6 Alternatives to Rockset To Consider Following OpenAI's Acquisition

In June 2024, OpenAI announced that it was acquiring Rockset. Unfortunately for customers of the real-time analytics platform, OpenAI only wanted Rockset's technology. Rockset customers found out at the same time as the rest of the world that they would only have a few months to get their data off the Rockset platform and find a new solution.

Were you a Rockset customer who now needs a new real-time analytics solution? Or maybe you're just researching low-latency data access options? Either way, let's learn about Rockset alternatives. We'll look at some of the leading cloud-native platforms for high-performance query execution. We'll also look into some careers that work with those platforms.

Why OpenAI Acquired Rockset

Many were surprised by OpenAI's acquisition of Rockset, but the reason is now obvious: real-time access and queries. Users of ChatGPT know that the service uses snapshots of the Internet and, for a while, couldn't answer questions based on real-time information. Only very recent versions of ChatGPT are capable of accessing the Internet or using recent snapshots to answer questions.

OpenAI trained ChatGPT on huge datasets, but as their user count increased to 100s of millions of weekly users and their daily requests climbed to 10 million every day, the service apparently struggled to keep up. OpenAI's system was capable of handling massive volumes of data with high performance, but they apparently needed Rockset's ability to provide real-time indexing and querying.

The ability to gain immediate insights from incoming data streams is probably another reason for the acquisition. A conversational system like ChatGPT needs a responsiveness and adaptability that Rockset's analytics database is uniquely qualified to provide. In the end, the need for faster access, faster analysis and bigger datasets is why OpenAI took over the Rockset analytics database product.

What Can Analytics Databases Do?

Analytics databases are specialized tools for quickly analyzing large amounts of data and providing insights in real-time. These powerful tools can handle diverse data from different sources, which means they can run complex queries and generate reports fast. An analytics database is perfect for a business that needs to make data-driven decisions on the fly.

Whether a company tracks sales trends, monitors user behavior, or has to notice and respond to sudden changes in conditions, analytics databases turn raw data into actionable information.

Many companies provide analytics databases or services that act like one. These cloud-based services are often designed to interact with other tools and services from the same company. Amazon, Google, and Microsoft are the big names on the scene. The integration between the databases and all the services on the platform can help simplify workflows and give access to powerful analytical capabilities.

1. Microsoft Fabric

Microsoft Fabric is a unified data platform designed to simplify data integration, management, and analytics. It combines many different Microsoft services into one cohesive experience. Using Fabric, you can ingest, prepare, manage, and analyze data all from one place.

Microsoft Fabric Capabilities and Limitations

Fabric, being a Microsoft service, is very well integrated with every other Microsoft service. Its real-time data processing, advanced analytics and intuitive data visualization with Power BI are considered industry standard. It's also designed as an enterprise solution with a focus on regulations, security, and governance.

Learning curves and cost are the biggest limitations on Microsoft Fabric. Small and medium sized companies may find that licensing costs quickly add up.

Careers with Microsoft Fabric

The Microsoft learning and certification program is excellent. Well-trained and certified Fabric Analytics Engineers can navigate the cost structure as well as the capabilities and help businesses reach their full potential with Fabric. The Microsoft Certified: Fabric Analytics Engineer Associate certification, which requires passing the DP-600 exam, demonstrates expertise in designing, creating, and deploying enterprise-scale analytics solutions with Microsoft Fabric.

2. Amazon Aurora

Amazon Aurora is a fully managed relational database service. Designed to be compatible with MySQL and PostgreSQL, Aurora provides the speed and availability of high-end commercial databases at a fraction of the cost. Large-scale applications are Aurora's primary focus, offering automatic scaling, backup and recovery features.

Aurora Capabilities and Limitations

Amazon claims that Aurora has five times the throughput of standard MySQL databases, and twice that of PostgreSQL services. Obviously, integrating with other AWS services is a huge benefit as well, making it easy to work into existing workflows. Built on Amazon's infrastructure, Aurora has high availability and high durability with multi-AZ deployments and automatic failover.

Aurora can be quite a lot more expensive than other database options. Configuration and management are also reportedly significant issues. Aurora requires more than good database administrators; it takes deliberate training and specific experience. Aurora is highly compatible with MySQL and PostgreSQL, but there are certain features unique to those database systems that aren't fully supported, which can cause headaches in certain applications.

Careers with Amazon Aurora

AWS doesn't have certifications related to specific applications and tools. Instead, their certification program focuses on job-related skills. Companies who want to incorporate Aurora into their workflow will need engineers and administrators who have earned the Certified Solutions Architect - Professional or who have taken Solutions Architect training. 

3. DynamoDB

Also from Amazon, DynamoDB is a NoSQL database service designed for fast performance with seamless scalability. Being NoSQL, DynamoDB is especially good for semi-structured data and applications that need low-latency data access.

