Overview
- IBM has rolled out the Cloud Private for Data platform that integrates data science, data engineering and app building
- It can access all the data you have on the cloud, including extracting from databases like MongoDB, Oracle, or an IBM repository itself
- The platform can take up to 250 billion events a day!
Introduction
IBM’s latest offering in the data science field brings analytics to your data, and not the other way around. Organizations looking to leverage AI to gain a competitive edge need to ensure that their data needs to have the proper security around it in order to be extract useful insights from it. IBM saw this gap and developed the Cloud Private for Data to fulfil that demand.
IBM Cloud Private for Data had been announced a couple of months ago at the company’s Think conference and they have now officially rolled out the first version to the general public. This platform aims to accelerate the AI journey of it’s users by simplifying data management, data governance and business analytics, all packaged into a single interface. In other words, this tool goes beyond simply data warehousing – it combines the power of data science and data engineering.
The platform can access all of the data you have on the cloud, whether it’s in Teradata, Oracle, MongoDB, PostgreSQL, Hadoop or an IBM repository itself. It is able to run natively on Red Hat’s container orchestration platform called OpenShift. According to IBM’s GM of Analytics Rob Thomas, the platform can intake 250 billion events a day. Remarkable!
The Cloud Private for Data platform brings the below four features to the data scientist or company using it:
- Data Security: The platform adds a solid security fence for your data. It allows all your critical data to remain behind your company’s firewall, while being accessed by cloud applications in order to generate insights you couldn’t think of getting before!
- Data Organization: Private Cloud for Data helps users locate existing data, request access to it, and seamlessly collaborate with your team members.
- Scale Insights: The platform collects data, makes it accessible, organizes it to build a trusted analytics framework, and finally makes it ready for analysis to scale and generate insights whenever required
- Prepare for AI: It reduces the time and budget required to generate these insights. You can move from raw data to clean and analysis-ready data in a breeze because the tool puts everything together in a single access point
Check out the below video which showcases a real-life scenario of this tool:
Our take on this
The end goal of using a tool for data scientists is to spend more time with the data in order to build your models (and less time collecting and cleaning it). This is where IBM Cloud Private for Data fills the gap. There are a few tools out there that aim to leverage cloud solutions in AI (Oracle Cloud, Microsoft Azure Stack), but with clout IBM has in the ML community, I expect this tool to be picked up quickly by organizations.
It reduces multiple iterations of combining your data from various platforms. It really speeds up your data science pipeline processes and reduces manual efforts. Data engineers, let us know your take on this platform!
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Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.
