How to combine Snowflake and Databricks for unified data architecture

Analysts and business users rely on Snowflake for fast, governed insights. Data engineers and scientists work in Databricks to build transformations, streaming pipelines, and ML models. This division of personas is why many enterprises run both platforms together. Each environment plays to its strengths — but the challenge is creating a unified architecture where workloads, governance, and costs remain in balance. Architectural patterns, such as Medallion Architecture, help bridge the gap by aligning raw, curated, and business-ready layers across Snowflake and Databricks into a consistent lifecycle. In practice, this means: - Raw (Bronze): Data is ingested into Databricks, where engineers and scientists handle transformations and model training. - Curated (Silver): Cleaned and standardized data flows through pipelines, creating a consistent layer that bridges Databricks and Snowflake. - Business-Ready (Gold): Governed datasets are published in Snowflake for analysts and business users to query with confidence Join us today at 1 PM ET to learn how our client Janus Henderson Investors brought this architecture to life — combining both platforms into a scalable, governed, and cost-efficient hybrid environment. Register here: https://lnkd.in/gvYCUY44 You’ll hear from Wayne Eckerson, Michael Spiessbach, and Mark Goodwin. #UnifiedArchitecture

Snowflake and Databricks, Unified. Enterprises often run both platforms to cover the full spectrum of data workloads: ▪️Snowflake for governed analytics, AI, and BI reporting. ▪️Databricks for data engineering, streaming, and ML at scale. ▪️But making them work seamlessly together is where the real challenge — and opportunity — lies. At 1 PM ET today, join Datalere and Janus Henderson Investors for a live webinar on building a unified architecture that connects Snowflake and Databricks into an end-to-end data lifecycle. 👉 Register here: https://lnkd.in/eK-r2y4q #unifieddataarchitecture #Snowflake #Databricks

When we think about our data strategies, the choice between platforms like Databricks and Snowflake is critical. Choosing the right tool shouldn't be a daunting task. Both have their strengths and can be tailored to meet specific needs. For instance, consider how Snowflake enhances scalability and data sharing. Imagine a team collaborating seamlessly across various data sources—every insight shared leads to better decision-making and innovation. The key takeaway from the article is recognizing that the best platform isn't just about features; it’s about understanding your unique requirements and fostering a culture of data-driven decision-making within your organization. Let's create a community where we learn from one another! If you've experienced a transition between data platforms or have insights to share, we would love to hear your thoughts. #DataScience #Snowflake #Databricks #CloudComputing #DataCollaboration https://lnkd.in/gdtjpsjC

Databricks vs Snowflake: Different Tools, Different Purposes I often see debates about which platform is “better.” Truth is — there’s no direct comparison. Both dominate in different segments: Snowflake’s sweet spot → Data warehouse migrations, BI workloads, regulatory reporting. Easy to use, reliable, and integrates smoothly with tools like Power BI. Snowflake’s ML journey → With Snowpark & Cortex AI, it’s entering the ML space. But for complex ML workflows & large-scale deployments, it’s not as mature as Databricks (yet). Where Databricks shines → Petabyte-scale data processing, hybrid Lambda architectures, and unmatched control over pipelines. The real challenge → Talent. Snowflake can be run by SQL-savvy analysts, while Databricks demands deeper expertise in distributed computing. My take: Both platforms have matured. Today, the choice depends on your use case and — more importantly — your team’s expertise. If I were deciding: Databricks for data processing Snowflake as the data warehouse That balance reduces tool overhead while building a unified data ecosystem. What’s your experience with Snowflake and Databricks in the same data solutions architecture? #𝖣𝖺𝗍𝖺𝖤𝗇𝗀𝗂𝗇𝖾𝖾𝗋𝗂𝗇𝗀 #𝖲𝗇𝗈𝗐𝖿𝗅𝖺𝗄𝖾 #𝖣𝖺𝗍𝖺𝖻𝗋𝗂𝖼𝗄𝗌 #𝖣𝖺𝗍𝖺𝖶𝖺𝗋𝖾𝗁𝗈𝗎𝗌𝖾 #𝖬𝖺𝖼𝗁𝗂𝗇𝖾𝖫𝖾𝖺𝗋𝗇𝗂𝗇𝗀 #𝖢𝗅𝗈𝗎𝖽𝖢𝗈𝗆𝗉𝗎𝗍𝗂𝗇𝗀 #AI / #ML

Building data projects shouldn’t feel like something you’ll want to rebuild later. 🏗️ With WhereScape, you can automate the design, rebuild, and migration of data warehouses, lakes, marts, and vaults—eliminating hand-coding, rework, and risk. Accelerate iteration across Snowflake, Databricks, Microsoft Fabric, and Redshift while maintaining governance, lineage, and audit-ready documentation. The result: AI-ready, future-proof data pipelines built on 25+ years of proven automation. Your blueprint for faster, governed, scalable data starts here 👉 https://ow.ly/YZ7o50X6OTq #DataAutomation #DataEngineering #MicrosoftFabric #Databricks #Snowflake #DataWarehouse

