The PuppyGraph team is in Miami and bringing the heat ๐ด๐ฅ
Weโre at @starburstdata#AIDataNova2026 with adorable puppy goodies and a whole lot of purple. You canโt miss us.
Come for the swag, stay for the graph talk. Weโre talking knowledge graphs, security graphs, fraud graphs, and how to give your agents a semantic layer that understands connected data.
Stop by our booth and say hi ๐ถ๐
If youโre in the Bay Area tomorrow and looking for an excuse to hang out with the @ApacheIceberg community. Come join us at the Bay Area Apache Iceberg meetup (https://t.co/920tD5c1tk)
Engineers from @awscloud, @puppyquery , @databricks, @Snowflake, @Cisco, hyperparam, supermetal, @LakeSailHQ, and @ZetaGlobal will keep you up to date on the latest and greatest.
Cool project and great minds alike!
Zhenni, cofounder of PuppyGraph here! Just want to clarify that thinking Puppygraph is not built for agentic workloads is simply wrong ๐ We have been empowering many big enterprises like AMD, Datadog and AI startups like Sola Security for their agent workloads.
In fact, many of our users told us that weโre the ONLY graph engine solutions that can query thousands of tables with < 3s performance. Not only we support PostgreSQL, we also support 20+ connectors, so many users use us as a federated query engine across all their data sources with zero ETL.
๐ช๐ฒ ๐ฝ๐๐น๐น๐ฒ๐ฑ ๐๐ต๐ฒ ๐น๐ผ๐ด๐. ๐ง๐ต๐ฒ ๐๐ฐ๐ฒ๐ฏ๐ฒ๐ฟ๐ด ๐ ๐ฒ๐ฒ๐๐๐ฝ: ๐ฅ๐ฆ๐ ๐๐ฑ๐ถ๐๐ถ๐ผ๐ป ๐ฟ๐ฒ๐ฐ๐ฎ๐ฝ ๐ถ๐ ๐น๐ถ๐๐ฒ ๐ฌ๐ง
During one of the busiest cybersecurity weeks of the year, we stepped away from the RSA crowds and into the AWS SF office for a technical evening on Apache Iceberg and scalable cyber infrastructure.
One thing was clear from the room: Iceberg is showing up in more real production use cases. For RSA Edition, the focus was massive threat data, modern data lakes, investigation workflows, and where Iceberg fits into the cyber data stack.
Huge shoutout to the speakers who made the night worth the walk from Moscone:
๐ง Leticia Webb (@ClickHouseDB)
๐ง Colin Gibbens & Paul Agbabian (@splunk)
๐ง Peyman Mani (@cogent_security)
๐ง Austin Groeneveld (@awscloud)
๐ง Yiheng An & Chao Lei (@PaloAltoNtwks )
๐ง Weimo Liu (@puppyquery )
And thank you to our amazing co-hosts and organizers for bringing the community together:
Amy Krishnamohan โข Nathan Yee (@awscloud), Zoe Steinkamp (@ClickHouseDB), Zhenni Wu โข Jaz Samantha Ku (@puppyquery) ๐
Couldnโt join us in March? Watch the highlights below ๐
And if you want to keep the conversation going, weโre back in the Bay Area on May 21 with @awscloud and @databricks ๐
Grab your spot here: https://t.co/WLp08xTqv6
#ApacheIceberg #Cybersecurity #DataLakehouse #DataEngineering #SecurityAnalytics
Thanks @puppyquery ! You have helped find relationships between data that I never dreamed of! Everyone needs to learn about Knowledge Graphs, ontology, semantic fields and collocations. I honestly believe that the end of apps is near. Build a knowledge graph and create an OpenAPI to access it. Knowledge is power. I can see how this knowledge will open new doors and opportunities. @KGConference
Changing your graph schema shouldnโt mean ๐ฟ๐ฒ๐น๐ผ๐ฎ๐ฑ๐ถ๐ป๐ด all your data.
But with the traditional graph pipeline, thatโs often the tradeoff:
๐ฆ Move data into a graph database
๐งฉ Define the schema up front
๐ Reload when the model changes
That slows teams down before they even get to the fun part: querying relationships.
With PuppyGraph, the data stays in tables while the graph schema sits logically on top.
Physically, tables.
