Cesar Zulaica

Beyond excited to announce our partnership with Databricks, a leader in enterprise data intelligence, to bring Anthropic’s world-class models directly to over 10,000+ enterprises. In meeting with customers, we’ve heard a consistent need for enterprise data, AI tooling, and frontier models together in one platform. We’re excited to help Databricks deliver this, and to do so with a partner who also values security and safety as much as we do. Starting today, Sonnet 3.7, our first hybrid reasoning model, is directly available in Databricks. We’re excited to partner with Databricks in our continued efforts to bring safe and secure frontier models to customers.

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"You're leading the SVP review." Six words that hit me like a thunderbolt - just 8 weeks after joining Amazon. I wasn't ready. I couldn't be ready. I had barely memorized our team's organizational structure. My brain screamed silent objections: "I haven't even written my first document yet." "I still don't understand half our acronyms." "There must be someone - anyone - more qualified." Every morning until then, I had waited for the tap on my shoulder. The "we've made a terrible mistake" conversation. The moment when everyone would realize what I already knew: I didn't belong here. But instead of that tap, my manager handed me the keys to a career-defining moment. What she saw as a straightforward assignment felt to me like being thrown into the deep end right after learning to float. Then something shifted. In the sleepless nights preparing, in the practice runs, in the feedback sessions - I realized she wasn't taking a risk on me. She was simply seeing what I couldn't yet see in myself. The review wasn't just successful—we secured alignment and headcount for our team. But something far more valuable happened in that conference room: I stopped waiting to be discovered as a fraud. The most powerful barriers to your success aren't external - they're the invisible ceilings you have built in your own mind. Sometimes we need someone else to hand us a sledgehammer. My manager didn't just assign me a task. She handed me a mirror that finally reflected my true capabilities, not my fears. Leaders: When was the last time someone's belief in you demolished a wall you thought was permanent?

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New research shows that mentions of burnout in Glassdoor reviews are at an all-time high, in data stretching back almost a decade: Three interesting insights from the research: ⚓ Many reviews that mention burnout talk about the cumulative effect of several years of layoffs and a soft job market weighing on employees. For example, a layoff survivor being asked to do the job of multiple people without any end in sight. 💫 Burnout is a particular risk for high performers. Reviewers who mention burnout are 59% more likely to apply to a new job on Glassdoor vs. those that don't. This increase is particularly acute for employees who are otherwise happy (i.e., rate their employers 4 or 5 stars). Employees who are already unhappy are likely to look for a new job regardless of whether they feel burned out or not, but burnout can force high performers who are otherwise satisfied to look for the door. 🔥 Burnout is a slow burn crisis for employers. As burnout builds, employees become disengaged, less productive and more likely to leave. In a softer job market, burned out employees may feel compelled to stay but if the job market opens up again, that could give an opening for burned out employees to hit the road. #burnout #retention #turnover

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As we have been working with customers who are adopting Snowflake Semantic Views, one of the top requests has been a native integration with dbt Labs for materialization and referencing of Semantic Views within dbt models. I’m excited to announce that there is now a new dbt package, available on Package Hub, that does just this! You can read Yutsing Liu's blog here to learn more: https://lnkd.in/deXJtJBQ In summary, the dbt_semantic_view package introduces a new semantic_view materialization for dbt Core projects targeting Snowflake, enabling you to define business-centric semantic views (tables + relationships + dimensions + metrics) via dbt and automate their creation, renaming, and dropping. Link to package is in the comments.

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Just read a fun new post from Benjamin Rogojan (Seattle Data Guy) on why data teams shouldn’t blindly sprint toward Apache Iceberg. As always, Ben’s writing is provocative in the best way - challenging us to pause and ask why we’re reaching for the latest tech before we commit. I'm going to counter Ben on this one: Firsthand, I’d argue that there is a clear place for it. One where it not only accelerates time-to-insight, but also helps reduce the need to pile more junk onto the dump. In the analytics world, we’re constantly under pressure to answer questions fast. Stakeholders needed insights yesterday, so we chase whatever gets us to a solution quickest. Most of the time, those swift moves leave behind a heap of tech debt to clean up next year. But is Iceberg just another patch for deeper infrastructure issues …or does it actually have a practical home in your stack? Take 6sense: in 2022 calendar year alone, we went from two distinct warehouse environments to 5+. Why? M&A of 4 product companies and numerous new feature launches with evolving architecture needs.... But more environments don’t change expectations. Everyone still wants centralized analytics. A real question just two weeks after acquisition of company #3: “Can we just see how usage of feature X in warehouse A + adoption of capability Y in environment B impacts customer health stored in warehouse C?” Sounds easy, right? Just write a cross-environment query and wrap it up before lunch? In reality, you’re scrambling to stitch everything together, suddenly managing dozens (or hundreds) of EL jobs just to centralize data. At our peak, we had over 100 of these running nightly - each one draining compute, delaying other analytics pipelines, and straining SLA expectations. Would Iceberg have solved all of that? No. Integrating acquired systems and legacy stacks is never simple. But had we adopted Iceberg earlier - with strong governance and a cross-functional design approach that prioritized analytics from the start - we could’ve avoided a lot of duplication and orchestration overhead. Iceberg is NOT a silver bullet. But in the right context, it’s a powerful architectural lever for data democratization and it can absolutely drive both efficiency and cost savings. Curious to hear from others: Has your team adopted Iceberg? What made it worth it…or not? Highly recommend giving Ben a follow if you work in data. The post in reference in comments!

