🚀 Big news: PuppyGraph raised $5M in seed funding, led by @defyvc! Huge thanks to @bmrothenberg, @yin_amy, and Adam Park for believing in us! 💥. We’ve been on fire, shipping many new features like #AgenticGraphRAG, interactive graph exploration, streaming support, and more! 🌐

Big shoutout to Jordano Mark from @dremio for breaking down how clustering and Z-ordering can supercharge your Iceberg queries. Lakehouse tuning never looked so good. Thanks to everyone who came out to the NYC Apache Iceberg Meetup!. #ApacheIceberg #Dremio #IcebergClustering

Roy Hasson (@royhasson) from @Microsoft closed out the night with a look at the next big shift in data platforms. He unpacked how open table formats like Apache Iceberg and object storage are reshaping the landscape, from OneLake to OTF-as-a-Service and what it all means for data

Zoe Steinkamp from @ClickHouseDB showed how you can run lightning-fast analytics on Apache Iceberg without the usual pipeline headaches. Real stories, real speed, and smart tips for getting sub-second queries straight from your data lake. Thanks to everyone who came out to the

Sean Sullivan from @Grubhub gave us a solid rundown on S3—the cloud storage backbone behind so many data platforms. He covered the nuts and bolts, best practices, and what not to do when working with S3 in the wild. Thanks to everyone who came out to the NYC Apache Iceberg

Matthew Topol (@zeroshade) from @columnar_tech gave us a great look at how Golang and Apache Arrow make working with Iceberg fast, simple, and scalable. He showed how to handle schema evolution, batch reads, and efficient writes using the iceberg-go library. Thanks to everyone

Shubham Satish Baldava (@cto_datazip) from @_olake shared how to rethink ELT for Apache Iceberg with lightweight, high-speed pipelines built in Golang. No Spark, no Flink, just fast, efficient ingestion and smart optimizations. Thanks to everyone who came out to the NYC Apache

Huge shoutout to our CEO Weimo Liu (@wmliu) for showing how Apache Iceberg and graph analytics work together to catch fraud that hides in plain sight. He walked through a real fintech use case that used Iceberg tables and graph patterns to surface fraud rings and risky

Yuri Zarubin from @awscloud gave a super helpful and info-packed talk on how S3 Tables make Apache Iceberg storage way easier to manage. No more chaos, just clean, scalable setup that actually works. Thanks to everyone who came out to the NYC Apache Iceberg Meetup!

Yuval Yogev (@yogevyuval) from @ryftdata dug into the real challenges of streaming data into Apache Iceberg. He covered small files, compaction, late-arriving data, and how to keep high-throughput pipelines running smooth without wrecking performance. Thanks to everyone who came

Yingjun Wu (@YingjunWu) from @RisingWaveLabs pulled back the curtain on what really makes streaming Postgres data into Apache Iceberg tricky. He talked TOAST, wild schema changes, late-arriving data, and tiny files, then showed how they got a real-time CDC pipeline working

Dr. Adnan Masood (@adnanmasood) from @USTglobal explored what it really takes to get Apache Iceberg ready for AI and LLM-scale workloads. He talked reproducible ML pipelines, early adoption hurdles, and how tools like PuppyGraph can surface graph lineage to bring much-needed

Viktor Gamov (@gAmUssA) from @confluentinc unpacked the challenge of querying real-time streams and how tools like Iceberg, Flink, Pinot, and Kafka fit into the puzzle. Thanks to everyone who came out to the NYC Apache Iceberg Meetup!. #ApacheIceberg #Confluent #KafkaStreams

Matt Forrest from @wherobots showed how to use Apache Iceberg with the new V3 spec for geospatial workloads. He shared a real-world use case using US river gauge data to analyze flooding in real time across the country. Thanks to everyone who came out to the NYC Apache Iceberg

Ron Kapoor from @CelerData dropped gems on speeding up Iceberg queries using materialized views—no data duplication, no rewrites, just fast results. Thanks to everyone who came out to the NYC Apache Iceberg Meetup!. #ApacheIceberg #CelerData #MaterializedViews #LowLatencyQueries

Huge thanks to Andrew Lamb (@andrewlamb1111) from @InfluxDB for showing how the new Variant type is changing how we handle messy, semi-structured data in Iceberg and Parquet. Thanks to everyone who came out to the NYC Apache Iceberg Meetup!. #ApacheIceberg #InfluxData #Parquet

200+ data pros. 15 talks. 1 unforgettable night in NYC. ❄️🔥. Thanks to our cohosts @awscloud @databricks @snowflake @risingwave @CelerData @dremio @ClickHouseDB @wherobots + all our amazing speakers!. Couldn’t make it? No FOMO. Here’s the recap 🎥👇

RT @Minio: New tutorial from @BrennaBuuck on how to build a #cloudnative graph analytics stack without migrating your data. Query structure….

RT @anujbpanchal: Discover how @MongoDB Atlas and @puppyquery let you query @awscloud CloudTrail logs as a virtual graph enabling security….

RT @Minio: New tutorial from @BrennaBuuck on how to build a #cloudnative graph analytics stack without migrating your data. Query structure….

RT @TuckLyeKoh1: Great catching up with Weimo Liu and super excited to know that @puppyquery is making great progress! .