SingleStore

90% of enterprises lose $300K+ per hour in an outage.⚠️ The May 2026 AWS outage lasted 20 hours — is your architecture ready when the unexpected hits? We broke it down. 👉 hubs.la/Q04gCGBT0

SingleStore is hitting the road in India! 🇮🇳 The AI Enablement Tour lands in Bangalore + Mumbai (May 11–15) — hands-on technical training for teams ready to build smarter and move faster. Will we see you at a stop? 👀 #SingleStore #AI #DataEngineering

There’s a narrative forming in the data world that if you have enough data, the lakehouse is the obvious place to put it. Open formats, cheap storage, shared access across tools. These are real advantages, and for the right workloads, the model works extremely well. But that narrative tends to blur an important distinction. Storage has largely been solved, yet what actually determines whether a platform works in practice is how it behaves when the data is used—when it’s being queried, updated, and expected to respond under load. That’s where the gap between storage and execution becomes clear. Lakehouses become much more powerful when paired with an execution layer designed for low-latency, high-concurrency workloads. 👉singlestore.com/blog/the-lake-…

Texas baseball fans, it’s time for another big night in Arlington! ⚾ On May 12, SingleStore invites you to Globe Life Field for an exclusive hospitality experience as the Texas Rangers take on the Arizona Diamondbacks. It’s the perfect mix of baseball, technology, and connection right here in the heart of Texas. 🤠 Seats are limited. Secure your spot today 👉hubs.la/Q04dysNp0

Tampa baseball fans! ⚾ Don’t miss Devil Rays vs. Giants at Tropicana Field! Suite access, food, drinks & networking with top tech execs—talk AI data stacks, then enjoy the game. Spots are limited! Register now: hubs.la/Q04cNMp50

Lisbon, we’re coming 🇵🇹 At SingleStore, we’re drawn to people with endless curiosity, a passion for excellence, and the resilience to power through the toughest challenges. We’re hiring engineers to work on real-time data, distributed systems, and the infrastructure powering modern apps. Proud sponsors of SINFO — come meet the team at #SINFO33 and let’s talk about what you could build with us 🚀

SingleStore is proud to sponsor #GoogleCloudNext 2026 🚀 Meet us at Booth #6301 in Las Vegas, April 22–24, and see how real-time data + a unified database power faster decisions, smarter apps, and AI at scale. #SingleStore

A “quick” follow-up question should not stall a portfolio review while the dashboard spins and everyone wonders if the data is current. In financial services, slow dashboards erode confidence exactly when advisors, risk teams, and executives need real-time insight. In his new blog, Justin Strnatko explains why traditional stacks struggle with live data, usage spikes, and direct query BI — and how #SingleStore delivers fast, fresh queries at scale without layers of caches, extracts, or custom pipelines. Read the full story and see how to design your own real‑time evaluation. 👉 hubs.la/Q04c7Zkr0

A great night last week at the SAP Center in San Jose hosting customers in a premium suite for the Chicago Blackhawks vs. San Jose Sharks game. 🏒🦈 Thanks to everyone who joined us for great conversation, great company, and an awesome night of hockey!

POV: Your data is this majestic full moon. Most databases see a blurry dot and call it a night. SingleStore? Sees every crater, every glow and delivers insights before you finish saying ‘real-time analytics’.

Revenue leaders don’t struggle with a lack of data – they’re drowning in numbers they don’t trust. End result 👉 long forecast calls, side spreadsheets & slow decisions. In our latest blog, Kevin Tran shares how a real-time SalesTech flywheel can restore forecast confidence. Read more hubs.la/Q04b4XpM0

A night among the Stars in Dallas. 🏒✨ Last week at the American Airlines Center, SingleStore brought together customers and friends for the Dallas Stars vs. Winnipeg Jets matchup — high energy on the ice and even better conversations in the suite. From real-time data to AI, we’re helping teams build and move at the speed today’s moment demands. Thanks to everyone who joined us.

@SandipSwain @arpit_bhayani Glad to hear that, Sandip! 🙌

Counting things seems simple, but at scale, how you count becomes an architecture decision. Here's a quick write-up on the need for columnar systems... Not counting like "how many signups today?" Counting like "for every user, across every product, across billions of usage events this month, what exact amount do we owe them?" The answer has to be right every single time. No approximations. Most systems start with a row-based database like MySQL or Postgres. Data is stored row by row, so each record contains all its fields together. This works well for transactional workloads. You insert an event, update a record, fetch a user - all efficient because everything you need is in one place. But aggregation is different. If you want to compute something like "total usage per user this month", a row-based system has to scan full records across the dataset, even though only a few columns are relevant. As data grows, this becomes increasingly expensive. Columnar databases take a different approach. Instead of storing data row by row, they store it column by column. All values for a single field are stored together. So when you run an aggregation like "sum usage grouped by user", the system scans only the required columns, not entire rows. This unlocks a few important advantages: - less data read from disk - better compression (similar values stored together) - vectorised execution over batches of values - easier parallelisation across large datasets The result is much faster performance for large-scale analytical queries. The tradeoff is that columnar systems are not optimised for row-by-row operations. Inserting or updating individual records is less efficient compared to row-based systems. So the distinction is simple: - Row-based databases are built for transactions. - Columnar databases are built for aggregations. Hope this helps.

Most revenue teams don’t have an AI model problem. The gap shows up in execution. We’ve seen call summaries and risk scores, but insight without action just creates AI fatigue. Early adopters of agentic AI start smaller: they pick one painful workflow (like inbound lead handling) and let an agent reliably own that motion end to end. That’s the difference between layering on AI and actually changing your business operations. In this new blog, Kevin Tran breaks down what that looks like in practice, and the five lessons teams are learning as they move from insight to action 👇 hubs.la/Q048v6dg0

We’ve all seen the flashy demos, but when it comes to real-world deployment, many AI projects are hitting a wall. The culprit 👉 The Context Gap. In our latest blog, SingleStore CEO Raj Verma breaks down why even the most advanced models fail without a real-time data foundation. 🔗 hubs.la/Q047bqbp0

When it comes to Vector Search for AI apps, what is your #1 performance deal-breaker?

Traditional data warehouses weren’t built for real-time, high-concurrency workloads. This leads to latency issues, complex integrations, and rising costs. Check out our latest blog by Nikhita Chandra, Sr. Solutions Engineer at SingleStore, on how modern teams create a unified, real-time data layer! Full blog 👉 hubs.la/Q045NnM00

RL is cool in notebooks but what about production RL? That’s a data engineering problem. If your agents learn continuously, you’re juggling: 👉 Massive trajectory data 👉 Reward logging at scale 👉 Real-time feedback loops 👉 Low-latency updates In this session, we'll break down RL fine-tuning for agent systems + how SingleStore handles the data firehose behind it. Register today 🔗 hubs.la/Q045b0mz0

This blog looks at why mutual TLS changes the game 👉 hubs.la/Q044Fd4M0

Password had a good run, but trust shouldn't hinge on secrets people reuse everywhere. How do you actually feel about passwords and authentication in 2026? 🧐