Snowflake is no longer just a data warehouse — it’s becoming the operating system for modern data engineering. One area I see many teams struggling with is balancing performance, governance, and cost at scale. A few hard-earned lessons from real-world implementations: 🔶 RBAC done right matters more than you think Don’t just replicate org hierarchies — design role layers (functional, environment-based, object-level) that make onboarding & audits effortless. 🔶 Pipelines ≠ observability Building ingestion pipelines is easy. Knowing why a flow failed, where latency builds up, and how freshness impacts KPIs is where the real challenge lies. Observability frameworks (telemetry models + golden KPIs + anomaly detection) are the future. 🔶 Compute cost ≠ governance Optimizing warehouses is step one. True cost governance means forecasting future consumption, tracking per-team usage, and simulating workload changes before they hit your bill. 🔶 Lineage is no longer optional As Snowflake integrates with BI, ML, and real-time apps, being able to trace data across flows isn’t “nice to have” — it’s the backbone of trust. Snowflake is evolving fast with features like Cortex AI, Streamlit, and marketplace listings — but without a technical architecture mindset, it’s easy to scale complexity instead of value. 🔮 My take: the next frontier is AI-assisted observability & governance accelerators that will let Snowflake teams predict and prevent issues before they impact business. What’s one Snowflake challenge you’re solving right now — latency, lineage, or cost? 👍 #Snowflake #DataEngineering #CloudArchitecture #Observability #CostGovernance #Lineage

🚀 Types of Data Pipelines in Snowflake In today’s data-driven world, building efficient and scalable data pipelines is key to unlocking insights. Snowflake provides flexibility to design pipelines that suit diverse business needs. Here are the most common types: 🔹 Batch Pipelines: Move large volumes of data at scheduled intervals. Ideal for reporting and analytics. 🔹 Streaming Pipelines: Capture and process data in real time for instant decision-making. 🔹 ELT Pipelines: Extract → Load → Transform directly inside Snowflake, leveraging its compute power. 🔹 ETL Pipelines: Extract → Transform → Load for pre-processed data before storage. 🔹 Event-Driven Pipelines: Triggered by specific events (e.g., file arrival in cloud storage). 🔹 Machine Learning Pipelines: Feed data directly into ML workflows for predictive insights. 💡 Choosing the right pipeline depends on your data volume, latency needs, and business goals. 👉 What type of pipeline do you use most often in Snowflake? 🌐 : https://lnkd.in/gnZxseBr #Snowflake #DataEngineering #DataPipelines #CloudData #Analytics #DataIntegration #StreamingData #BatchProcessing #MachineLearning

✅ Snowflake World Tour Virtual 2025 – Completed! ❄️ It was an insightful day packed with cutting-edge sessions on how Snowflake is redefining data workflows, AI integration, and cloud modernization. Key takeaways from today’s sessions 👇 📊 Streamlining Data Workflows with Snowflake Openflow – simplifying complexity 🤖 Building Agentic AI Applications on Snowflake – intelligent data conversations in action 🧊 Unifying Data Lakes with Apache Polaris & Iceberg Integration – seamless interoperability 🧠 Bridging AI and Data through Model Context Protocol Integration 🔐 Advanced Security and Governance in the Snowflake Data Cloud 💡 Modernizing Legacy Data Warehouses for efficiency and scale 💰 Maximizing ROI through Cost Optimization A great learning experience on how Snowflake empowers organizations to make smarter, faster, and more secure data-driven decisions. Excited to apply these insights in real-world AI and analytics solutions! 🚀 #Snowflake #DataCloud #AI #MLOps #MachineLearning #DataEngineering #DataScience #Analytics #CloudComputing #Event

LinkedIn Post: "Data Vault: The Architecture Built for Change" 🧱 Why Data Vault Is (Still) a Powerful Modeling Approach In fast-changing data environments, traditional modeling techniques — like 3NF or even star schemas — often fall short. That’s where Data Vault shines. It was designed to solve a real problem: 🔄 How do you model data that is constantly changing — without breaking everything? 💡 Core Concepts of Data Vault: Hubs → represent unique business keys Links → define relationships between hubs Satellites → store context (descriptive data + history) ✅ Key Advantages: Scalability for large, evolving datasets Historical tracking built-in Auditability and lineage support Fits well with modern ELT & cloud data lakes 🧰 Tools like dbt, Snowflake, and Databricks are making Data Vault more approachable and scalable than ever. 🔍 But it's not perfect: It requires discipline in modeling Querying across multiple joins can get complex Not always the best fit for real-time use cases 🚀 When does it shine? Enterprises needing historical fidelity and regulatory compliance Complex data landscapes with many disparate sources 👇 What’s your experience with Data Vault? Have you used it in production — and if so, what were your lessons learned? #DataEngineering #DataVault #DataModeling #ETL #ModernDataStack #CloudDataWarehouse #Snowflake #Databricks #dbt #ELT #DataArchitecture

Exploring Databricks: A Step Towards Modern Data Solutions! 🚀 I’ve recently taken the first step in my journey with Databricks and learned some incredible insights into how its platform is transforming the world of data. Here’s what I discovered: 🚀 The Databricks Story: Understanding how Databricks is solving complex data challenges with its powerful unified platform. 🏞️ Data Lakehouses & Data Intelligence: Exploring the concept of data lakehouses and how they bring together the best of data lakes and data warehouses to enable intelligent analytics. 🏛️ Databricks Architecture: A deep dive into the platform’s architecture and how it powers scalable, distributed analytics. 🔐 Security & Governance: Learning how Databricks ensures robust security and governance practices to protect and manage data efficiently. 🛠️ Databricks Products & Features: Getting familiar with the products and features offered by Databricks for data engineering, analytics, and machine learning. Excited to continue learning and explore more about how Databricks is shaping the future of data-driven solutions! Harshavardhan B #Databricks #DataLakehouse #BigData #MachineLearning #DataScience #Analytics #ContinuousLearning #DataEngineering