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We’ve partnered with AARP to publish a new report highlighting the advantages of multigenerational teams and the unique skills workers 50+ bring to the workforce. Here’s a few of the insights that I found most interesting: Workers 50+ are closing the tech skills gap: LinkedIn data shows a 25% increase in disruptive tech skills among older professionals over the past five years. Retention rates tell a powerful story: 85% of workers aged 50+ hired last June were still with their employer a year later, compared to just 71% of younger hires. That matters because losing new employees costs companies more—not just in hiring expenses, but also in lost productivity while replacements get up to speed. Networks open doors to more knowledge and opportunity: Workers 50+ have networks that are 20% larger and more senior, helping companies tap into broader expertise and opportunity. Overall, when generations collaborate, companies see higher engagement, lower turnover, and greater innovation. Investing in age-diverse talent isn’t just a sound talent strategy, it’s a good business strategy. See the full report for more findings.

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Data Commons is constantly evolving to make the world’s most critical public statistics more accessible. Today, we're accelerating the integration of global statistical data (GDP, population, etc.) with new open-source tools supporting SDMX 2.1 (Statistical Data and Metadata eXchange). https://lnkd.in/ekCUJh52 SDMX is a global standard for exchanging statistical data and metadata such as unemployment rates, GDP, and population figures, widely used by national statistical offices, central banks, and international organizations like the OECD, the World Bank, and the United Nations. -Key benefits now: Automatic download and transformation of SDMX sources for easy usage in Data Commons. -What's next? Our roadmap is focused on auto-schematization to fully automate the rest of the ingestion process with a goal of less manual work and more high-quality data. More info (with links to GitHub) in our post from Rohit Kumar and Sandeep Tuniki: https://lnkd.in/ekCUJh52 #DataCommons #SDMX #Roadmap #DataScience #DataEngineering

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Last week I spoke at Snowflake's World Tour event. The data ecosystem + career paths have changed quite a bit in the last four months since I presented at Snowflake Summit. A few changes I've seen on our data team since then: Today’s AI tools shorten "time-to-impact". We've seen new hires use Claude Code, Cursor, Notion AI, etc. to onboard faster to codebases and business domains. If you can search, reason, and ship, you can contribute far faster than before. You cannot measure agentic product experiences without full-stack data talent. Ramp's proportion of LLM products, questions, and decisions will only go up. AI-as-a-feature requires eval datasets, offline/online metrics, and drift monitoring. This is product analytics with semantic rigor vs. siloed DS research or SQL-only data modeling. Increasingly, the skill we're asking our team to develop / hiring for is owning the full product loop (metrics → modeling → experiment → decision → monitor) vs. deep specialization in one tool, language and ecosystem (whether "applied science" or "analytics engineering"). Big thanks to Nicole and Bobby for having me!

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We have released TimesFM-2.5, the new leader in the GIFT-Eval on all accuracy metrics among zero-shot foundation models. This improved foundation model is now available on Hugging Face, with an upcoming release on GCP's BigQuery and Model Garden. This release represents a significant advancement. TimesFM-2.5 outperforms TimesFM 2.0 on leading benchmarks by up to 25%, while using half the number of parameters (200M). It also features a longer maximum context length of 16K. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗧𝗶𝗺𝗲-𝗦𝗲𝗿𝗶𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴? Time-series analysis is the process of studying data points collected over time to make predictions and identify trends. It is a critical tool for a wide range of applications, like: ➡️ Forecasting future product demand. ➡️ Tracking weather and precipitation ➡️ Optimizing supply chains and energy grids. 𝗪𝗵𝘆 𝗶𝘀 𝗶𝘁 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗶𝗻𝗴? Time-series forecasting is difficult because data patterns are often complex, can change over time, and are influenced by numerous factors. Developing a single model that can perform well across diverse datasets without being explicitly trained on each one has been a major challenge. 𝗧𝗶𝗺𝗲𝘀𝗙𝗠-𝟮.𝟱: 𝗔 𝗡𝗲𝘄 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗳𝗼𝗿 𝗭𝗲𝗿𝗼-𝗦𝗵𝗼𝘁 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 TimesFM-2.5 sets a new standard for a decoder-only foundation model trained on a large time-series corpus. 🤗 Leading Performance: TimesFM-2.5 holds the top position on the GIFT-Eval leaderboard for point forecasting accuracy (MASE) and probabilistic forecasting accuracy (CRPS). 🤗 Efficiency: The model's efficiency is a key feature, with a small parameter count that makes it practical for a wide range of production environments. 🤗 Longer Context: The increased context length allows the model to process more historical data, leading to more accurate forecasts. This work reiterates that building a single foundation model for time series forecasting is possible. Google Research keeps pushing forward the frontier of time series forecasting research. We are grateful to our community and customers who have provided feedback and deployed TimesFM in production. We are interested to hear more about how you are using TimesFM. Read more in our repository and see the leaderboard. GiFT-Eval: https://lnkd.in/dAwAcKA7 GitHub: https://lnkd.in/dWXH7BAm  Hugging Face: https://lnkd.in/dtx9iMHE To learn more about the foundational model read the Paper: https://lnkd.in/dRw3zzXT

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Higher pay remains the top reason people look for a new job — but it’s far from the whole story. Indeed’s 2025 Workforce Insights Survey shows that while compensation still dominates, benefits, work-life balance and culture play a growing role in job search decisions, reflecting shifting expectations shaped by inflation, burnout and a reassessment of what work should provide. Priorities vary across countries. In the US, benefits matter disproportionately because employer-sponsored health insurance underpins financial security. In Canada, the UK, Ireland and Australia, workers tend to value benefits linked to retirement, paid leave, family support and overall job stability. In Japan, benefits often signal long-term security, including bonuses and transport allowances. France and Germany stand apart: in both, a better work-life balance is the second-most cited reason for looking for a new role — underscoring the growing importance of balance alongside pay. Read more via link in comments

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