Tinybird
@tinybird.co
The analytics backend for your app. Ship software with big data requirements faster and more intuitively than you ever thought possible.
Pinned
Tinybird
@tinybird.co
· Mar 14
Introducing Tinybird Forward: Ship software with big data requirements.
YouTube video by Tinybird
youtu.be
🌅 Tinybird Forward is here »
Forward is a major evolution of Tinybird, designed to make shipping software with big data requirements faster and more intuitive.
No complex infra project. No context switching. No esoteric architectures. Just code.
(🔊 sound on! 👇)
youtu.be/vaSjWu3XFdY
Forward is a major evolution of Tinybird, designed to make shipping software with big data requirements faster and more intuitive.
No complex infra project. No context switching. No esoteric architectures. Just code.
(🔊 sound on! 👇)
youtu.be/vaSjWu3XFdY
Two weeks ago, we published v1 of our LLM SQL Generation Benchmark. Your feedback was generous, thoughtful, and sometimes brutally honest.
Here's what we got wrong, what we got right (but didn’t explain), and how we’re improving the benchmark for round two.
tbrd.co/llm-sql-rd2
Here's what we got wrong, what we got right (but didn’t explain), and how we’re improving the benchmark for round two.
tbrd.co/llm-sql-rd2
We graded 19 LLMs on SQL. You graded us.
We benchmarked 19 LLMs on analytical SQL and the internet had thoughts. Here's a breakdown of your feedback, what we got wrong, what we got right (but didn’t explain), and how we’re improving the…
tbrd.co
May 16, 2025 at 11:01 AM
Two weeks ago, we published v1 of our LLM SQL Generation Benchmark. Your feedback was generous, thoughtful, and sometimes brutally honest.
Here's what we got wrong, what we got right (but didn’t explain), and how we’re improving the benchmark for round two.
tbrd.co/llm-sql-rd2
Here's what we got wrong, what we got right (but didn’t explain), and how we’re improving the benchmark for round two.
tbrd.co/llm-sql-rd2
Read the full case study here: tbrd.co/phantom
Phantom gives crypto traders faster buy/sell decisions with Tinybird
Learn how the most popular Solana wallet processes 2.5B+ daily events for 15M+ users while maintaining sub-50ms query latency.
tbrd.co
May 12, 2025 at 3:12 PM
Read the full case study here: tbrd.co/phantom
One of the most popular crypto wallets in the world, Phantom, builds features with Tinybird to increase revenue and swaps by displaying trending tokens to end-users in milliseconds.
Top quote from their Sr. Data Engineer ↓
Top quote from their Sr. Data Engineer ↓
May 12, 2025 at 3:12 PM
One of the most popular crypto wallets in the world, Phantom, builds features with Tinybird to increase revenue and swaps by displaying trending tokens to end-users in milliseconds.
Top quote from their Sr. Data Engineer ↓
Top quote from their Sr. Data Engineer ↓
Reposted by Tinybird
How could I have missed it
@tinybird.co just solved the biggest pain point to self host @openstatus.dev with their local container 🥰
www.tinybird.co/docs/forward...
@tinybird.co just solved the biggest pain point to self host @openstatus.dev with their local container 🥰
www.tinybird.co/docs/forward...
Tinybird Local container (Forward) · Tinybird Docs
Use the Tinybird Local container to run Tinybird locally and in CI workflows.
www.tinybird.co
April 24, 2025 at 9:56 PM
How could I have missed it
@tinybird.co just solved the biggest pain point to self host @openstatus.dev with their local container 🥰
www.tinybird.co/docs/forward...
@tinybird.co just solved the biggest pain point to self host @openstatus.dev with their local container 🥰
www.tinybird.co/docs/forward...
