Oracle Converged Database …the Eternal Engine!!!!
This is 1st Part of multi part series of Blogs/Articles for you to get more value out of your Oracle Database.
Data Evolution
Information Technology has come a long way from era of mainframes to RISC to Intel to Cloud to host and run various business applications. Similarly there has been continuous evolution in programming languages , various languages C++, COBOL etc. which were favorites during their time and now are considered being challenged and replaced by new languages like JavaScript,Python,GO etc.
Also has evolved the Data Store or RDBMS requirements for modern applications from Relational to Object Databases to XML databases to Json Databases…
However, despite the change of Infrastructures & Languages the need to store data for eternity/long term has been always been a requirement either for retaining customer information to building newer applications or adding new capabilities around existing core applications or meet regulatory reasons for storing data for long time, to the more modern requirements of building machine learning based predictive capabilities that rely on past Data.
Data Management historically has been compartmentalized due to rigid schema and format requirements which made more suited for Business applications which were clearly defined.
But with the advent of Internet/Mobile era there is a rapid increase of data streams being generated by applications, devices, the wide web, processes and enterprises which need to be processed, stored and analyzed securely and at scale. Also to keep up with modern application requirements schema less development for applications that keep changing has become defacto. With Languages like Javascript gaining more prominence we see rise of JSON overtaking XML as standard message exchange and datatype.Also combined this with use of spatial to tag location data to use of graphs to find relationship between data elements.The richness and variety of data has evolved a lot with rapid technology changes.
Rising Data Complexity and Data Management
With changes in type of data also rose the problem of data complexity , with many companies creating applications that store data in multiple data types relational, object,xml to json in different specialized databases e.g. Document DB, Key -Value DB , Graph , Spatial etc . This lead to issues on how to query this data stored across different data stores , how to backup the different datastores , how to restore in case of failure , how to to achieve consistency between data stores , ensuring security as each specialized database will have needs effort.
Let me elaborate on this with the use of below example of large scale application to clarify on the data complexity and data management challenges with multiple data stores, here is large scale application which stores data in multiple data stores such as HDFS,Hbase ,Mysql,Mongodb , Neo4j etc for different application or microservice requirements. Though as per application requirement , scale,performance it definitely looks like a architecture which meets the purpose , but if we look at the data layer then there are a lot of challenges with this architecture. Let me explain…
The pros of this architecture are
- Each specialized database serves unique data requirement — High Ingestion to document database to graph to relational requirements
- Best of Breed
- Open Source Community Edition
- Low Cost
The Cons of this architecture are
- Each database uses proprietary APIS for access making integration complex
- Data propagation is complex and data can be unreliable
- Security and management across all databases in complex
- Specialized skills , each specialized database will have its unique requirements in terms of consistency , backup, recoverability , backup , skillsets required to manage…
Also this kind of setup requires tough architecture choices to be made for setting up the infrastructure, operating it, tuning it, upgrading it and scaling it .Though the attraction of Open Source , low cost may be attractive but the sheer effort and cost it terms of managing it will make it expensive proposition over the long term.
if i compare this to Oracle Converged Database which provides a Multi-Model Multitenant, Multi-workload database , Oracle is provides a simpler and easy to maintain architecture.
Oracle Converged Database : Less Complexity & More Predictability
Oracle Converged database offers an architecture which gives you capability to store data for diferent datatypes within the same database in different tables JSON , XML , Relational , ML etc. which serve different purposes and still you have the familiarity of using Oracle Database instead of having multiple specialized databases to cater to different requirements.
Here the same architecture from Figure A can be simplified with Oracle Converged Database with( Fig 1) : Oracle Converged with Container Database and single Pluggable Database or (Fig 2): Oracle Converged database with container database and multiple Pluggable databases for different datatype/microservices requirements.
Benefits of Oracle Converged Database
- Single Database or Multiple Database on Single Container
- Less data movement , ability to query seamlessly between relational , json , xml tables without complex apis or events or messages
- Scalability, Availability,Performance and Security of Oracle Database
- Easy to backup,restore,patch,upgrade
I know we can always debate the pros and cons of each specialized database still the sheer convenience of using a familiar database with all its enterprise capability is reassuring compared to manage diverse and disparate Databases which can get very complex to manage.
Summary: -
Oracle Converged Database offers a great platform to meet your future business requirements , cater to different datatypes and meet performance,scalability, availability for any application.
Disclaimer: I am currently an employee of Oracle Corporation. All views expressed in this article are in my personal capacity and do not necessarily reflect my employer’s views.
