Kunal Nagda

If you'll be at the #DataAISummit, join Ashok Singamaneni and myself (Scott Haines) as we teach you the ropes of building "Effective Lakehouse Streaming with Delta Lake and Friends". https://lnkd.in/gqmcJmRC We'll be teaching lessons learned (the hard way), and showing how streaming-first is the correct fit for "most" if not all your workflows. Learn to leverage #DeltaLake like an expert, and we'll introduce you to some effortless patterns for making your streaming journey a breeze. You'll also hear from Ashok Singamaneni as we talk about how to leverage painless workflow orchestration with #brickflow, and how to build data trust-networks using #sparkexpectations. #spark #deltalake #sparkexpectations #brickflow #nike #oss #dataai

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In an exclusive conversation with Debanagana Ghosh, moneycontrol.com, Oracle's #AI engineer Saurabh Netravalkar highlights the growing impact of #dataanalytics in sports, aiding teams in #performance optimization and #strategicplanning and how well he is balancing his love for #cricket and #technology Oracle. #AI #Dataanalytics #worklifebalance #CloudWorld https://lnkd.in/gf2anwvV

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I am extremely bullish on the growth of AI – and SingleStore – in India. I sat down with Amit Raja Naik of Analytics India Magazine (AIM) to discuss why we’re so excited about the Indian market, why simplicity is key to scaling AI, and why the ultimate use of data is to improve the lives of the underserved. Check out my full interview here: https://lnkd.in/eD7zKj8h #AIM #AI #Data #SingleStore

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Imagine if you had a generative AI assistant to help you explore data and uncover insights faster. Find out how Einstein Copilot for Tableau can help.

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It gives us great pleasure to announce that Apache Spark in BigQuery is now officially available in Private Preview. This feature expands BigQuery’s processing capabilities by offering Apache Spark as an OSS processing engine under a unified control plane (admin, monitoring, billing, security, governance). It is the first step in delivering on the Unified Data Platform vision and was a key announcement at Next ‘24 #googlecloud #bigquery #spark

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India’s data streaming ecosystem is scaling at an unprecedented rate, and Confluent is powering that transformation. In a recent interview with DataQuest, Will LaForest and Rohit Vyas explored how Confluent is enabling industries like finance, digital retail, and government to leverage real-time data for maximum impact. They highlighted our investment in making data streaming accessible to startups, supporting AI-powered applications, and securing data across multi-cloud environments. Read more to discover how Confluent is aligning our platform with India’s rapid digital progress!

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Oracle and Google Cloud just made a major move for AI-powered businesses. Now, you can run Oracle's industry-leading #databases directly on Google Cloud, seamlessly integrating with Google's cutting-edge #AI and #analytics tools. #VertexAI #Gemini #googlecloudpartners

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When managing Snowflake at scale, it can be difficult to balance cost and performance. Capital One Slingshot makes it easier, building on tools we created internally to help our own teams streamline provisioning, optimize workloads and reduce query inefficiencies. See how Slingshot can help you maximize your #Snowflake investment. https://bit.ly/3YyE4zk

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What are collations, and why should you care (especially if you are migrating a data warehouse workload to the cloud)? In a #database, a collation is a set of rules that determine how data is sorted and compared, usually applied to how text data is sorted in different languages for the purpose of indexing text data and for making comparisons between text values. Different languages have different character sets and ordering. When you are migrating a traditional #datawarehouse workload to the #datacloud, robust collation support plays a pivotal role in ensuring your data behaves predictably, especially when dealing with case sensitivity. Some examples of where collations come into play: > Data Consistency: Collations define how the database sorts and compares strings. Choosing the wrong one could lead to unexpected results, impacting everything from reports to application functionality. > Case Insensitivity: Are you dealing with names, emails, or any data where case differences shouldn’t matter? Collations like _ci (case insensitive) ensure that ‘John’ and ‘john’ are treated as the same, simplifying queries and ensuring data integrity. > Performance: Optimal collation selection can significantly boost query performance by reducing unnecessary comparisons and sorting operations. > Global Reach: In a connected world, collations also affect how your data interacts across different languages and locales. Choosing the right collation ensures your data remains consistent and understandable regardless of where it’s accessed. At Snowflake, we provide a broad range of collations tailored to your specific needs, including the recently launched upper, lower, and locale-ci. Curious to learn more? Check Qinyi Ding's insightful deep dive into choosing the right case-insensitive collation in Snowflake: https://lnkd.in/dz9UaHXx

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🚀 Boost Your Apache Spark Game! Struggling to maximize Spark performance? Dive into this in-depth guide on Adaptive Query Execution (AQE) — Spark's game-changing feature for dynamic query optimization. Learn how AQE can fine-tune shuffle partitions, manage skewed data, and auto-optimize join strategies for blazing-fast data processing. Perfect for data engineers and analytics pros looking to unlock Spark's full potential. 👉 Read now and supercharge your queries! https://lnkd.in/gjqgby4h #DataEngineering #BigData #ApacheSpark #AQE #PerformanceOptimization

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Learn the ins and outs of data latency with this expert guide from Oracle. https://lnkd.in/gnQJdJgS

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How will you score with “price a tray?” Are you hungry to modernize your retail store operations with AI at the edge? Learn how Google Distributed Cloud serves up the latest AI, Kubernetes, and on-premises computing in this interactive “price a tray” demo.

Continuing my #100PapersChallenge In the last post, I discussed one of the industry leaders in the Data warehouse world: Google BigLake, an evolution of Google BigQuery. Let's take another leader today: Snowflake. I will discuss the paper Snowflake: The Modern Cloud-Based Data Warehouse. Snowflake is a modern system and comes with 𝐡𝐞𝐚𝐯𝐲 features: 🔹 Snowflake was designed from the ground up to be an 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞-𝐫𝐞𝐚𝐝𝐲 and 𝐜𝐥𝐨𝐮𝐝-𝐛𝐚𝐬𝐞𝐝 data warehouse. 🔹 Snowflake’s architecture 𝐬𝐞𝐩𝐚𝐫𝐚𝐭𝐞𝐬 𝐬𝐭𝐨𝐫𝐚𝐠𝐞 𝐟𝐫𝐨𝐦 𝐜𝐨𝐦𝐩𝐮𝐭𝐞, allowing independent scaling of resources. 🔹 No more headaches with 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭, scaling, or maintenance. Snowflake handles it all. 🔹 Snowflake 𝐨𝐩𝐞𝐫𝐚𝐭𝐞𝐬 𝐨𝐧 𝐒𝐐𝐋, making it easy for teams to query and manage data. 🔹 Snowflake natively supports 𝐬𝐞𝐦𝐢-𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 data formats and 𝐬𝐜𝐡𝐞𝐦𝐚-𝐥𝐞𝐬𝐬 data. 🔹 Enjoy fast query execution with 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜 𝐪𝐮𝐞𝐫𝐲 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐜𝐚𝐜𝐡𝐢𝐧𝐠. 🔹 Snowflake offers a cost-effective 𝐩𝐚𝐲-𝐚𝐬-𝐲𝐨𝐮-𝐠𝐨 pricing model, allowing you to only pay for what you use. Let's dive deep to know the details: 🌐 Read on my website: https://lnkd.in/gYvx8SGi 📖 Read on Medium: https://lnkd.in/g8ggG6nz To get these articles in your inbox, Please subscribe here: https://lnkd.in/g_gGV3rm 👍 Like, share, and comment to spread the knowledge! Happy Learning! #Snowflake #DataWarehouse #CloudComputing #DataAnalytics #BigData

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