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Artie is what you'd get if Fivetran and Confluent had a baby.
Moving high volumes…
Moving high volumes…
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Jacqueline Cheong posted thisFor a long time, I thought Iceberg had no real value. Save cost? Not really. Not once you factor in the engineering overhead and expertise required to run it. Simpler? Definitely not. If anything, it adds more moving pieces and more more failure modes when things go wrong. So for a while, I didn’t get the hype. Then over the past year, we started seeing teams use it in ways that actually matter. Not for cost savings. But to simplify architecture. A few patterns kept showing up: • Multi-engine without duplication Instead of replicating data into both Snowflake and Databricks, teams replicate once into Iceberg and query it from both. • Decoupling ingestion from the query layer Iceberg becomes the source of truth, so pipelines don’t have to change every time you switch warehouses. • Unifying streaming + batch CDC and batch jobs both land into the same place, instead of maintaining separate “real-time” and “analytics” systems. That’s when it clicked: Iceberg isn’t a cost optimization tool. It’s an organizational scaling tool. If you’re a single-engine team with simple pipelines, you probably don’t need it. But once your data stack starts fragmenting across teams, tools, and use cases… the value becomes very real. Iceberg solves a very real problem - just not the one most people think.
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Jacqueline Cheong shared thisReviewing pipeline changes should not mean diffing massive JSON blobs. We updated Artie's pipeline history to show structured JSON diffs. When a pipeline's advanced settings change, the history view now highlights only the modified keys and values instead of rendering two full JSON documents. For example, if timeouts.merge changes from 7200 to 5400, the diff shows exactly that change. Small improvement, but it makes debugging pipelines, reviewing changes, and auditing deployments much easier. Changelog in the comments
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Jacqueline Cheong reposted thisJacqueline Cheong reposted this⭐ 𝗗𝗮𝘁𝗮 & 𝗘𝗻𝗴 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗼𝗿𝘀 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁: 𝗩𝗲𝗱 𝗣𝗿𝗮𝗸𝗮𝘀𝗵, 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗮𝗹 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗮𝘁 𝗚𝗶𝘁𝗹𝗮𝗯 ⭐ We’re excited to highlight ved prakash Principal Data Engineer at GitLab and one of 17 data leaders globally on Snowflake ’s Data Superhero Council. Ved is redefining how enterprises operationalize real-time data and govern AI at scale. At GitLab, Ved built Project Siphon, an enterprise-scale CDC backbone delivering real-time streaming, sub-hour latency, and disciplined cost control. He shares lessons through talks (Berlin Buzzwords, DSC Europe) and DataAI Chronicles on next-gen real-time and AI-integrated data platforms. 𝗛𝗼𝘄 𝗵𝗮𝘀 𝘁𝗵𝗲 𝗿𝗼𝗹𝗲 𝗼𝗳 𝗮 𝗱𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗹𝗲𝗮𝗱𝗲𝗿 𝗲𝘃𝗼𝗹𝘃𝗲𝗱 𝘀𝗶𝗻𝗰𝗲 𝘆𝗼𝘂 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝘆𝗼𝘂𝗿 𝗰𝗮𝗿𝗲𝗲𝗿? Early on, leadership meant keeping pipelines running and reports accurate across ETL, queries, and infrastructure. Today it’s strategic: data leaders make multi-million dollar build vs buy decisions, shape company-wide tech direction, and tie architecture to ROI and cost efficiency. The biggest shift is owning platform economics, from Snowflake spend and AI governance to real-time systems that change how the business operates. 𝗪𝗵𝗮𝘁’𝘀 𝗮 𝗿𝗲𝗰𝗲𝗻𝘁 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗮𝗹 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝘆𝗼𝘂’𝗿𝗲 𝗽𝗿𝗼𝘂𝗱 𝗼𝗳 — 𝗮𝗻𝗱 𝘄𝗵𝘆? I’m proud of the AI Cost Control Framework we built for Snowflake Cortex at GitLab as Cortex credit usage scaled without visibility. It monitors consumption in real time, applies configurable thresholds, alerts before budgets are breached, and attributes spend to specific teams and services. As AI embeds into data platforms, proactive cost governance becomes table stakes for scaling innovation with financial discipline. 𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗺𝗶𝘀𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝗶𝗼𝗻 𝘆𝗼𝘂 𝘀𝗲𝗲 𝗮𝗯𝗼𝘂𝘁 𝗖𝗗𝗖 𝗼𝗿 𝘀𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀? CDC isn’t “just faster data.” It’s an architectural transformation involving data movement, consistency guarantees, schema evolution, and how deletes, updates, and backfills coexist with streaming changes. Downstream systems, from dbt models to BI tools to ML workloads, also need to adapt to incremental semantics. CDC requires monitoring, alerting, deep database knowledge, and tight coordination across data, platform, and application teams. 𝗪𝗵𝗲𝗿𝗲 𝗱𝗼 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗔𝗜 𝘄𝗶𝗹𝗹 𝗵𝗮𝘃𝗲 𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗶𝗺𝗽𝗮𝗰𝘁 𝗼𝗻 𝗱𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗼𝘃𝗲𝗿 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗳𝗶𝘃𝗲 𝘆𝗲𝗮𝗿𝘀? 1. Intelligent orchestration and self-healing pipelines (fewer 2am pages) 2. Code generation and platform acceleration (less boilerplate, more time for architecture) 3. Intelligent cost optimization (dynamic optimization for Snowflake/Databricks, especially where streaming and CDC costs can spike) At Artie, we’re excited to feature leaders like Ved, who are pushing the boundaries of what modern data engineering looks like.
