Shivanshu Gupta’s Post

🚀 Simplifying data workflows just got easier! Check out our latest article: “How to Read Unity Catalog Tables in Snowflake in 4 Easy Steps.” This guide will help you seamlessly access and manage your data across platforms. #Databricks #UnityCatalog #Snowflake #DataIntegration #DataEngineering

🚀 Unlocking the Path to Seamless Data Integration with Snowflake + Azure Data Factory (ADF) 🌐 Are you curious about how to leverage Snowflake and Azure Data Factory for your data engineering journey? We've got you covered! In our latest YouTube video, we unveil the ultimate roadmap to mastering these powerful tools. Whether you're just starting out or looking to refine your skills, this roadmap will guide you through: ✅ Key concepts to learn ✅ Tools and technologies to focus on ✅ Best practices for seamless integration ✅ Real-world scenarios and tips 📽️ Watch the full video here: Don't miss this chance to supercharge your data engineering skills and stand out in today's competitive market! 💬 Tell us in the comments: What's your biggest challenge with Snowflake or ADF? We'd love to address it in our next video! https://lnkd.in/geBkY7QS https://lnkd.in/g4mHR2wg #Snowflake #AzureDataFactory #DataEngineering #Roadmap #TechSkills

As data volumes grow exponentially, the need for scalable, efficient, and flexible ETL pipelines has never been more crucial. In my recent work, Snowflake has become a game-changer—enabling us to handle complex data transformations at scale with ease and agility. With its ability to decouple compute from storage, manage semi-structured data, and support seamless integrations, Snowflake is empowering data engineers to create pipelines that are both robust and future-proof. The future of ETL isn't just about moving data, but about optimizing performance, reducing costs, and driving insights faster. 🚀 What trends in data engineering are you keeping an eye on? Let’s connect and discuss! #DataEngineering #ETL #Snowflake #CloudData #BigData #Innovation

Databricks Day 11: #dataengineeringin30days A Lakehouse combines the scalability and flexibility of data lakes with the ACID transactions and BI tools support of data warehouses. It leverages the best of both worlds to offer a unified platform for all types of data workloads - from machine learning to business intelligence and real-time analytics. Key Benefits of Lakehouse Architecture: Unified Data Management: Simplify your data architecture by eliminating the need for separate systems for analytical and operational workloads. Cost-Effective Scalability: Store massive amounts of structured and unstructured data economically, thanks to the data lake foundation. Real-time Insights: Support real-time analytics and reporting directly on your operational data without compromising performance or security. Enhanced Data Governance: Implement fine-grained security and governance across all your data, ensuring compliance and data quality. Find the attached ebook with everything you need to know about Lakehouse in Databricks. #gritsetgrow #databricks #dataengineering #sql

#Snowflake is one of the most commonly used data warehouses. Activities requiring orchestration are usually part of a larger data pipeline that involves ingestion, transformation, and reverse #ETL activities. In this blog we are zooming into one part of the data pipeline: orchestrating Snowflake with Control-M orchestrating the following activities.

Data warehousing on the lake - a one-stop serverless unified data platform. Excited to share my latest venture into Databricks Lakehouse Fundamentals! 🚀 I've been diving into this platform that blends data lake and data warehouse capabilities for streamlined data management and analysis. #Databricks #LakehouseFundamentals #DataManagement #Teqfocus

I believe a well-designed data warehouse can truly transform how businesses operate. Recently, we worked with a media agency to build a Snowflake data warehouse that became their single source of truth. Their data was scattered across multiple silos, making it hard to analyze and gain insights efficiently. The challenge was designing a schema that could handle billions of rows without slowing down performance. By optimizing the query structure and automating ETL pipelines, we achieved a 30% improvement in the speed of generating cross-platform insights. This allowed their team to make real-time, data-driven decisions effortlessly. This project reaffirmed my belief that a robust data warehouse is the backbone of efficient analytics. If your data is fragmented, investing in a scalable and high-performance solution like Snowflake can unlock faster insights and better business outcomes. It’s not just about managing data; it’s about empowering action. #DataWarehouse #Snowflake #ETLPipelines #DataAnalytics #BusinessInsights #complereinfosystem

Snowflake Summit key takeways: ⚡️Pandas helps in Data Manipulation, Data Analysis and Data Exploration ⚡️why? Easy, flexible and more functional and can be integrated with other libraries ⚡️pandas with snowflake Modin libraries help to run huge data set and faster ⚡️open source: Open code , open development and open Governance #Snowflake #Data #GenAI

Datalere brings unparalleled expertise to Databricks, empowering your organization with advanced data governance, warehousing, ETL, and orchestration capabilities. Our seasoned team ensures you get the most out of Databricks, optimizing your data infrastructure for superior performance and scalability. Learn how we can help: https://bit.ly/49rbMeg #Databricks #DataPlatform #DataOptimization #DataManagement

Well for all our discussion on iceberg today, this is a great read. Shravan T Srini Simhadri