The data game has become more complicated and with 2019 we are staring towards the new challenges and technology that could be disrupting the solutions. Artificial Intelligence and cloud will be the key to the database technology because each of them has given promising solutions to deliver better results as the enterprises adopt them. The focus for the enterprises is slowing shifting from simple insightful information towards a more complicated form of data also known as Big Data. Big data is nothing but a complicated form of data that cannot be analyzed using the traditional form of tools or software. Big data is already known for the huge amount of storage requirement to store them and this makes it imperative for the enterprises to adopt cloud. Big Data and cloud can be the focus for the enterprises in 2019, as almost 45 percent of them are planning on adopting both the technologies. The cloud is redefining the basic architecture for the database with current assumptions and also developing the design. The current on-premise environment is dealing with several infrastructural costs, licensing cost and also frequent audits to deal with the database technology. The on-premises database consisted of several barriers that stopped the enterprises from inducting the database on-premise.
Cloud offered a perfect commodity for the infrastructure solutions with cost friendly storage of data, network and easy maintenance of the data. Although the current cloud providers are being given requirements of separating the database from the computing. Many tech experts believe that cloud database deployment is using the Database-as-a-Service (DBaaS) that can be patched, upgraded, and required operational changes without involving a DBA. The cloud provider will have better control over the cloud database.
Machine Learning Intruding For Better Database Management
The cloud-based database is already making the database self-reliant using the machine learning technology. Oracle had made the first go towards the self-managed database using the machine learning in the Autonomous Data Warehouse 18c, followed by the Autonomous Transaction Database 18c. The current offering of the autonomous was mostly kept on the keen eyes of Oracle holding the control. Machine Learning adds value to the database and also eases the operational requirements. The database operation generates a huge amount of log data that can be fed to the ML models and the usual challenges don’t drift to any ends. It reduces the unnecessary tasks for the DBA by managing the database by configuring according to the data-type or load. The offering in the database will not change the actual function of DBA but it will shift the focus towards something new. The option of utilizing the machine learning will be one of the necessary options many cloud providers that offer database service will be inducted.
Accepting The Server-Less Into The Cloud Database
The serverless computing will also become the new requirement with the introduction of AWS Lambda by simplifying the application development. Implementation of the provisions that can auto-scale the servers will one of the most important applications that can be implemented with the cloud DBaaS. Serverless cannot just be a simple option for everybody if the load is predictable it will be more economical. Some of the examples that are being used in the cloud are Amazon DynamoDB, where serverless is the core in design along with Amazon Aurora for the same purpose. Even Google cloud has introduced the different server-less offers with firestorm.
Introducing The Distributed Database
Distributed Database is the next wave innovation that’s being observed in the database technology. Another innovation made feasible with the cloud is the distributed database. This year, we will see the distributed database make writes first-class citizens on par with reads. The cloud providers can use the distributed technique to eliminate the need for on-premise data centers and also much of the data is being added to the cloud through different operational needs. Most of the distributed database work on the fine-line of reading and write performance. The master and slave architecture that’s being used with centralized master nodes for committing the writes or updates, while the read-only devices can be distributed over the geographical area. The multi-master technology can allow multiple nodes to be declared as a master for the specific transactions allowing the nodes to designate the write for the master. This method effectively removes the write bottlenecks on a globally distributed ledger. Various cloud providers offer this operation but only support within the region rather than across the global region. In 2019, we will see multi-region implementation of the data.
Return Of The Graph Database
The current business problems can be easily solved using the graph database such as patterns in social networking so the brands can be better equipped approaching the opinion leaders. Mapping and optimizing the different paths in the supply chain operations. The above are some of the real-life examples that can be used to represent the relation of graphs. For the computing using the graph database that uses the graph structure for semantic queries with nodes, edges, representation properties, and store data. The key system that is used in the graph representation is graph directly relates to the data items in the store a collection of nodes of data and edges. The relation allows the data that is stored to be linked together directly giving the opportunity to the user to retrieve the data using a single operation. The graph database relationship between the data is being given more priority. The acceptance of graph database tools is helping to increase the adoption of cloud providers. With the increase in IoT applications and the increase in demand for the graph database will make it more accessible. The adoption of Cloud Database will widely depend on different opportunities that enterprises feel that can be leveraged and also the technology adoption. The recent data breaches have prompted many enterprises to secure their network across different channels and also reduce the network requirements.