Data is fueling today’s digital transformation, but only 15% of organizations get what they need from their data. Out of them, 87% review or adopt a fast data analytics strategy within the next two years to better achieve their goals.
On the path to transforming the organization to be AI-enabled, there is a need to have a prescriptive approach to accelerating the journey. The 5 steps to get to AI:
- Modernize all the data estates in a multi-cloud environment.
- Collect data to make it simple and accessible.
- Organize data to create a business.
- Analyze and scale AI everywhere with trust and transparency.
- Infuse and operationalize AI throughout the business.
There’s no AI without IA (Information Architecture), and drilling down into the collection piece of making data simple and accessible shows that there are many options in terms of choosing the data repository that’s right. A Hybrid Data Management Platform allows the deployment and scaling of data when and where required and allows the choice of deployment target - public or private cloud, on-premise, and even appliance. All of these data stores are intertwined with one Common SQL Engine and one unified experience across all the different flavors.
There are various types of workloads for handling the hybrid data management strategy, which are best suited for the job:
- A most trusted database management system in the enterprise space used by some of the largest institutions in the world to run their most important transactional workloads and analytics.
- A highly flexible data warehouse, optimized for fast deployment into private or virtual private clouds via docker containers.
- An enterprise-grade, hybrid ANSI-compliant, SQL on Hadoop engine, delivering massively parallel processing (MPP) and advanced data query–offering a single database connection or query for disparate sources such as HDFS, RDMS, NoSQL databases, object stores, and WebHDFS.
Another category of data workloads focused on fast data which is used for types of data stores for IoT or real-time analytics use cases. In this space, data gets generated in real-time; therefore, it has be acted upon in real-time as well. For example, telecom companies generating large amounts of data from CDRs (Call Data Records) need to analyze data in real-time so they can act on potential fraud or load balancing in the network. In addition, a manufacturing line, where there are millions of data points coming out from sensors that can detect when a part is going to fail in real-time–and be able to act on the failure without having to impact the whole production sequence. These are examples where a database is needed that can rapidly ingest and analyze streamed data for event-driven applications. The said database is capable of ingesting hundreds of billions of events per day and can analyze the ingested data immediately for real-time insights. The system also stores all the data it ingests in particular format and is continuously available, meaning that hardware failures don’t impact the ability to ingest the data or derive insights.
The database is optimized for machine learning and comes embedded with special features. This means it can be used for the data collected from streaming sources and AI can be applied in real-time on the ingested data. The database is a premium add on the existing system (a new kind of data and analytics platform that simplifies how data is collected, organized, and analyzed to accelerate the value of data science and AI). It’s also optimized for IoT with features such as new time series libraries containing special SQL functions for this type of use case. Fast data is rapidly becoming one of the biggest challenges and opportunities for businesses that want to transform into real-time–and get the benefits from gleaning meaningful insights to help give them a competitive advantage.