Not Suited for Modern Apps

IBM DB2® has been an important part of the enterprise IT infrastructure since the 90s for high availability and operational use cases. DB2 was originally built on mainframe technology and is now available on many platforms. However, legacy SQL databases such as DB2 do not have the ability to scale automatically across multiple nodes.

Cloud Migration Considerations

If you have an application powered by Db2 and you plan to migrate to the cloud you may be considering a few different options:

  • Migrate the application and Db2 to the cloud
  • Migrate the application to a cloud native RDBMS
  • Rewrite the application on a cloud native NoSQL platform.

We believe that none of these options are cost effective nor do they provide a foundation to extend the application with new data and AI. Here’s why:

Approach Cost Impact Modernization Limitation
Migrate Both App and Db2 Db2 is too expensive Can’t scale out
Migrate to cloud RDBMS Cloud RDBMS can’t provide analytics and need to be coupled with cloud data warehouses increasing database costs ETL limits real-time capabilities and no native ML
Rewrite to NoSQL Super expensive to re-write application because not only do you have to rewrite all the data access you have to write primitives that SQL has and NoSQL does not. NoSQL systems can not handle analytics

Key Factors of Db2 Modernization

Licensing Cost

High licensing cost is the primary consideration for companies to migrate from Db2

Scalability

It is difficult for Db2 to scale as the data volume increases because the size of the database is limited. Modern applications now store and analyze petabytes of data

Architectural Complexity

As the size and complexity of the environment grows, Db2 becomes harder to maintain and tune due to which management
costs increase rapidly

Duplicate Data For Analytics

To meet performance requirements, Db2 apps require additional Db2, Netezza, or Teradata data marts that results in data aggregation/ duplication and ultimately to latency in decision making


If you are building a new operational application or modernizing an existing one, then you should give a closer look at the factors above.

A Db2 Application Modernization should not just lift and shift the application to the cloud but should save costs by consolidating databases onto a scale-out platform and extend the application with new data and AI and machine learning.

In-Database Machine Learning

ML MANAGER: KEY BENEFITS

Support for Jupyter Notebooks: In Splice Machine 3.0 Jupyter notebooks are the standard. Splice Machine’s native Jupyter support comes with JupyterHub as well as BeakerX

Industry-Leading Libraries (Coming Soon): Access to the H2O Libraries, including deep learning TensorFlow integration, GLM, GBM, XGBoost, and AutoML

Ease of Use Across the Entire Product: Access to Apache Spark’s ML library, including algorithms, featurization, pipelines, persistence and utilities

Rapid Experimentation: MLflow to manage the experiments and model runs based on key parameters, versions and metrics

Seamless Deployment: MLflow packages models into Docker images, which are then deployable directly via Sagemaker for implementation

Superior Performance: Direct API between Splice Machine tables and Spark Data Frames for high performance

Take a look at ML Manager in this quick demo hosted by Ben Epstein, Machine Learning Engineer for Splice Machine, and click the button to access a full video demo.

Splice Machine: A Proven Replacement for Db2 that Delivers Lower Costs, Scale Out and Intelligence

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 enables the applications to add new data sources at a massive scale. Splice Machine enables enterprises to unify analytics and machine learning that used to be on separate platforms to be native to the application thereby reducing ETL latency and infrastructure costs.

Why Splice Machine Is Best for Powering Db2 Applications

With Splice Machine, there is an opportunity to leapfrog the legacy database and modernize the application on a scale-out, HTAP database that has native AI and in-database machine learning. Splice Machine 3.0 now supports many Db2-specific extensions that make it easy to migrate from Db2 with minimal SQL rewrite. Examples include support for Db2 trigger syntax, error codes, text manipulation syntax, etc.

 

Customer Example: Global Payments Processor

  • For a global payments processing company, Splice Machine migrated the existing SQL dispute resolution application from Db2 to Splice platform enabling millisecond queries on petabytes of payment history and live, up to the second streaming data.
  • Splice Machine’s platform resulted in vastly improved customer service and a new system that can scale out to meet future business needs.

Customer Example: Leading Insurance Company

  • A leading insurance company migrated its global claim, client, & policy applications from an on-premise Db2 environment to the Splice Machine platform in the Cloud. On Db2, 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 real-time application.
  • With Splice Machine, the insurance 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 the Splice Machine RDBMS today for free. With this trial, you get an ACID SQL RDBMS like Db2 but 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.