OpenAI Acquires Rockset: What it Means for Data Analysts

In June 2024, OpenAI announced its acquisition of Rockset. OpenAI is best known for its GPT series of AI models. ChatGPT, OpenAI's flagship, was released in late 2022. ChatGPT quickly became one of the world’s most popular AIs. So popular that in just a few months, players like Google, Apple, and Facebook were scrambling to release their own competitors.

What is Rockset?

Rockset is a software-as-a-service (SaaS) provider of real-time analytics databases. Analytics databases are specially designed to handle complex queries of large volumes of data. Rockset’s client base was mostly enterprise-level customers who needed help managing and analyzing vast amounts of data in real time.

Why Did OpenAI Acquire Rockset?

OpenAI didn’t buy out Rockset because of their customer list. In fact, quite the opposite. Rockset’s customers found out the same day as everyone else that OpenAI was closing down the customer-facing part of the Rockset business because OpenAI is interested in the underlying technology.

OpenAI bought Rockset for its advanced real-time analytics and data indexing capabilities. They plan on working those into OpenAI’s own retrieval infrastructure. By meshing Rockset’s database solutions with their own products, OpenAI plans on making it possible for their users, developers, and enterprises to leverage data better and enable even more powerful and efficient AI applications.

This acquisition marks an important moment, especially for anyone who works in data or data analytics. People interested in data have reason to be pretty excited about OpenAI’s acquisition of Rockset. It all hints at specialized skills in data management and analysis being very, very important for the future of AI. For data professionals who know how to capitalize on this, the Rockset acquisition could reveal tremendous opportunities.

Why Rockset’s Analytics Databases Stand Out

Rockset is a leader in the field of analytics databases. Analytics databases are much more than big virtual bins for storing data. They can run complex and intelligent queries on stored data that enable real-time insights while supporting decision-making processes. An analytics database is designed to handle large volumes of data with high efficiency. They're particularly valuable for companies who need quick data retrieval and analysis of huge data sets.

Key Features of Analytics Databases

A few things make analytics databases special. First, they’re optimized for read operations, can retrieve data very quickly, and immediately make it available for analysis. Analytics databases also support complex queries of large sums of data. Rockset’s model, in particular, supports complex queries, aggregations, and joins, which all means they can combine fast and in-depth data analysis with very fast retrieval of huge amounts of data.

Real-Time Data Processing

Analytics databases are also capable of real-time data processing, which means insights are provided almost immediately from fresh data. They are also highly efficient in their querying and data compression and extremely scalable.

Why Did OpenAI Need an Analytics Database?

In November 2023, ChatGPT received more than ten million queries per day and had 100 million weekly users, and that number has only grown. OpenAI needs to increase the amount of data ChatGPT can "see" and access.

OpenAI plans to improve the speed and depth with which ChatGPT can access the truly insane amount of data it uses to come up with its answers.

Limitations of ChatGPT

If you’ve been a user of ChatGPT, you know that for a long time, it could only answer questions with dated information. ChatGPT was built on a snapshot of the internet, and if your question was about something after the snapshot was taken, ChatGPT couldn't find the answer.

Recently, new versions of ChatGPT can browse the internet and have access to more recent snapshots. But digging into those snapshots and coming back with responses quickly was stretching their analytics capacity. ChatGPT’s data processing speeds were too slow to meet growing real-time requests.

What Data Analysts Should Take Away From This?

OpenAI is at the cutting edge of technology. Yet, it still needed to make a huge investment in revolutionizing its real-time analytics, data indexing, and querying capabilities. A company of AI engineers and data analysts still needed the help of data specialists.

Data Analysis Skills in Demand

This should tell you that skills and tools related to real-time information access and analysis are extremely valuable right now. Plus, as companies deepen and expand their artificial intelligence capabilities, demand for those skills is only going to increase.

