On SingleStore Pro Max. Q&A with Nadeem Asghar
Q1. You called your recent product launch ‘SingleStore Pro Max’… Talk to us about why you chose this name?
In the 2000s, smartphones emerged. They eliminated the complexity of having to lug around multiple devices (such as MP3 players or GPS or cameras) and to keep them in sync with each other. Smartphones soon transformed people’s lives as well as businesses forever. Since they had several capabilities built into one thing, they were great for building new applications that could take advantage of multiple capabilities– Maps, Podcasts, communication apps, banking apps, etc. In other words, a smartphone was a platform.
‘SingleStore Pro Max’ is a tip of the hat to the best-known smartphone in the world, and is meant to indicate the large payload of innovation that now forms our product and makes it a data platform. And we don’t stop at just being a data platform, we consider ourselves to be the only real-time data platform.
Q2. What makes SingleStore a real-time data platform?
At SingleStore, we believe a real-time data platform is one that is built for all data, all data apps and all data pros.
- Real time. A data platform that’s real time is designed for zero ETL across transactional and analytical systems, regardless of the source or destination of data. It is capable of taking in vast amounts of fresh data, making it immediately available for use.
- Any data. A data platform handles all kinds of data — from structured data that fits in neat rows and columns, to semi-structured (JSON, BSON, text) and unstructured data like audio, video, PDFs and images. It handles the complete data lifecycle, From creation to integration, visualization and insight.
- All applications. The data platform serves all kinds of applications — generative AI, transactional applications, petabyte-scale analytics, front-office analytical applications, etc. Its high concurrency allows it to serve end users, not just back office analytical systems.
- All makers and data professionals. A data platform doesn’t just serve back-end and full-stack developers but also ML engineers, data scientists and data analysts. Through the applications built by its customers, a data platform also serves every end-user who interacts with data-driven systems.
Q3. Could you elaborate on what goes into a real-time data platform and why that’s unique to SingleStore?
These are a few things that make a data platform needs in order to be truly real-time:
- Support for streaming data: Processing streaming data effectively is crucial to developing real-time GenAI applications. Therefore, real-time data platforms need the proper framework in place to manage the intake of streaming data.
- Data integration: An effective data platform needs seamless integration of various data sources, including structured and unstructured data formats into one single repository. This enables organizations to have a unified and comprehensive view of all their data.
- Low latency processing: This capability helps organizations make real-time data-driven decisions by reducing data movement and reducing resource usage. When data is processed swiftly, this allows people to quickly analyze outputs and draw immediate conclusions.
- Scalability: As data volumes increase, these platforms need to scale effectively to handle the larger workloads and accurately respond to user requests. Scalable data platforms allow organizations to better manage resources and work more efficiently with massive datasets.
SingleStore has been engineered from the ground-up with all of these capabilities. Not just that, it is the only data platform that can deliver all of this in one form factor…Like our CEO Raj Verma likes to say, Only Simplicity Scales.
Q4. Do you consider any of the products launched as game-changers in the data & AI industry?
Ultimately what’s game-changing is what makes it so for customers. SingleStore is fortunate to serve many Fortune 500 companies as well as hyper growth startups across diverse sectors…. In my conversations with technology leaders in these companies, I find customers are most excited about our new AI platform capabilities. Some of these capabilities are Indexed Vector search, Job Service and a new compute service for GPUs. Many developers we talk to are excited also about the new Free Shared Tier. All of these capabilities make it easy to build and operationalize powerful generative AI applications at a fraction of the costs and at greater performance than any other alternatives out there.
Q5. You mentioned a lot of new capabilities, could you elaborate on a few?
Our indexed vector search using Approximate Nearest Neighbor (ANN) algorithms can improve performance by 800-1000x compared to exact KNN methods. This enables users to perform true hybrid search while leveraging the power of analytics and scalability of an enterprise SQL database. Taking this into account, vector search plays a pivotal role in the development of AI applications as it supports efficient retrieval of vector data, using vector distance measures while still having a familiar SQL interface for sophisticated queries.
Likewise, our new compute service enables users to deploy and scale powerful CPUs and GPUs for your application code and workloads: data analysis with SQL and Python, scheduled jobs for ETL and ML flows, and building dashboards and BI. None of your data needs to leave to your secure SingleStore environment.
Another fundamental part of our data platform framework is adding native data integration capabilities. We want to reduce the burden and costs of data movement. We just announced support for real-time ingestion (CDC in) for MongoDB and MySQL. We also now support ingesting Apache Iceberg. With our new CDC Out capabilities, users will be able to connect SingleStore to their other data sources for applications and databases such as cloud data warehouses and lake houses. This can be powerful not for a well-connected data estate including homogeneous databases (such as MySQL or SingleStore) or heterogeneous databases (such as with MongoDB, Snowflake, Google Vertex AI/BigQuery).
Q6. Speaking of MongoDB, earlier you mentioned SingleStore Kai™ . Could you describe what Kai is and what challenges it solves?
We saw a need in the marketplace to create something that developers have been looking for: a way to power fast analytics on JSON. Since MongoDB® is a document database, it doesn’t have the ability to handle quick queries at an enhanced performance level. With our API, Kai, first introduced last year, it speeds up analytics for MongoDB® applications by a factor of 100s. On top of this, it requires no code changes or data transformations, simplifying the process and helping users build their projects with little to no friction. Fortunately, the API works for both NoSQL and SQL databases, meaning more users will be able to leverage this tool for improved and quicker performance. It’s now generally available for transactional and analytical processing for apps originally built on MongoDB.
Qx7. Is there anything else you like to share?
SingleStore is at the forefront of the real-time AI movement. With SingleStore Pro Max release, users can access a multitude of resources from vector search to compute power, making their tasks more efficient in the long run.
If you’re interested in learning more about our latest announcements, check out our blog post that goes into further detail. But if you already want to get started experimenting with our new free tier, visit this page to learn more. To view the recording or our launch event, please visit singlestore.com/pro-max.
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Nadeem Asghar, SVP Product Management & Strategy, SingleStore.
Nadeem is Hands on technology Leader (CTO, CPO, CSO) with over 25 years of extensive experience scaling early-stage startups to $1B+ revenue from ground up. Prior to joining SingleStore, Nadeem has played key Product, Pre-sale, Technology and Strategy roles at Cloudera, Hortonworks, Morgan Stanley, Incentify, Capgemini and Lucent Technologies. Nadeem holds a Master’s degree in Computer Science from NJIT.
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