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Jacqueline Cheong shared thisThe best data conversations I've had this year didn't happen at conferences. They happened around a dinner table with 10 people and no agenda. No slides. No pitches. Just data and engineering leaders talking honestly about what's working, what's broken, and what they're betting on next. That's why we keep hosting these. Our next Data Executives Dinner is April 16 in San Francisco - private dining room at the Four Seasons. Matthew Powers (CTO @ momoGood + Tatango) will be sharing how he rolled out data infra changes and pushed AI extensively across his org. Director of Data at ClickUp will also be at the table. 10-12 people. Always small. Always off the record. If you're a data or engineering leader in the Bay Area and this sounds like your kind of evening, DM me and I'll send you the details. And if you know someone who'd be great at this table, send them my way.
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Jacqueline Cheong shared thisWe're sunsetting real-time replication. After talking to hundreds of data teams, we've come to a difficult conclusion. Batch is good enough. The latency doesn't matter. The freshness doesn't matter. Your dashboards being 6 hours behind is fine. Nobody's making decisions that fast anyway. And AI in production? It doesn't need real-time data either. Your models will perform just fine on yesterday's data. Stale context never hurt anyone. We'll be migrating all customers to a daily sync by end of Q2. If you need fresher data, we recommend refreshing your browser. Happy April Fools. Real-time is the future. Your AI models deserve live data. And we're not going anywhere.
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Jacqueline Cheong shared thisBackfills are often the slowest part of standing up a new pipeline. So we added parallel MySQL backfills for tables with INT primary keys. Artie automatically chunks a table into primary-key ranges and backfills those ranges concurrently instead of running a single threaded scan. For example, if orders.id ranges from 1 to 200,000,000, Artie can split that range into multiple chunks and backfill them in parallel. Parallelizing the backfill turns one slow scan into multiple concurrent range reads and writes. This can dramatically reduce wall-clock time while keeping the logic simple and deterministic using PK ranges. This is especially useful for teams replicating large MySQL tables, particularly on read replicas, who want much faster initial loads without changing their schema or writing custom partitioning logic. Change log in the comments.
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Jacqueline Cheong reposted thisJacqueline Cheong reposted thisOperating Apache Iceberg in production? We’ll be at 𝗕𝗼𝗼𝘁𝗵 𝗕𝟵 at Iceberg Summit. Stop by to talk real-world Lakehouse operations: catalogs, compaction, schema evolution, interoperability. Right after: Come by for Iceberg on the Rocks, a happy hour where conversations keep flowing 👉️ https://luma.com/a6xq8l6c #IcebergSummit2026 #ApacheIceberg™ #OpenData
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Jacqueline Cheong shared thisA pipeline that fails is annoying. A pipeline you can’t debug is a nightmare. Production pipelines aren’t just about pipelines working. They’re about having deep visibility when things inevitably go wrong. But a lot of teams still evaluate data pipeline tools based on connectors and latency. The real test shows up later: “What do you see when something breaks?” During a demo someone described their troubleshooting process: Is the connector on? Is the connector off? That was about the level of observability available. The moment data becomes operationally important, that’s not enough. You need to see things like: • replication lag • flush behavior • table-level throughput • replication slot health • where the pipeline is actually stuck Because when a pipeline stops moving, the worst possible situation is this: You know it’s broken. But you have no idea why. Production pipelines shouldn't be judged on the days everything works. They should be judged on how quickly you can understand them when they don’t.
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Jacqueline Cheong shared thisThey say that good artists copy, great artists steal. So I stole directly from Marty Kausas. Almost word for word. I met Marty over dinner, and he hammered in the idea: your LinkedIn header is your billboard. So, we're officially building the AWS DMS killer. No explanation needed if you've ever used AWS DMS. And that's the point. Thank you Marty 🙏
Jacqueline Cheong commented on a post
23m
Curious for context on this one - wild. "One company walked in doing $27M ARR."
