To further advance as the world’s fastest HTAP database, SingleStore has added Projections.
SingleStore, the database that allows you to transact, analyze, and contextualize data, announced powerful new capabilities — making it the industry’s only real-time data platform. With its latest release, dubbed SingleStore Pro Max, the company announced ground-breaking features like indexed vector search, an on-demand compute service for GPUs/ CPUs, and a new free shared tier, among several other innovative products. Together, these capabilities shrink development cycles while providing the performance and scale customers need to build applications.
In an explosive generative AI landscape, companies are looking for a modern data platform that’s ready for enterprise AI use cases — one with best-available tooling to accelerate development, simultaneously allowing them to marry structured or semi-structured data residing in enterprise systems with unstructured data lying in data lakes.
“We believe that a data platform should create new revenue streams while decreasing customer technological costs and complexity. And this can only happen with simplicity at the core,” said Raj Verma, CEO, SingleStore. “This isn’t just a product update, it’s a quantum leap… SingleStore offers truly transformative capabilities in a single platform for customers to build real-time applications, AI or otherwise.”
“At Adobe, we aim to change the world through digital experiences,” said Matt Newman, Principal Data Architect, Adobe. “SingleStore’s latest release is exciting as it pushes what is possible regarding database technology, real-time analytics and building modern applications that support AI workloads. We’re looking forward to these new features as more and more customers seek ways to take full advantage of generative Al capabilities.”
Key new features launched include:
Indexed vector search. SingleStore has announced support for vector search using Approximate Nearest Neighbor (ANN) vector indexing algorithms, leading to 800-1,000x faster vector search performance than precise methods (KNN). With full-text and indexed vector search capabilities, SingleStore offers developers a true hybrid search that takes advantage of the full power of SQL for queries, joins, filters and aggregations. These capabilities firmly place SingleStore above vector-only databases that require niche query languages and are not designed to meet enterprise security and resiliency needs.
Free shared tier. SingleStore has announced a new cloud-based Free Shared Tier designed for startups and developers to quickly bring their ideas to life — without needing to commit to a paid plan.
On-demand compute service for GPUs and CPUs. SingleStore announces a compute service that works alongside SingleStore’s native Notebooks to let developers spin up GPUs and CPUs to run database-adjacent workloads, including data preparation, ETL, third-party native application frameworks, etc. This capability brings computing to algorithms, rather than vice versa, enabling developers to build highly performant AI applications safely and securely using SingleStore without unnecessary data movement.
New CDC capabilities for data ingest and egress. To ease the burden and costs of moving data in and out of SingleStore, SingleStore is adding native capabilities for real-time Change Data Capture (CDC) for MongoDB, MySQL, and ingestion from Apache Iceberg without requiring other third-party CDC tools. SingleStore will also support CDC capabilities that ease migrations and enable using SingleStore as a source for other applications and databases like data warehouses and lakehouses.
SingleStore Kai. Now, it is generally available and ready for analytical and transactional processing for apps originally built on MongoDB. Announced in public preview in early 2023, SingleStore Kai is an API to deliver over 100x faster analytics on MongoDB® with no query changes or data transformations required. Today, SingleStore Kai supports BSON data format natively, has improved transactional performance, increased performance for arrays, and offers industry-leading compatibility with MongoDB query language.
Projections: To further advance as the world’s fastest HTAP database, SingleStore has added Projections. Projections allow developers to greatly speed up range filters and group by operations by introducing secondary sort and shard keys. Query performance improvements range from 2-3x or more, depending on the table size.
With this latest release, SingleStore becomes the industry’s first and only real-time data platform designed for all applications, analytics, and AI. SingleStore supports high-throughput ingest performance, ACID transactions, low-latency analytics, structured, semi-structured (JSON, BSON, text), and unstructured data (vector embeddings of audio, video, images, PDFs, etc.). Finally, SingleStore’s data platform is designed not just with developers in mind, but also with ML engineers, data engineers, and data scientists.
“Our new features and capabilities advance SingleStore’s mission of offering a real-time data platform for the next wave of gen AI and data applications,” said Nadeem Asghar, SVP, Product Management + Strategy at SingleStore. “New features, including vector search, Projections, Apache Iceberg, Scheduled Notebooks, autoscaling, GPU compute services, SingleStore Kai, and the Free Shared Tier allow startups — as well as global enterprises — to build and scale enterprise-grade real-time AI applications quickly. We make data integration with third-party databases easy with both CDC in and CDC out support.”
“Although generative AI, LLM, and vector search capabilities are early stage, they promise to deliver a richer data experience with translytical architecture,” states the 2023 report, “Translytical Architecture 2.0 Evolves To Support Distributed, Multimodel, And AI Capabilities,” authored by Noel Yuhanna, Vice President and Principal Analyst at Forrester Research. “Generative AI and LLM can help democratize data through natural language query (NLQ), offering a ChatGPT-like interface. Also, vector storage and index can be leveraged to perform similarity searches to support data intelligence.”