Reposted by Tinybird
May 4, 2025 at 7:18 PM
Despite the proliferation of AI chat apps, Explorations wasn't a simple build. Read the engineering post with our experience on LLM orchestration, prompt chaining, tools definition, system prompts, and handling unexpected LLM outputs -> tbrd.co/explorations_tech
Building Explorations A Conversational Analytics Ai
Visit www.tinybird.co/blog-posts/building-explorations-a-conversational-analytics-ai
tbrd.co
May 6, 2025 at 1:02 PM
Despite the proliferation of AI chat apps, Explorations wasn't a simple build. Read the engineering post with our experience on LLM orchestration, prompt chaining, tools definition, system prompts, and handling unexpected LLM outputs -> tbrd.co/explorations_tech
You can read more about Explorations in our announcement post -> tbrd.co/explorations
May 6, 2025 at 1:02 PM
You can read more about Explorations in our announcement post -> tbrd.co/explorations
Here's what you can do with Explorations:
- Query data in your natural language
- Build notebook-style analysis
- Customize output with rules
- Visualize results as timeseries charts
- Fix errors automatically
- Query data in your natural language
- Build notebook-style analysis
- Customize output with rules
- Visualize results as timeseries charts
- Fix errors automatically
May 6, 2025 at 1:02 PM
Here's what you can do with Explorations:
- Query data in your natural language
- Build notebook-style analysis
- Customize output with rules
- Visualize results as timeseries charts
- Fix errors automatically
- Query data in your natural language
- Build notebook-style analysis
- Customize output with rules
- Visualize results as timeseries charts
- Fix errors automatically
Before you write your first pipe, you must understand your data.
This takes time. It starts with SELECT * … LIMIT 1 and ends with many open SQL docs tabs.
Explorations reduces time-to-first-API by turning natural language queries into optimized & contextualized SQL.
This takes time. It starts with SELECT * … LIMIT 1 and ends with many open SQL docs tabs.
Explorations reduces time-to-first-API by turning natural language queries into optimized & contextualized SQL.
May 6, 2025 at 1:02 PM
Before you write your first pipe, you must understand your data.
This takes time. It starts with SELECT * … LIMIT 1 and ends with many open SQL docs tabs.
Explorations reduces time-to-first-API by turning natural language queries into optimized & contextualized SQL.
This takes time. It starts with SELECT * … LIMIT 1 and ends with many open SQL docs tabs.
Explorations reduces time-to-first-API by turning natural language queries into optimized & contextualized SQL.
dbt shook up the data world in 2016.
We like dbt. But it's not suited for real-time workloads (because data warehouses aren't suited for real-time workloads).
Tinybird is a lot like dbt. But it's also different... 👀
tbrd.co/dbt-real-time
We like dbt. But it's not suited for real-time workloads (because data warehouses aren't suited for real-time workloads).
Tinybird is a lot like dbt. But it's also different... 👀
tbrd.co/dbt-real-time
dbt in real-time
Tinybird is kind of like dbt, but for real-time use cases. Here's how and why you might migrate real-time API use cases from dbt to Tinybird.
tbrd.co
April 24, 2025 at 4:54 PM
dbt shook up the data world in 2016.
We like dbt. But it's not suited for real-time workloads (because data warehouses aren't suited for real-time workloads).
Tinybird is a lot like dbt. But it's also different... 👀
tbrd.co/dbt-real-time
We like dbt. But it's not suited for real-time workloads (because data warehouses aren't suited for real-time workloads).
Tinybird is a lot like dbt. But it's also different... 👀
tbrd.co/dbt-real-time
In post 3, we compared Reddit's original architecture to the optimized Tinybird approach in terms of cost, performance, and complexity -> tbrd.co/100bpt3
April 21, 2025 at 7:00 PM
In post 3, we compared Reddit's original architecture to the optimized Tinybird approach in terms of cost, performance, and complexity -> tbrd.co/100bpt3
In post 2, we considered how to effectively scale the counter -> tbrd.co/100bpt2
April 21, 2025 at 7:00 PM
In post 2, we considered how to effectively scale the counter -> tbrd.co/100bpt2
In post 1, we offered a very simple approach to count 100B rows in Tinybird -> tbrd.co/100bpt1
April 21, 2025 at 7:00 PM
In post 1, we offered a very simple approach to count 100B rows in Tinybird -> tbrd.co/100bpt1
We just wrapped a blog series on counting 100 billion rows efficiently.