Logically, a graph. ๐
Check out the clip below to see how this makes graph adoption much easier. ๐
#GraphAnalytics #GraphDatabase #DataEngineering #DataArchitecture #ZeroETL
Come wrap up spring with the ๐๐ฎ๐ ๐๐ฟ๐ฒ๐ฎ ๐๐ฐ๐ฒ๐ฏ๐ฒ๐ฟ๐ด ๐ฐ๐ฟ๐ฒ๐ ๐ง๐ธ
@puppyquery is proud to co-host the @ApacheIceberg Meetup with @awscloud and @databricks, and youโll want to be in the room for this one ๐
๐ ๐ ๐ฎ๐ ๐ฎ๐ญ | โฐ ๐ฑ:๐ฌ๐ฌโ๐ด:๐ฌ๐ฌ ๐ฃ๐ |๐ ๐๐๐ก๐ข๐ฃ๐ฌ ๐ ๐ฒ๐ป๐น๐ผ ๐ฃ๐ฎ๐ฟ๐ธ
Weโre bringing Iceberg builders together for an evening of technical talks, real production stories, and lessons that usually only come from doing the hard parts yourself.
Expect sessions on:
๐พ Iceberg in production, not just in theory
๐พ Architecture choices, technical lessons, and edge cases
๐พ What builders are learning as Iceberg matures
Plus plenty of time to swap notes, chat with other builders, and geek out with the Iceberg community.
And weโre not letting you leave hungry: unagi don, spicy stir fry, and more (desserts included!) ๐ฑ๐ถ๏ธ
If youโre building on Iceberg, curious about Iceberg, or just want to meet the folks deep in the lakehouse weeds, come hang out.
๐๏ธ ๐ฅ๐ฒ๐ด๐ถ๐๐๐ฒ๐ฟ ๐ณ๐ผ๐ฟ ๐ณ๐ฟ๐ฒ๐ฒ: https://t.co/mBHi2bRSuC
๐ค ๐ช๐ฎ๐ป๐ ๐๐ผ ๐ฝ๐ฟ๐ฒ๐๐ฒ๐ป๐? Submit your CFP here: https://t.co/nX4CdAjHMx
Big thanks to the organizers: Scott Haines and Lisa N. Cao (@databricks), Amy Krishnamohan and Nathan Yee (@awscloud), and Zhenni Wu, Weimo Liu, and Jaz Samantha Ku (@puppyquery) ๐๐
Cybersecurity questions are rarely about a single event or a single asset.
They are about relationships.
Which identities can reach which systems?
How do permissions connect across resources?
Where does exposure exist?
And if one system is compromised, what else becomes at risk?
Cyber attacks unfold in moments.
So, answering these questions requires tracing paths across the latest state of your infrastructure in real time.
But most security stacks are not built for that.
We are hosting a webinar with PuppyGraph to show what an architecture that actually solves this looks like.
Our speakers:
Yingjun Wu, Founder and CEO at RisingWave
WeimoLiu, Co-Founder and CEO at PuppyGraph
If you are working on security data, observability, or anything that requires tracing relationships across infrastructure in real-time, this webinar is for you!
Register here: https://t.co/E7gGcgQPXW
AI is changing cybersecurity on both sides: ๐ฉ๐ฐ๐ธ ๐ข๐ต๐ต๐ข๐ค๐ฌ๐ด ๐ฉ๐ข๐ฑ๐ฑ๐ฆ๐ฏ, ๐ข๐ฏ๐ฅ ๐ฉ๐ฐ๐ธ ๐ต๐ฆ๐ข๐ฎ๐ด ๐ณ๐ฆ๐ด๐ฑ๐ฐ๐ฏ๐ฅ.
As models become more capable, theyโre powering more sophisticated attacks while also enabling stronger defenses. This puts more pressure on the data layer to keep security context fresh, connected, and ready for real-time detection.
Thatโs what Weimo Liu @wmliu (@puppyquery ) and Yingjun Wu @YingjunWu (@RisingWaveLabs) will explore in our live webinar on cybersecurity + graph analytics on streaming data.
๐ May 7 | โฐ 9:00โ10:00 AM
This session looks at how to keep up:
๐พ Real-time data processing with RisingWave
๐พ Subsecond graph queries on fresh data with PuppyGraph
๐พ Faster detection with multi-hop reasoning across live relationships
๐พ An agent-friendly setup with ontology-enforced graph querying
Weโll use a cybersecurity demo to show how streaming data and graph queries can work together for faster investigation.