Jacqueline Cheong replied to a comment
2d
John Schwegler Great example!! branch support is basically git for data: validate before you publish, no more breaking downstream tables with a bad push. really smart pattern to call out :)
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Jacqueline Cheong liked thisJacqueline Cheong liked thisY Combinator just had its best batch ever. 14 companies hit $1M ARR by Demo Day. Last batch had 5. One company walked in doing $27M ARR. Average weekly revenue growth across the whole batch was 14%. That is freaking impressive. What I keep thinking about is a different question. Ivy League representation in W26 actually dropped to 12%, down from 27% the batch before. Berkeley produced more founders than Stanford. Only 25% had Big Tech on their resume. So the strongest batch in YC history was also the least pedigreed in recent memory. I don't think that's a coincidence. When the cost of building software drops to near zero, the value moves to the person who knows what to build. Domain expertise. Time spent inside broken workflows. Pattern recognition you can't get from a CS curriculum. We've been saying this since day one at Daring Ventures. We're pre-seed investors who believe the best founders are often the ones nobody expected. Good stuff! (Thanks for the stats, Chris Pisarski)
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Jacqueline Cheong liked thisJacqueline Cheong liked thisToday is a big day for Insight Health. We are officially announcing $11M in Series A funding led by Standard Capital, with participation from Pear VC, Kindred Ventures, Eudemian Ventures, ElevenLabs, and 43. Healthcare's administrative crisis is enormous. Over $1 trillion in annual overhead, 50%+ staff turnover at many practices, and 80% of physicians reporting that administrative burden contributes to burnout, which negatively impacts patient care. We built Insight Health's voice-first clinical agents so healthcare organizations can operate at the level their patients deserve, and we are seeing first hand that the healthcare organizations that embrace the power of AI to alleviate these challenges will be the ones that thrive and better serve their patients. Insight Health has completed more than 3 million autonomous clinical conversations with patients, and our customers like The Oregon Clinic, Pacific Sports & Spine, Coastal Health, and Santiam Hospital are collectively saving over $50 million in annual administrative costs. I truly believe that 2026 is the year patient-facing AI will take off, and we are proud to be part of that evolution of healthcare. I’m deeply grateful to our investors like Dalton Caldwell, Kanyi Maqubela, Soso Sazesh, Kathleen Estreich, Andrew Parambath MD MBA, Raoul Felix Maier, Abbas Haider Ali, AJ S., Invariantes Fund, Andrea Funsten, Steve Jang, RTP Global, Bo Berluti, Angie Lal, Calvin French-Owen, Chuck Hwang, Peter Reinhardt, Christian Monberg, Neej Gore, Eleanor Dorfman, Kevin Niparko, Tido Carriero, Diede van Lamoen, James Ho, Christie Kong, Rishi Shah, Tejas Manohar, David Russo, DO, MPH, Trey Rigert, my co-founders Eric Stecker, Saran S., Pankaj Gore, MD, and the amazing team we’ve built, and the healthcare organizations building this future alongside us. Read more about our news today in Fortune (link in comments) #AIinHealthcare #DigitalHealth #Fundraise Insight Health AI, Inc.
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Jacqueline Cheong liked thisJacqueline Cheong liked thisMost important launch of my life… she said yes!!! I secretly wrote my proposal to Xinyi on our first satellite and then showed her as it passed above us at sunrise in Mexico 😍😍🤓🤓🌹🌹🥰🥰😘😘🤗🤗💎💎🎊🎊💘💘💋💋😻😻
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Artie (YC S23)
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English
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Cantonese
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Chinese Mandarin
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Capital Investments at Berkeley (CIB)
Portfolio Management Team; Equity Research Analyst
- Present• Participated in weekly financial market discussions, writing equity research journals and company evaluations, and competitions through simulated investments on online platforms (Investopedia and ForexMaster) to develop investment knowledge and interest
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International Student Association at Berkeley (ISAB)
External Vice President, External Events Chair, External Events Committee Member
- Present• Oversaw marketing and event committees by providing guidance to committee leaders to develop their leadership skills and prepare the future executive team • Managed ISAB’s events and relations to increase campus presence and solidify relationships with other clubs • Collaborated with 5 other cultural and business organizations to host 5 events to spread cultural awareness
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Global Leadership Organization (GLO)
Registration Coordinator, Planning Committee
-• Coordinated registration and organized the biggest leadership conference in U.C. Berkeley with 70+ participants to provide students and young professionals the opportunity to gain tangible skills to become more effective leaders • Engaged in leadership workshops and seminars with the conference counselors and attendees to develop leadership skills, such as holding more efficient meetings and motivating and managing others
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Sigma Eta Pi – U.C. Berkeley’s Premiere Entrepreneurship co-ed Fraternity
Alumni Relations
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