Kirti Kangne’s Post

Modern data teams are no longer just storing data they are building platforms that support analytics, reporting, and AI at scale. That’s where Snowflake stands out as a powerful cloud data platform. Snowflake simplifies how organizations manage and analyze large datasets by separating: - compute - storage - and data services This makes it easier to scale workloads without impacting performance. What makes Snowflake especially valuable in real-world data engineering environments: - Handles structured and semi-structured data efficiently - Supports high-performance analytics at scale - Enables secure data sharing across teams and organizations - Integrates easily with Spark, BI tools, and cloud ecosystems - Reduces infrastructure management overhead Instead of spending time tuning infrastructure, teams can focus more on delivering insights faster. Today, Snowflake is not just a data warehouse it’s becoming a central analytics layer for modern data platforms. #Snowflake #CloudDataWarehouse #DataEngineering #BigData #AnalyticsEngineering #ModernDataStack #CloudComputing #DataPlatforms #DataArchitecture #BusinessIntelligence #DataAnalytics #TechCareers

Most companies are quietly burning their budget on data… and no one is accountable for it. Not marketing. Not product. Not even engineering. It just shows up as a growing cloud bill. I keep seeing this with Snowflake projects. Teams invest in a modern data platform expecting clarity, speed, and lower costs. But instead: - pipelines keep growing - data gets duplicated - compute runs when no one needs it So the business pays more. And gets the same (or worse) outcomes. That is the real problem. Snowflake is powerful, but it will not fix waste by itself. If anything, it can scale that waste faster. We saw this in a migration from Databricks. The turning point was not the move to Snowflake. It was when we: Cut unnecessary workloads, Restructured how compute is used, and aligned the platform with actual business needs. That is how you get: - up to 70% cost reduction - simpler architecture - faster analytics and ML adoption Not from just migrating. At inVerita, as a Snowflake Select Partner, this is where most of the real value comes from. Fixing the hidden waste. So here is a question most teams avoid: If you had to justify every dollar spent on your data platform… could you? Or is your data stack just quietly burning budget in the background? #dataengineering #snowflake #cloud #analytics #ai

I used to think all data warehouses were the same… until I worked with Snowflake. At first, it felt like just another tool for storing and querying data. But once I started using it in a real project, I noticed something different. Things were faster. Scaling felt easier. And I didn’t have to worry much about infrastructure. That’s when I understood: Snowflake is not just a database — it changes how you think about data. Here’s what stood out to me: Separation of compute and storage makes scaling flexible Performance tuning feels simpler compared to traditional systems Handling large-scale data becomes more efficient Less time managing infrastructure, more time focusing on data That experience shifted my mindset. Because in modern data engineering, 👉 simplicity and scalability matter more than complexity. Now I don’t just ask, “Can it store data?” I ask, “Can it scale without friction?” Have you worked with Snowflake or any cloud data warehouse? What was your experience? #Snowflake #DataEngineering #CloudData #DataWarehouse #BigData #Analytics #DataPipeline #ETL #ModernDataStack #TechLearning #DataEngineer #CloudComputing #CareerGrowth

Snowflake has become one of the most important platforms in modern data engineering because it makes storing, processing, and sharing data much easier at scale. How to use Snowflake in a practical way: Start by loading data from sources like APIs, databases, cloud storage, or streaming pipelines into Snowflake. From there, use SQL, tasks, streams, and procedures to clean, transform, and organize the data for reporting, analytics, machine learning, and downstream applications. Teams can create secure data models, optimize performance with proper warehouse sizing, and share data across departments without moving it manually. Why Snowflake is important: It separates compute and storage, which means teams can scale performance without disturbing other workloads. It supports structured and semi-structured data, enables fast analytics, simplifies data sharing, and offers strong security features like RBAC, masking, and encryption. For businesses, that means faster insights, better governance, and more reliable data platforms. In today’s data-driven world, Snowflake is not just a warehouse tool. It is a key platform for building scalable, secure, and analytics-ready data ecosystems. #Snowflake #DataEngineering #CloudData #BigData #SQL #ETL #ELT #DataAnalytics #DataPlatform #ModernDataStack#C2C