Data Analyst Career Opportunities

Data professionals of any kind and new IT professionals should take the OpenAI acquisition as a roadsign. It's pointing out the direction that companies not just in the tech industry, but across all industries are going to be headed in the near future: more data, more capabilities, and more integration of advanced analytics into everyday operations.

The emphasis companies are placing on real-time data processing, and AI-driven insights is growing. That means skills in large-scale data analytics, developing intelligent applications, and advanced data querying are going to become very valuable. People who embrace these trends and find ways to make careers out of them will be at the forefront of the next wave of innovation in data analytics.

Best Job and Training Opportunities for Data Analytics Professionals

Companies of all sizes and industries need subject matter experts in designing, creating, and deploying enterprise-scale data analytics solutions. And the demand is only going to increase. There are many different ways to use those skills and many different vendors or tools you can learn.

Microsoft Fabric

For example, Microsoft Fabric. Microsoft Fabric is a unified, cloud-based platform that provides advanced capabilities across Microsoft’s other cloud services. Companies who’ve already invested in some of Microsoft's products are going to want to stay in the Microsoft family. 

A certification like the Microsoft Certified: Fabric Analytics Engineer Associate demonstrates familiarity with transforming data into reusable analytics assets with Microsoft Fabric components. The corresponding DP-600 exam tests these skills in real-world scenarios, ensuring that certified professionals can design, implement, and manage complex analytics solutions. Earning it depends on a mastery of Microsoft Fabric, and it proves a commitment to professional skill development.

AWS Solutions

Then there’s AWS – one of the leaders in cloud-based networking and data. They provide holistic cloud solutions that work together seamlessly to provide businesses and organizations with unique and powerful solutions. 

Mastering the AWS cloud with a cert like the AWS Certified Solutions Architect - Associate proves your knowledge and skills across the entirety of AWS services. AWS has a huge assortment of tools and solutions, and even though they’re designed to work together, they can be overwhelming. Fortunately, all it takes is some AWS training, and you'll know how to inventory the needs and operations of a company and come up with IT-based ways to satisfy them.

Sometimes, what AWS provides out of the box isn’t enough, and when unique challenges arise, you have to develop, test, deploy, and debug your own AWS Cloud-based applications. 

The AWS Certified Developer - Associate proves to employers that you know how to write your own applications that customize how a company uses the AWS Cloud. Preparing for its certifying exam, DVA-C02, has to include developing and optimizing cloud-based apps, navigating and integrating APIs and SDKs, and deploying apps within AWS’ CI/CD workflow.

How to Get Started in Data Analytics

The growing demand for data analysts and specialized data professionals isn’t a short-term development. OpenAI’s acquisition of Rockset is the most recent symptom of a worldwide shift. Companies everywhere need faster access to much larger datasets. That means it’s a good time to start your data career.

Learning the Basics of Data Analytics

Even if you’re just getting started and can barely spell “AI” or “ML,” there’s a way to get started. A good machine learning introductory course can help in many ways. First, it’ll teach you the vocabulary and concepts the entire industry uses to talk about AI and ML, putting you on an even footing. 

But it also helps show you how you can incorporate AI and ML into fields you're already familiar with. You don’t have to be a total expert to find ways to use AI; learning the basics can help you find areas where you can specialize.

Expanding Your Skills

Or, if you’ve already gotten your toes wet in data analysis, you can expand your skills into programming or learning the SQL language. Expanding your skills and knowledge improves your marketability as a data professional. You could be the person who comes up with the bright AI ideas that companies are going to be looking for in the aftermath of the OpenAI acquisition of Rockset.

Conclusion

OpenAI’s acquisition of Rockset is indicative of a large, worldwide trend. Companies have spent years acquiring as much data as they could get their hands on, but now they don’t know what to do with it, and they need trained professionals who can help. 

Technologies are coming that will help companies make better use of the data they already have access to. But they’ll need practitioners who know how to use that tech. Training, preparation, and certifications are the best tools a new or experienced data analytics professional has to become more marketable and valuable.