The takeaway? Tinybird is fast and cost-effective when it comes to real-time aggregation at scale. Duh.
Links to all 3 below ↓
The takeaway? Tinybird is fast and cost-effective when it comes to real-time aggregation at scale. Duh.
Links to all 3 below ↓
April 21, 2025 at 7:00 PM
We just wrapped a blog series on counting 100 billion rows efficiently.
The takeaway? Tinybird is fast and cost-effective when it comes to real-time aggregation at scale. Duh.
Links to all 3 below ↓
The takeaway? Tinybird is fast and cost-effective when it comes to real-time aggregation at scale. Duh.
Links to all 3 below ↓
Quick update on this ↓
Spots are filling up. We're ~75% full with a week to go. If you want to meet other #NewYorkCity devs building real-time data applications and learn from someone who has actually built and scaled the thing in prod, register now.
Register: lu.ma/9wazu8zx
Spots are filling up. We're ~75% full with a week to go. If you want to meet other #NewYorkCity devs building real-time data applications and learn from someone who has actually built and scaled the thing in prod, register now.
Register: lu.ma/9wazu8zx
April 21, 2025 at 4:01 PM
Quick update on this ↓
Spots are filling up. We're ~75% full with a week to go. If you want to meet other #NewYorkCity devs building real-time data applications and learn from someone who has actually built and scaled the thing in prod, register now.
Register: lu.ma/9wazu8zx
Spots are filling up. We're ~75% full with a week to go. If you want to meet other #NewYorkCity devs building real-time data applications and learn from someone who has actually built and scaled the thing in prod, register now.
Register: lu.ma/9wazu8zx
Where my Copenhagen devs at?
Next Wednesday we're running a Tinybird Hackathon at the @BlastTV offices in Copenhagen 🇩🇰.
Learn how Blast built their stats leaderboard for the game Deadlock, and get hands-on experience building real-time analytics APIs.
Register here: lu.ma/wafkvnza
Next Wednesday we're running a Tinybird Hackathon at the @BlastTV offices in Copenhagen 🇩🇰.
Learn how Blast built their stats leaderboard for the game Deadlock, and get hands-on experience building real-time analytics APIs.
Register here: lu.ma/wafkvnza
April 16, 2025 at 1:45 PM
Where my Copenhagen devs at?
Next Wednesday we're running a Tinybird Hackathon at the @BlastTV offices in Copenhagen 🇩🇰.
Learn how Blast built their stats leaderboard for the game Deadlock, and get hands-on experience building real-time analytics APIs.
Register here: lu.ma/wafkvnza
Next Wednesday we're running a Tinybird Hackathon at the @BlastTV offices in Copenhagen 🇩🇰.
Learn how Blast built their stats leaderboard for the game Deadlock, and get hands-on experience building real-time analytics APIs.
Register here: lu.ma/wafkvnza
There are 6 functions to get a unique count in Tinybird:
1. uniq
2. uniqExact
3. uniqCombined
4. uniqCombined64
5. uniqHLL2
6. uniqTheta
Each one has tradeoffs.
Read how to combine them to efficiently count billions of unique IDs. ↓
1. uniq
2. uniqExact
3. uniqCombined
4. uniqCombined64
5. uniqHLL2
6. uniqTheta
Each one has tradeoffs.
Read how to combine them to efficiently count billions of unique IDs. ↓
The simplest way to count 100B unique IDs: Part 2
How to make a simple counter scale to trillions by using the right count functions paired with pre-aggregations
tbrd.co
April 11, 2025 at 4:04 PM
There are 6 functions to get a unique count in Tinybird:
1. uniq
2. uniqExact
3. uniqCombined
4. uniqCombined64
5. uniqHLL2
6. uniqTheta
Each one has tradeoffs.