Exploring the Power of Snowflake – The Modern Data Cloud In today’s data-driven world, organizations need platforms that can scale, perform, and simplify data management. One platform that stands out is Snowflake. 🔹 What makes Snowflake powerful? ✅ Cloud-Native Architecture – Built for the cloud and runs seamlessly on Amazon Web Services, Microsoft Azure, and Google Cloud. ✅ Separation of Storage & Compute – Enables independent scaling for better performance and cost optimization. ✅ High Performance – Handles massive datasets with fast query processing. ✅ Secure Data Sharing – Share live data across teams, partners, and organizations without moving or copying it. ✅ Supports Multiple Workloads – Data warehousing, data lakes, data engineering, data science, and application development. 💡 Why organizations adopt Snowflake? • Simplified data architecture • Near-zero maintenance • High scalability • Built-in security and governance For professionals working in Cloud, DevOps, and Data Engineering, learning Snowflake opens opportunities to work with modern data platforms and analytics ecosystems. 📊 Data is the new fuel, and platforms like Snowflake help organizations unlock its full potential. #Snowflake #DataEngineering #CloudComputing #DataPlatform #BigData #Analytics #DataCloud #Azure #AWS #GoogleCloud

𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 𝗝𝘂𝘀𝘁 𝗠𝗮𝗱𝗲 𝗔 𝗦𝗲𝗿𝗶𝗼𝘂𝘀 𝗠𝗼𝘃𝗲 𝗳𝗼𝗿 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗧𝗲𝗮𝗺𝘀. Snowflake Gen2 warehouses powered by Google Cloud Axion processors are delivering up to 50% better performance compared to previous generations. That's not a minor infrastructure update — that's a meaningful shift in what enterprise data teams can expect from their analytics and AI workloads. For organizations running complex queries, large-scale transformations, or increasingly demanding AI pipelines, this combination of improved memory bandwidth and processing efficiency translates directly into faster insights and lower compute costs. What stands out here is the practical impact. Workloads that used to strain your warehouse capacity can now run leaner and faster without rearchitecting your entire data platform. That's the kind of improvement that CFOs and data engineers can both appreciate. If you're evaluating your current Snowflake warehouse configuration or planning a migration, this is worth a close look before you finalize your architecture decisions. Read the full article here: https://lnkd.in/gtNDiwh3 #Snowflake #GoogleCloud #DataAnalytics #CloudPerformance #EnterpriseData

Snowflake has become one of the most important platforms in modern data engineering because it makes storing, processing, and sharing data much easier at scale. How to use Snowflake in a practical way: Start by loading data from sources like APIs, databases, cloud storage, or streaming pipelines into Snowflake. From there, use SQL, tasks, streams, and procedures to clean, transform, and organize the data for reporting, analytics, machine learning, and downstream applications. Teams can create secure data models, optimize performance with proper warehouse sizing, and share data across departments without moving it manually. Why Snowflake is important: It separates compute and storage, which means teams can scale performance without disturbing other workloads. It supports structured and semi-structured data, enables fast analytics, simplifies data sharing, and offers strong security features like RBAC, masking, and encryption. For businesses, that means faster insights, better governance, and more reliable data platforms. In today’s data-driven world, Snowflake is not just a warehouse tool. It is a key platform for building scalable, secure, and analytics-ready data ecosystems. #Snowflake #DataEngineering #CloudData #BigData #SQL #ETL #ELT #DataAnalytics #DataPlatform #ModernDataStack #C2C

𝗔𝘁 𝘄𝗵𝗮𝘁 𝗽𝗼𝗶𝗻𝘁 𝗱𝗼𝗲𝘀 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝘀𝘁𝗼𝗽 𝘀𝗰𝗮𝗹𝗶𝗻𝗴… 𝗮𝗻𝗱 𝘀𝘁𝗮𝗿𝘁 𝘀𝗹𝗼𝘄𝗶𝗻𝗴 𝘆𝗼𝘂 𝗱𝗼𝘄𝗻? Data platforms are under more pressure than ever. → More data sources → More expectations from the business → More demand for real-time insights Yet many organizations still struggle with the same challenge: their data platform becomes increasingly complex as it grows. → Pipelines multiply → Architectures become fragmented → Teams spend more time maintaining infrastructure than creating value Modern data platforms require a different approach. 𝙄𝙣 𝙩𝙝𝙞𝙨 𝙘𝙖𝙧𝙧𝙤𝙪𝙨𝙚𝙡 𝙬𝙚 𝙝𝙞𝙜𝙝𝙡𝙞𝙜𝙝𝙩 𝙝𝙤𝙬 𝘿𝙖𝙩𝙖𝙗𝙧𝙞𝙘𝙠𝙨 𝙤𝙣 𝘼𝙒𝙎, 𝙘𝙤𝙢𝙗𝙞𝙣𝙚𝙙 𝙬𝙞𝙩𝙝 𝙩𝙝𝙚 𝙄𝙣𝙩𝙚𝙡𝙡𝙪𝙨 𝙈𝙚𝙩𝙖𝙙𝙖𝙩𝙖-𝘿𝙧𝙞𝙫𝙚𝙣 𝙁𝙧𝙖𝙢𝙚𝙬𝙤𝙧𝙠, 𝙝𝙚𝙡𝙥𝙨 𝙤𝙧𝙜𝙖𝙣𝙞𝙯𝙖𝙩𝙞𝙤𝙣𝙨 𝙗𝙪𝙞𝙡𝙙 𝙨𝙘𝙖𝙡𝙖𝙗𝙡𝙚 𝙖𝙣𝙙 𝙛𝙪𝙩𝙪𝙧𝙚-𝙥𝙧𝙤𝙤𝙛 𝙙𝙖𝙩𝙖 𝙥𝙡𝙖𝙩𝙛𝙤𝙧𝙢𝙨. Swipe through the key ideas or dive even deeper into the architecture and approach by 𝗿𝗲𝗮𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝘄𝗵𝗶𝘁𝗲𝗽𝗮𝗽𝗲𝗿 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲 on our website: https://lnkd.in/eABKn4-H #Aivix #IntellusGroup #Databricks #AWS Databricks Amazon Web Services (AWS)

