Firebolt

Firebolt is the Analytical Database for Real-time Applications.

11/27/2025

What do analytics and re:Invent have in common?
They both run better with the right connections. 🤝

We’re heading to to connect builders and data leaders with AI-era performance.

Want to connect while we’re there?
📅 Book some time with the team to discuss your toughest data challenges → https://okt.to/ivZGJY

🍸 Or join us at on Monday at Cañonita → https://okt.to/fbzJaI

11/26/2025

Real-Time Vector Search at Scale: 100× Faster. ⚡

Every company talks about vector search - few have taken it to production at scale. Similarweb did.

By building on Firebolt, they delivered semantic search across hundreds of millions of rows - achieving 100× faster retrievals without a specialized vector .

Join this deep technical to see how Firebolt engineered the solution from Hackathon to in just three months, and how Similarweb achieved these massive gains on live workloads.

Save your spot 👉 https://okt.to/IZFurb

11/25/2025

We’re heading to AI London 🎡

Whether you're building apps, scaling agentic systems, or just sick of slow dashboards… come by 𝗕𝗼𝗼𝘁𝗵 𝗚𝟭𝟬𝟰 to see how we’re helping teams ship faster and query bigger.

11/20/2025

Firebolt 🤝 Mitzu

We’re excited to announce that Mitzu now integrates directly with Firebolt!

You can now run Mitzu directly on your Firebolt database. This unlocks instant journey mapping by backing Mitzu’s deep exploration capabilities with Firebolt’s query performance.

Get set up here: https://okt.to/LCodh5

11/19/2025

Heading to ? So are we - and we're bringing the team that makes data move faster:

🧠 Darlene Volas - engineering fast, flexible pipelines for apps.
🤝 Bryan Emig - bringing Firebolt’s efficiency to data and teams.
🌐 Abhishek Reddy - connecting the ecosystem powering low-latency .

They’ll be meeting with data and engineering leaders throughout the week.

Want to connect?
📅 Book a meeting → https://okt.to/748x1B
🎉 Or join us at on Mon Dec 1 at Cañonita → https://okt.to/OtsCQP

11/18/2025

Tomorrow: joins us to show how they deliver sub-second on live transactional data - powered by Firebolt.

We’ll cover:
🔹 How vectorized ex*****on and aggressive pruning power queries
🔹 Why first-class upserts make mutable data painless
🔹 What it takes to run + workloads together, at scale

📅 Save your seat: firebolt.io/lp/solving-the-olap-oltp-workload-problem-how-merchjar-achieves-sub-second-analytics-on-transactional-data

11/17/2025

Running compute-optimized workloads on Firebolt? You’ll see a performance boost.⚡

Our compute-optimized clusters now run on AWS (c7gd) - swapping x86 for ARM to squeeze out more performance per core.

You get:
🔹 Faster CPU performance, DDR5 memory (50% higher bandwidth), and higher network throughput
🔹 Proven real-world gains - Tripledot's heavy workloads ran consistently faster

🧠 Full breakdown: https://okt.to/TIn18W

11/13/2025

Analytical speed or operational flexibility - modern data teams know the trade-off. ⚖️

Join us on Nov. 19 to see how , an ad optimization platform, delivers sub-second on live transactional data using Firebolt. We’ll break down how Firebolt’s first-class upserts, vectorized ex*****on, and aggressive make it happen.

📅 Save your seat: https://okt.to/bgxSdo

11/11/2025

Happening tomorrow 🗓️

Learn how to make your assistant actually analyze data - not just talk about it.
We’ll show how to connect it directly to Firebolt using the Model Context Protocol ( ) and query data in milliseconds.

👉 Save your spot at the : https://okt.to/4caxbR

11/10/2025

CRN thinks we’re stellar - and we’ll take it. 😎

Honored to be recognized for building the analytical powering real-time applications - where queries, complex joins, and heavy batch all run efficiently in one place.

🔗 Check out the list of in Big Data: https://okt.to/a1QRZp

11/06/2025

Are you treating your ingestion and transformation layers like traditional data engineering work?
That’s a mistake that costs you time and money. 💸

Ashok Singamaneni, Principal Data Engineer at Nike says it best:
👉 “Treat that as a software product, not like a data engineering product.”

Why? Because a software mindset demands checks and balances:
🔹 Data quality checks
🔹 Unit testing
🔹 Integration testing

This rigor pays off - as your product scales, tracing issues gets exponentially harder.
Treat your pipeline like software, and make debugging painless.

🎧 Tune in: https://okt.to/gHdGko
Spotify: https://okt.to/9YedoO

11/05/2025

Most AI assistants can talk about data - but can’t analyze it.

Join our hands-on virtual to learn how to connect an assistant directly to Firebolt’s analytical engine using the Model Context Protocol ( ) - the open standard for linking to live data.

Run queries and return grounded results in milliseconds. ⚡

🗓️ Save your seat: https://okt.to/HX8IcU

Address

Palo Alto, CA

Website

https://www.firebolt.io/

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