At IBM, we’re proud of our decades of industry leadership in the database space—inventing SQL, pioneering relational databases like Db2 and Informix for high performance data analytics, and helping drive forward open-source technologies such as Apache, Eclipse, Java, Python and Hadoop, to name just a few.

But the modern data landscape demands more than one type of database. That’s why IBM has rolled out JSON-document-based databases in Db2 and Cloudant, as well as partnered with select database providers to offer developer-focused database services through the IBM Compose platform.

We’ve also been working closely with industry partners such as Hortonworks and EnterpriseDB to extend the enterprise capabilities of our data platforms. We recently announced some more big news on that front. With the release of IBM Data Management Platform for MongoDB Enterprise Advanced, the MongoDB JSON-document-store database is now part of an integrated data management environment from IBM.

Why integrate MongoDB? To give new and existing clients more options for capabilities within the IBM family. The data landscape in 2018 is fast and fluid, with new types of data in ever-increasing volumes. Relational databases such as Db2 and Informix play a key role in this data landscape, but more and more customers are implementing hybrid database environments that include both relational and NoSQL components. The meteoric rise of MongoDB in recent years shows that there’s substantial demand for a flexible, JSON-document-based datastore in today’s data economy.

If you think about it, that makes perfect sense. The industry demands applications that make use of data quickly and can be adapted as needs change by the week or the day. Developers want to stash their data somewhere, often before they know what it will be or what it can do. JSON-document-based databases fit more naturally with those needs. By adding MongoDB to the IBM ecosystem, we’re able to offer the best of both formats and help them work together.

The case for adding NoSQL

Document-based NoSQL databases like MongoDB have proliferated to support rapid deployment, agile processes and working with data even before the data schema is understood.

As MongoDB themselves put it, the document-based data model addresses several issues that the relational model is not designed to address:

  • Large volumes of rapidly changing structured, semi-structured, and unstructured data
  • Agile sprints, quick schema iteration, and frequent code pushes
  • API-driven, object-oriented programming that is easy to use and flexible
  • Geographically distributed scale-out architecture instead of expensive, monolithic architecture

NoSQL databases also give developers different options for data virtualization and running on multiple platforms, such as x86 and System z.

Put another way, NoSQL databases give modern enterprises a new, compelling set of capabilities to complement the known strengths of relational databases. If you want to run complex analytics on your data, a relational environment with SQL queries will probably give you more thorough results.

Compliance for atomicity, consistency, isolation and durability (ACID) is much easier — or sometimes only possible — with relational systems. For extremely large or complex data stores, relational databases offer unmatched configuration flexibility and robustness with high availability and linear scalability. And of course, enterprises that already run relational infrastructure have the expertise and experience to add more capabilities through relational means.

The value of an integrated environment

The current data landscape demands more than an either/or approach. As analyst Jackie Barre puts it, relational databases “must be able to cohabit with NoSQL.” There’s tremendous value to an integrated development environment (IDE). When building this IDE, you should “select tools that do one thing brilliantly and can be linked together to automate the important processes within the database lifecycle.”

Consider, for example, enterprise resource planning (ERP), a standard for relational databases. What if you want to offer ERP forms users can actually modify if they need to? A document-based NoSQL database such as MongoDB can provide that functionality without requiring you to rebuild your whole data schema every time a user wants to change the data format.

This small example hints at exactly what IBM and MongoDB are trying to offer with this combined ecosystem. This new offering accelerates the deployment of scalable, flexible MongoDB and offers a more robust, security-rich suite of database deployment management tools to your organization. By deploying MongoDB within an IBM ecosystem, you get much more than the NoSQL database itself. Additional features such as Compass, a MongoDB GUI, and OpsManager add simplicity in managing large MongoDB deployments.

But most importantly, when MongoDB is part of the IBM ecosystem, it benefits from the IBM commitment to support both the database and its integration with your other IBM systems. IBM data federation capabilities, including the common SQL engine that underpins all our Db2 offerings, offer a flexible and seamless portal for accessing your data wherever it’s stored.

If you want to run MongoDB on Power hardware or IBM Z, we can help. If you want to put MongoDB into your IBM Cloud Private for Data instance, we’ll be there. We’re committed to making the hybrid database architecture work for our clients, and we’re excited to see what uses our clients envision for these new tools.

There are scenarios that fit naturally into the strengths of NoSQL and relational databases, like when an enterprise uses MongoDB as a particular application’s operational datastore but runs their analytics from a relational datastore. However, hybrid database deployments can enable some surprising use cases that go beyond the obvious.

For example, Craigslist hosts its active listings in a SQL database, but is obligated to archive every bit of data that users post. Keeping this enormous archive in the same relational format as the active listings means that any improvement to the current data schema must be propagated through billions of old records, at enormous hassle and expense. By transferring their old listings into a document-based store, Craigslist can maintain its archiving obligations without the overhead. The document-based store takes full advantage of modern commodity storage and processing power. You wouldn’t necessarily think of a document-based datastore for a long-term cold storage, but in this context, paired with a relational front end, it makes perfect sense.

As data proliferates, it’s critical for clients who want to engage with the broad range of possible data environments to have options. The fact that IBM can now offer support for MongoDB on premises and in private cloud environments, as well on the cloud with IBM Compose offerings means that the solution is prepared to meet your needs across a variety of deployments, and we’re already planning to do even more.

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