DynamoDB Capabilities and Limitations

DynamoDB has a lot going for it. First, it supports both document and key-value store models. Second, it scales rapidly and automatically while providing encryption at rest. Third, it integrates with other AWS services. DynamoDB is also known for its multi-region, fully replicated data, which ensures high availability and disaster recovery.

Cost is a frequent limitation, and that's once again true in DynamoDB's case. The pricing model can become expensive for high-traffic applications. Another potential drawback is the lack of complex querying capabilities compared to SQL databases.

Unlocking DynamoDB's most advanced features tends to be complex, so database administrators and engineers will need to be thoroughly trained in its architecture and best practices.

Careers with DynamoDB

AWS doesn't offer a certification or training specifically related to DynamoDB, but the AWS Certified Solutions Architect – Associate includes sections specifically related to managing DynamoDB.

Even experienced database engineers and database administrators will want to study AWS best practices and not rely solely on past experience with other databases – even other NoSQL database services.

4. AWS Glue

AWS Glue is a fully managed ETL service. The Extract, Transform, Load service simplifies the process of preparing and loading data for analytics. Since it automates most of the effort that goes into data integration, AWS Glue makes it easier for users to catalog, clean, enrich, and move data.

AWS Glue Capabilities and Limitations

AWS Glue comes with a data catalog that automatically discovers and stores metadata about its assets, making finding and managing them much easier. Its built-in IDE makes building, testing, and executing jobs more scalable and cost-efficient. Glue also supports many data formats and integrates seamlessly with other AWS services.

When AWS Glue works, it works well. Unfortunately, it's pretty complex to set up initially. Plus performance can vary significantly, usually based on size and complexity of transformations. AWS Glue is also not fully customizable, so it may not support highly specialized ETL tasks and niche applications.

Careers with AWS Glue

Database engineers and Solutions Architects tend to be the careers associated with using AWS Glue. Past ETL experience will be helpful, but Glue is a particular tool, so even if you're experienced with ETL tasks, getting trained specifically will be extremely beneficial. The Solutions Architect - Associate and Solutions Architect - Professional certifications both include sections on AWS Glue.

5. Amazon Athena

Amazon Athena is a serverless, interactive query service. From Athena, it's possible to analyze data in Amazon S3 directly with SQL. There's no infrastructure to manage with Athena since it scales automatically to execute queries and delivers fast results. The ideal use case of Athena is users who need to run ad-hoc queries on S3 data but don't need a complex data warehouse setup.

Athena Capabilities and Limitations

Athena is flexible and supports a wide range of data formats. It integrates with Glue, enabling automatic schema discovery and metadata management. Since it's pay-as-you-go, you only pay for the queries you run.

Although Athena can handle large datasets and has built-in analytical functionality, query performance can be limited by size and format. Large and unoptimized datasets will lead to slower response times. Plus, it's a good ad-hoc query solution, but it's not a good choice for complex and long-running workloads. Also, pay-as-you-go works well when you're careful, but inefficient or poorly structured queries can get expensive quickly.

Careers with Amazon Athena

Athena is a good tool for professionals in many different capacities. Like the other AWS tools, a Solutions Architect should always know how to use it. But many companies are looking for data engineer associates who know how to set up, use and query data with Athena.

6. Amazon Relational Database Service (RDS)

RDS is a fully managed service from AWS that makes setting up and operating databases in the cloud easy. RDS supports several database engines, from MySQL, PostgreSQL, MariaDB, and Oracle to MS SQL Server. It's a flexible data solution for different needs. As a fully managed service, RDS handles routine tasks like provisioning, patching, backup, recovery, and scaling.

RDS Capabilities and Limitations

The best parts of RDS are related to its being a fully managed database service from AWS. Automated backups, snapshots, and global deployments make it available and durable. Automated patching, monitoring, and optimization are key benefits of RDS.

But since RDS manages the underlying infrastructure, complex or custom configurations can be impossible. Costs can accumulate quickly, as well. And even though RDS supports many database engines, it doesn't always include every feature available in their standalone versions.

Careers with RDS

Many AWS certifications include sections on RDS. From their no-experience-needed Foundational Cloud Practitioner cert, to the Associate-level AWS Developer and Data Engineer, and even up to the Specialty-level Security cert, RDS is one of AWS' most common data solutions. Any AWS training that prepares you for those certs will help you use and navigate RDS, making you valuable to most companies.

Conclusion

Many companies find Rockset's departure problematic, but for people willing to adapt, it could be a great opportunity. There's never been a better time to learn about and get certified in alternative real-time analytics platforms. Whether it's Microsoft Fabric, Amazon Aurora, or RDS, robust solutions exist that can meet your analytics needs.

If you're transitioning from Rockset, don't be afraid! It can be daunting, but trained and certified data professionals can help you explore new technologies and expand your capabilities.

If you're a data professional, consider getting training that prepares you for certs like the AWS Solutions Architect. With that cert, you can confidently tell companies you know how to match their data ambitions with the right solution.