๐ Save your spot: https://t.co/rNpzkFAPJW
#Cybersecurity #GraphAnalytics #StreamingData #AIInfrastructure #DataEngineering
PuppyGraph is honored to be featured in the Google Cloud Lakehouse partner ecosystem.
For our team, this was a real moment to pause. Our logo is now sitting next to companies weโve looked up to for years, in a space weโve cared deeply about from day one.
Having PuppyGraph included in ๐๐ฉ๐ฆ ๐จ๐ฐ๐ท๐ฆ๐ณ๐ฏ๐ฆ๐ฅ ๐ฐ๐ฑ๐ฆ๐ฏ ๐ญ๐ข๐ฌ๐ฆ๐ฉ๐ฐ๐ถ๐ด๐ฆ: ๐๐ค๐ฉ๐ช๐ฆ๐ท๐ฆ ๐ช๐ฏ๐ต๐ฆ๐ณ๐ฐ๐ฑ๐ฆ๐ณ๐ข๐ฃ๐ช๐ญ๐ช๐ต๐บ ๐ข๐ฏ๐ฅ ๐ถ๐ฏ๐ช๐ง๐ช๐ฆ๐ฅ ๐ค๐ฐ๐ฏ๐ต๐ฆ๐น๐ต with Ahmet Altay, Vinod Ramachandran, and Sumeet Singh made the moment feel even bigger.
We started with a simple belief: teams should be able to query their existing data as a graph, without moving it into a separate graph database. Seeing that idea become part of a broader lakehouse ecosystem means a lot.
As AI agents create new demands for context, semantics, and interoperability, weโre proud to help bring graph-powered context to lakehouse data.
No data movement. No new source of truth. Just connected context where teams already work.
Big thanks again to Jobin George, Talat Uyarer, and the entire Google Cloud team for the partnership and support that made this launch possible. ๐
๐ PuppyGraph is proud to be the launch partner for @Google Cloud Lakehouse! With support for the Iceberg REST Catalog API.
Unveiled today at #GoogleCloudNext 2026, GCPโs new Iceberg-native Lakehouse gives you open, managed #Iceberg tables. PuppyGraph turns those tables into an enforced ontology your AI agents can actually trust.
What does this integration gives you:
๐พ Multi-hop graph queries directly on Google Cloud Lakehouse data
๐พ An enforced ontology layer for AI agents
๐พ Sub-second performance at petabyte scale
๐พ openCypher + Gremlin on your existing tables (no graphDB required)
๐พ Built for agentic workloads, security, fraud, and more
When relationships matter, tables alone arenโt enough.
PuppyGraph adds structure on top of Lakehouse tables so agents can access context, validate queries, and recover when they get things wrong.
Big thanks to Jobin George, Talat Uyarer (@talatuyarer), and the entire Google Cloud team for the partnership and support that made this launch possible. ๐
๐ฅ Teaser video below
๐ Full blog here: https://t.co/ZSQvjlWgtk
What a fun time at #DEOF2026 ๐ถ
We had a blast meeting so many thoughtful data engineers and hearing how teams are architecting frontier solutions across AI, analytics, and modern data infrastructure. So many great conversations, big ideas, and inspiring builds packed into one event.
Safe to say the coffee cart was a hitโฆ engineers need their caffeine โ๏ธ
Huge shoutout to Xinran Waibel and @dataengthings for putting together such an awesome gathering. Weโre so glad we got to be part of it.
#DEOF2026 #DataEngineering #GraphAnalytics #AIInfrastructure #DataArchitecture
See how to query Apache Doris data as a graph:
@puppyquery lets you run graph queries directly on Apache Doris. No need for ETL into a dedicated graph database.
Use Cases:
- Cyber Security
- Anti-Fraud
- Fail Text-to-SQL
- Supply Chain
๐https://t.co/Kfl2pDgec8
Knowledge graphs โ ontologies ๐ถโ๐ซ๏ธ
As LLMs and agentic systems start depending on structured, meaningful data to reason over, both terms keep coming up. Often in ways that blur together.
That is where things get messy.
The confusion doesn't just live in terminology. It shows up in how teams model data, scope graph projects, and think about semantics in production.
These concepts are related, but treating them as the same thing leads to the wrong architecture and the wrong expectations.
We'll explore what each one does, how they work together, and what that looks like in practice.
Full breakdown in the blog ๐ Link at the end of the carousel ๐
#KnowledgeGraphs #Ontology #LLMInfrastructure #AgenticAI #DataArchitecture