💸 Deciding between Databricks, Fabric, and Snowflake? Stop comparing features, compare what you'll pay. We just dropped the final part of our Big Three data platform series, and this is where things get concrete: pricing models, cost incentives, and what each platform actually means for your budget. Here's the TL;DR: 🔵 Databricks → Can be the lowest cost option — but only if you have platform engineers who actively tune clusters, spot instances, and cloud infrastructure. The savings are real, but they come with a price tag of their own. 🟡 Microsoft Fabric → Fixed capacity means a predictable bill. A solid fit for decentralized teams and budget control. But when demand goes beyond your limit, performance slows down — your costs don't. ⚪ Snowflake → Pay only for what you run, scale on demand, and keep your team focused on data instead of servers. The catch? Your bill goes up when usage does. The platform you choose doesn't just impact your architecture, it determines who you hire, how your teams work together, and what trade-offs you're ready to live with. The biggest mistake isn't picking the "wrong" platform. It's using the right platform the wrong way. ✍️ Written by Kasper Uleman, Data Engineer at Xomnia. Read the full breakdown on our website > link in the comments! #DataEngineering #Databricks #Snowflake #MicrosoftFabric #DataPlatform #AnalyticsEngineering #CloudData #DataArchitecture #Xomnia

❄️ Snowflake vs. 🏠 ClickHouse: The Ultimate Comparison (2026) Choosing between these two isn't about which is "better"—Think of it as choosing between a Universal Remote and a Dedicated Controller. 1. Performance: Throughput vs. Latency ClickHouse: Built for low-latency, real-time analytics. It excels at scanning billions of rows in milliseconds for sub-second dashboard refreshes. It uses vectorized query execution to squeeze every bit of power from the hardware. Snowflake: Built for large-scale batch processing and complex joins. While very fast, it has a "cold start" overhead (spinning up warehouses), making it less ideal for sub-second interactive apps but great for heavy BI. 2. Maintenance & Operations Snowflake: Zero-Ops. You don't manage indexes, vacuuming, or partitioning. It’s fully managed; you just load data and write SQL. ClickHouse: Requires active "gardening." You need to define Primary Keys, Sorting Keys, and Partitioning schemes. While ClickHouse Cloud simplifies this, the self-hosted version requires deep DevOps expertise. 3. Cost Efficiency Snowflake: Uses a credit-based model. You pay for compute by the second (with a 1-minute minimum). Great for variable workloads (start/stop), but can get expensive for "always-on" dashboards. ClickHouse: Generally 3–5x cheaper for high-volume, consistent workloads. Its compression is legendary (often 10:1 or better), drastically reducing storage costs compared to Snowflake. 4. Reads vs. Writes Writes: ClickHouse is a beast for high-frequency streaming ingestion (millions of rows/sec). Snowflake prefers batch uploads via Snowpipe or COPY commands. Reads: ClickHouse is better for simple, wide-table aggregations. Snowflake is superior for complex, multi-table joins and diverse analytical queries across different departments. 5. Security & Governance Snowflake: Tier-1 enterprise security. Role-Based Access Control (RBAC), data masking, and end-to-end encryption are baked in. ClickHouse: Basic RBAC and encryption are available, but enterprise-grade governance (like Snowflake's "Data Clean Rooms") often requires 3rd-party tools or the Cloud version. #snowflake #clickhouse #learning #comparision #performance #MultiCloud #aws #azure #gcp