Enterprises need information architectures capable of aggregating, analyzing, automating and managing multiple types of data, including semi-structured and unstructured data from sources like Internet of Things (IoT) devices. With access to more robust data sets, organizations can  take full advantage of artificial intelligence (AI) to inform more complete models and generate deeper insights.

For this reason, many organizations have begun to employ multiple databases. SQL databases designed to provide scheduled reporting and data storage are now augmented with NoSQL databases capable of capturing the semi-structured and unstructured data required for more holistic insights.

Choosing the best SQL or NoSQL database

Before choosing one – or several – databases, consider what makes each unique. Structured query language (SQL) databases use a predefined schema in a table-based, 2-dimensional column/row structure. Generally best suited for multi-row transactions including historical accounting or for legacy systems built for relational data, SQL databases work best when there is a need for ACID compliance (atomicity, consistency, isolation, durability), or when data is structured and unchanging.

NoSQL databases are non-relational and document-based, providing storage and retrieval of data by key-value, graph, or wide column stores. The NoSQL model is highly flexible, allowing the storage of any type of data without the need for the predefined schema. This flexibility is valuable when creating data records, querying document collections, and analyzing large quantities of information.

Optimizing a multi-database architecture

Whether an organization chooses to use a SQL database, a NoSQL database, or, mostly likely, a combination of the two, it must optimize the overall architecture to address several rising challenges.

Managing the information architecture and accessing the data must be simplified so that DBAs and IT architects spend their time on value-add activities rather than routine maintenance, patches and backups. Data scientists should not waste valuable time pulling together siloed data; they should have access to a single view of data no matter its format or where it resides. Only by simplifying both access and format can enterprises achieve the speed and holistic data inclusion they need to support effective AI and machine learning models.

Enterprises also need to enable faster app development while driving lower costs to remain as competitive as possible. Achieving this requires the ability to stand up a database with the right amount of storage and integrate it quickly without sacrificing effective database management.

Finally, scalability, availability, and security must be maintained to ensure the ability to access and ingest increasing volumes of data from various sources while remaining compliant with regulations. A variety of hybrid deployment options are needed so that organizations are able to select the location that is most beneficial for each database. This extends beyond just on-premises and cloud and should include multi-cloud deployment capabilities across vendors as well. In this way, companies will have the flexibility to choose whichever option has the best scalability, security, and availability depending on their needs.

MongoDB meets the multi-database challenge

MongoDB capably meets each of these challenges. An open-source NoSQL database, MongoDB provides an elastic data model that enables users to store and query multivariate data types with ease. This not only simplifies database management for developers but also creates a highly scalable environment for applications and services. MongoDB uses documents or collections of documents as its basic unit of data. Formatted as Binary JSON (JavaScript Object Notation), these documents can store various types of data and be distributed across multiple systems.

MongoDB Atlas is a database service now available from IBM^®. An open source-based global cloud database managed service designed to handle all the complexity of deploying, managing, and healing deployments, MongoDB Atlas is cloud-agnostic, compatible with cloud service providers including Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform. This MongoDB managed service focuses development and delivery teams away from the complex DBA and production support tasks most databases usually require. Across industries including Healthcare, Financial Services, and Communications, use cases include:

• Simplifying administration – Patching for the OS and database, lifecycle management, backup, and system hardening are managed so downtime is minimized and DBAs within the company can focus on tasks that are more likely to propel the business forward.
• Scalability and cost – Virtual machines in the cluster grow according to the load with the ability to automatically scale up and down with cyclical and seasonal variations. The as-a-service model provides pay-as-you-go options to optimize cost.
• Performance, security, and monitoring – Managed database services provide automated, centralized network services and applications, including setup, management, and protection for your business so you can focus on market-differentiating solutions.

Open Source NoSQL database with enterprise support

Even with their many advantages, open source solutions will require enterprise-grade security and support to succeed. IBM offers the services and multivendor support to help you choose, implement and protect the right database solution for the needs of your organization across on-premises, cloud and multi-cloud environments. IBM multivendor support ensures you make a single call to get the answers you need about your open source databases, Db2® and other IBM products related to data management and beyond.

Learn more about MongoDB Atlas with IBM by visiting the IBM Open-Source community or book a consultation with an IBM expert to discuss your needs related to MongoDB on-premises or as a managed service.