Igor Freitas’ Post

I know I'll be challenged but think really about this: "A semantic layer that lives inside your warehouse is only as universal as that warehouse". Is this your situation? Do you want to evaluate alternatives? Let me know.

Snowflake. Databricks. Metadata often matters more than the data itself. One thing I’ve learned building data platforms: The real leverage doesn’t come from pipelines or storage. It comes from understanding how your data behaves. • lineage that shows where data really flows • freshness signals that surface silent delays • usage patterns that reveal what’s worth optimizing • query insights that expose hidden cost drivers • quality metrics that prevent downstream breakage When metadata becomes a first‑class asset, everything gets easier debugging, scaling, cost control, even stakeholder trust. In every mature platform I’ve worked on, observability wasn’t an add‑on. It was the foundation. #DataEngineering #DataArchitecture #Snowflake #Databricks  #ApacheSpark #ETL #DataPipeline

Snowflake costs don’t explode because of warehouse size. They explode because of workload behavior. In my experience working on large‑scale Snowflake platforms, resizing warehouses almost never fixes cost issues. The real drivers sit inside the workload. Here are the patterns I’ve seen repeatedly: • Heavy transformations re‑running instead of using incremental logic • Poor clustering leading to unnecessary large scans • Stacked views creating deeply nested, expensive queries • Full‑table scans for simple analytical questions • BI dashboards triggering constant background queries Snowflake scales beautifully. But that also means it can burn credits quietly if workloads aren’t designed with discipline. What has consistently worked for me is focusing on: • Understanding query patterns • Strong data modeling • Building incremental pipelines • Monitoring warehouse usage at the query level • Isolating workloads to prevent noisy neighbors Because in almost every environment I’ve worked in, the warehouse isn’t expensive. The workload is. #Snowflake #DataEngineering #CloudComputing #Databricks #DataArchitecture #ETL #DataModeling #BigData

What is Snowflake? ❄️ Why is everyone in IT talking about it? 🤔 Snowflake is a cloud-based data warehouse designed to handle huge amounts of data effortlessly 📊 👉 Since it’s a data warehouse, you can store massive data 👉 It’s easy to scale as your data grows 👉 That’s why companies are rapidly adopting Snowflake 💡 Why Snowflake is Trending: ✔️ Handles huge volumes of data easily ✔️ No infrastructure management required ✔️ Fully cloud-based ☁️ ✔️ High performance & faster query execution ⚡ ✔️ Pay only for what you use 🔥 Compared to traditional databases, Snowflake is faster, simpler, and more scalable 👉 That’s why it’s one of the most in-demand technologies in the IT industry right now! 🚀 Want to learn Snowflake and become a Data Engineer / Data Analyse 👉 Comment “SNOWFLAKE” and get a FREE roadmap to learn Snowflake step-by-step 🚀 #Snowflake #DataWarehouse #CloudComputing #DataEngineering #TechSkills

❄️ Snowflake won’t fix your data architecture. I work with Snowflake regularly, and here’s a common misconception: 👉 Snowflake is powerful. 👉 But it amplifies both good and bad architecture. You can: ✅ scale compute in seconds ✅ run complex queries fast ✅ store massive amounts of data But if your data is: ❌ poorly modeled ❌ inconsistent across sources ❌ loaded without clear logic (ELT chaos) — performance will drop, and costs will rise. Good analysts use Snowflake. Great analysts: 🧠 design clean data models 🧠 think about cost & performance 🧠 build with scalability in mind Because in the end: Snowflake is a tool. Architecture is the advantage. Have you seen Snowflake projects where cost or performance got out of control? 👇 #Snowflake #DataEngineering #DataAnalytics #DataModeling #CloudData

❄️ Snowflake Data Sharing — Share Data Without Moving It After working with Snowflake architecture and compute, this is one feature that genuinely stood out 👇 👉 Data Sharing in Snowflake 💡 Think about this… You need to share data with another team or partner. Normally, you would: • Export data • Create multiple copies • Move files across systems • Deal with version mismatches 👉 More copies = more confusion. 🔹 What Snowflake Does Differently ✔️ Share live data ✔️ No duplication ✔️ No data movement ✔️ No additional pipelines The consumer queries the same data — securely and in real time. 🔹 What’s Actually Happening? • Data stays in the provider account • Access is granted, not copied • Consumer sees it like a normal table • Always up-to-date 👉 One source of truth. Zero redundancy. 🚀 Why This Matters for Data Engineers ✔️ No need to build extra pipelines for sharing data ✔️ Reduced data duplication and storage overhead ✔️ Simplified architecture and maintenance ✔️ Real-time access without synchronization issues Most systems focus on moving data faster. Snowflake removes the need to move it at all — and that’s the real shift. #Snowflake #DataEngineering #CloudComputing #DataSharing #ModernDataStack #BigData #DataArchitecture