Read how to combine them to efficiently count billions of unique IDs. ↓
1. uniq
2. uniqExact
3. uniqCombined
4. uniqCombined64
5. uniqHLL2
6. uniqTheta
Each one has tradeoffs.
Read how to combine them to efficiently count billions of unique IDs. ↓
April 10, 2025 at 3:04 PM
The basic process:
1. Create an API to pass input to an LLM
2. Pass user input + sys prompt to the LLM
3. Have the LLM return structured filters
4. Fetch your data API using the LLM filters
The key is a good (dynamic!) system prompt & a fast analytics backend (👋 Tinybird).
1. Create an API to pass input to an LLM
2. Pass user input + sys prompt to the LLM
3. Have the LLM return structured filters
4. Fetch your data API using the LLM filters
The key is a good (dynamic!) system prompt & a fast analytics backend (👋 Tinybird).
April 10, 2025 at 3:04 PM
The basic process:
1. Create an API to pass input to an LLM
2. Pass user input + sys prompt to the LLM
3. Have the LLM return structured filters
4. Fetch your data API using the LLM filters
The key is a good (dynamic!) system prompt & a fast analytics backend (👋 Tinybird).
1. Create an API to pass input to an LLM
2. Pass user input + sys prompt to the LLM
3. Have the LLM return structured filters
4. Fetch your data API using the LLM filters
The key is a good (dynamic!) system prompt & a fast analytics backend (👋 Tinybird).
You can reclaim some UI space by distilling filter UIs into a clean free-text prompt and use an LLM to parse the result.
@dubdotco has a good example. With 20 high-cardinality filter dimensions, Dub simplifies the filter UI by prioritizing free-text AI input ↓
@dubdotco has a good example. With 20 high-cardinality filter dimensions, Dub simplifies the filter UI by prioritizing free-text AI input ↓
April 10, 2025 at 3:04 PM
You can reclaim some UI space by distilling filter UIs into a clean free-text prompt and use an LLM to parse the result.
@dubdotco has a good example. With 20 high-cardinality filter dimensions, Dub simplifies the filter UI by prioritizing free-text AI input ↓
@dubdotco has a good example. With 20 high-cardinality filter dimensions, Dub simplifies the filter UI by prioritizing free-text AI input ↓
Filters are important for any dashboard. But when the number of filter dimensions grows, UI components for filtering can get clunky and eat up a lot of space.
👇 See how much real estate this sidebar takes?
👇 See how much real estate this sidebar takes?
April 10, 2025 at 3:03 PM
Filters are important for any dashboard. But when the number of filter dimensions grows, UI components for filtering can get clunky and eat up a lot of space.
👇 See how much real estate this sidebar takes?
👇 See how much real estate this sidebar takes?
💡 Quick win to improve the UX of your real-time analytics dashboards: Add an "Ask AI" feature. ↓
More info on how to do it in the 🧵
More info on how to do it in the 🧵
April 10, 2025 at 3:03 PM
💡 Quick win to improve the UX of your real-time analytics dashboards: Add an "Ask AI" feature. ↓
More info on how to do it in the 🧵
More info on how to do it in the 🧵
Just announced: Real-time Data Meetup with Tinybird, Estuary, and PlayOn! Sports.
3 talks.
A room full of devs and data people.
Free food and imbibements.
April 29th at 6 PM ET
FirstMark Capital - NYC
Register here: lu.ma/9wazu8zx
3 talks.
A room full of devs and data people.
Free food and imbibements.
April 29th at 6 PM ET
FirstMark Capital - NYC
Register here: lu.ma/9wazu8zx
April 7, 2025 at 4:00 PM
Just announced: Real-time Data Meetup with Tinybird, Estuary, and PlayOn! Sports.
3 talks.
A room full of devs and data people.
Free food and imbibements.
April 29th at 6 PM ET
FirstMark Capital - NYC
Register here: lu.ma/9wazu8zx
3 talks.
A room full of devs and data people.
Free food and imbibements.
April 29th at 6 PM ET
FirstMark Capital - NYC
Register here: lu.ma/9wazu8zx