What is a Converged Database?

At the recent OOW European conference there was a lot talk about Converged Databases and how they can greatly simplify data-driven app development.

But if you missed the conference, you might find yourself wondering what exactly is a Converged Database and what is the difference between a Converged Database and an Autonomous Database?

So, I thought it would be a good idea to write a short blog post explaining what a Converged Database is and how it relates to the Oracle Autonomous Database.

A Converged Database is a database that has native support for all modern data types (JSON, Spatial, Graph, etc. as well as relational), multiple workloads (IoT, Blockchain, Machine Learning, etc.) and the latest development paradigms (Microservice, Events, REST, SaaS, CI/CD, etc.) built into one product.

By having support for each of these datatype, workloads, and paradigms as features within a converged database, you can support mixed workloads and data types in a much simpler way. You don’t need to manage and maintain multiple systems or worry about having to provide unified security across them.

You also get synergy across these capabilities. For example, by having support for Machine Learning algorithms and Spatial data in the same database, you can easily do predictive analytics on Spatial data.  The Oracle Database is a great example of a Converged Database, as it provides support for Machine Learning, Blockchain, Graph, Spatial, JSON, REST, Events, Editions, and IoT Streaming as part of the core database at no additional cost.

A good analogy for a Converged Database is a smartphone. In the past, if you wanted to take a picture or video you would need a camera. If you wanted to navigate somewhere you would need a map or a navigation system. If you wanted to listen to music, you needed an iPod and if you wanted to make phone calls, you would also need a phone.

But with a smartphone, all of these products have been converged into one. Each of the original products is now a feature of the smartphone. Having all of these features converged into a single product inherently makes your life easier, as you can stream music over the phone’s data plan or upload pictures or videos directly to social media sites.

During the keynote at the OOW London, Juan Loaiza did a great job of explaining what a converged database is and the benefits you get from it compared to the alternative approach of running multiple single-purpose, proprietary databases.

Is a Converged Database just a Multi-Model Database?

No! Recently a lot of single-purpose databases have begun claiming they are multi-model because they have added support for one other datatype or workload. Meaning they can do two things and are therefore Multi-Model. What makes a converged database stand out is that it integrates all the workloads and datatypes needed by modern Data-Driven Apps.  Also, it has the ability to transparently scale out and up to handle any scale of workload.

What’s the difference between a Converged Database and an Autonomous Database?

Oracle Autonomous Database is a family of cloud services that use machine learning to automate database provisioning, tuning, security, backups, updates, and other routine management tasks. So, these cloud services run on top of the Oracle Database along with additional automation and Machine Learning to provide performance and availability SLAs.

So, a Converged Database isn’t the same thing as an Autonomous Database.  But the Oracle Database (a converged database) is one of the components that make up an Oracle Autonomous Database. Hopefully, the formula below makes this clear.

Finally, you can get more information on why a Converged Database is the only database you will ever need, in the video below where Noel Yuhanna, Principal Analyst at Forrester and Thirthankar Lahir discuss this very topic!

2 thoughts on “What is a Converged Database?”

    1. Hi Marcel,

      That’s an excellent question.

      ADW and ATP are a perfect example of a converged database in action. Even though ADW and ATP have been optimized for different workload using different data formats, different initialization parameter settings (SGA, PGA etc.) and different access patterns, they are both running on the same converged Oracle Database.

      This makes it extremely easy for DBA to be able to navigate, secure application data, and monitor these systems as they can use a standard set of tools for both.

      It’s also much easier for developers as they can use a common language (SQL), and a standard set of API to access both ADW and ATP.

      There is also a great opportunity to integrate ADW and ATP because of the common platform seemly. Take, for example, how easy it is to do predictive analytics.

      Imagine we are trying to predict which customers will buy season tickets for our sports team.

      Frist we would build our Machine Learning model in ADW, where we have a detailed history of all the fan activities, using the in-Database Machine LearningL algorithms:

      DBMS_DATA_MINING.CREATE_MODEL(
      model_name => 'SEASON_TKS_MODEL',
      mining_function => dbms_data_mining.classification,
      data_table_name => 'FAN_DETAIL_TAB',
      case_id_column_name => 'FAN_ID',
      target_column_name => 'BUY_SEASON_TKS',
      settings_table_name => 'GLM_SETTINGS');

      Once we have perfected the model on ADW, we can deploy it on ATP via a simple SQL query to predict which fans are likely to buy season tickets for the upcoming season:

      SELECT prediction_probability(BUY_SEA_TKS, ‘Yes’
      USING 3500 as bank funds, 40 as age, ‘Married’ as marital_status)
      FROM dual;

      The common converged database platform makes this type of predictive analytics a trivial exercise rather than a painful one including a lot of data movement.

      Hopefully, this helps explain how ADW and ATP fit into the vision of a converged database?

      Thanks,
      Maria

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