I used to think choosing the right data platform was about picking the “best” tool. Over time, I realized it’s not really about tools . it’s about trade-offs and architecture decisions. Platforms like Snowflake, BigQuery, and Databricks are all powerful, but they solve different problems depending on the context. What matters more is: understanding the system you’re building knowing your constraints and making decisions in the right order I wrote a short article on how I think about designing modern data platforms from an architectural perspective, and when tools like Snowflake actually make sense. Would be great to hear how others approach this as well. 👉 Read it here: https://lnkd.in/e2fdPQXc #DataEngineering #DataArchitecture #Snowflake #BigQuery #Databricks #SystemDesign #CloudComputing #SoftwareEngineering #Tech

𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 𝗧𝗮𝘀𝗸𝘀 + 𝗦𝘁𝗿𝗲𝗮𝗺𝘀 – 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗘𝗟𝗧 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 One thing I’ve seen many teams overlook in Snowflake is how powerful Streams and Tasks can be when used together. You can actually build a near-real-time ELT pipeline fully inside Snowflake without relying heavily on external schedulers like Airflow. In a recent use case, we had continuous data landing into Snowflake via Snowpipe from cloud storage. Instead of scheduling batch jobs every 30 minutes, we used Streams to track incremental changes at the table level. A stream essentially captures CDC-like behaviour by recording inserts, updates, and deletes on a table. The beauty is that it’s metadata-driven and doesn’t duplicate data, so it stays lightweight even at scale. On top of that, we created Tasks to process this incremental data. Tasks can be scheduled or triggered based on dependencies, which allows us to build a chain of transformations directly inside Snowflake. The flow looked something like this: New data lands → Stream captures change → Task triggers transformation → Data moves from raw → staging → curated layer Instead of reprocessing entire tables, we were only processing deltas, which significantly reduced compute costs and improved performance. Another advantage is orchestration simplicity. Why this matters Reduces dependency on external orchestration tools Processes only changed data instead of full loads Improves pipeline latency significantly Keeps everything inside Snowflake ecosystem Cleaner, simpler architecture with fewer moving parts This is one of those patterns where Snowflake is not just a warehouse anymore; it starts behaving like a full data processing engine. #Snowflake #DataEngineering #ELT #DataPipeline #StreamingData #DataArchitecture #DataOps #ModernDataStack #Analytics #BigData #CloudData #DataEngineer

Diving deeper into Snowflake ❄️ Recently, I’ve been strengthening my hands-on experience with Snowflake and exploring some of its powerful capabilities that make modern data warehousing highly efficient and scalable. 1. Zero-copy cloning – Enables instant data duplication without additional storage, making testing and development faster. 2.Time Travel – Allows access to historical data, ensuring better data recovery and auditing. 3.Separation of compute & storage – Provides flexibility to scale workloads independently, improving performance and cost efficiency. What stands out is how Snowflake simplifies handling large-scale data with minimal operational overhead, while maintaining high performance and concurrency. As someone working in data, this has helped me better understand how cloud-native architectures are transforming data engineering and analytics. #Snowflake #DataEngineering #DataAnalytics #ETL #CloudComputing #SQL #Learning

⚡ Stop Treating Data Pipelines Like Legacy Systems Data engineering has evolved — but many pipelines still look like they’re stuck in the past. With platforms like Snowflake, the game has changed: No servers to manage. No rigid scaling limits. No unnecessary complexity. Yet the real question is 👇 👉 Are we actually using Snowflake to its full potential? 🔍 What I’ve been rethinking: Moving from ETL → ELT mindset Letting Snowflake handle heavy transformations instead of external tools Designing pipelines that are modular, scalable, and cost-efficient Focusing more on data modeling + performance, not just ingestion 📈 The shift: It’s no longer about how fast you can move data It’s about how well your data is structured for decision-making Snowflake isn’t just a warehouse — it’s becoming the core engine of modern data platforms. And the engineers who understand this shift? They’re the ones building the future. #DataEngineering #Snowflake #ModernDataStack #ELT #CloudData #Analytics #DataArchitecture #SQL