Manish Patel

Accelerate your platform implementation of the Snowflake Data Cloud. ❄ FUEL is built to cover these 4️⃣ milestones in 5️⃣ five weeks. 1. Discovery: Select sample data sources to replicate in Snowflake, decide on queries for visualization, and assess monitoring, logging, and auditing requirements. 2. Implementation: Build out a data platform infrastructure, a data pipeline to process your data, and create a dashboard in the tool of your choice. 3. Validation: User Acceptance Testing is conducted to validate the functionality of the data pipeline. 4. Project Handoff: Presentation of deliverables with proper enablement to transfer ownership of the platform assets. Explore more ➡ https://lnkd.in/gbqwTjx8

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Once described as "The best FREE add-on for your Snowflake platform," the Advisor Tool is your new best friend in helping improve the security and performance of your Snowflake account ❄ In just 10 minutes and a few clicks, you'll gain critical insights into your Snowflake environment and uncover tailored solutions to enhance your operations. To see the Advisor Tool in action, check out this interactive tutorial 👇 https://lnkd.in/gq_gkxm9 If you're ready to start leveraging the Advisor Tool, download it today from the Snowflake Marketplace👇 https://lnkd.in/gXMx2Zad Special shoutout to Justin G. for the kind words 🙏

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💪 Super excited to finally share the new Spark Performance Optimization capabilities on the definity platform! ✨ Spark data engineering teams and platform leaders -- ready to unlock performance insights and cost savings from day-1 ? ⚡ Proud of the definity team who's moving at lightning speed to deliver tangible value to our customers. Check out the article by Tom Bar-Yacov (link below) and reach out to us to learn more!

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AI & ML is where all the fuzz is if you work in a data team. But most data teams have only just scratched the surface. dbt surveyed thousands of data professionals in their '2024 State of Analytics Engineering Report'. They found that only a third of respondents currently manage data for AI model training today. But 55% expect to soon. In this post, I've looked into the AI & ML landscape if you work in a data team, including the importance of building on top of solid foundations → State of AI in data teams → AI and ML use cases  → Data quality in AI and ML systems → The five steps to reliable data https://lnkd.in/e-6JTHQm

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Your Snowflake bill shouldn't scale faster than your data. 👋 Meet Radar - Our new AI-powered tool that slashes Snowflake costs by 15-25% without sacrificing performance. No complex setup. No performance tradeoffs. Just click a few buttons and let our AI do the heavy lifting: 🔄 Automatically scales warehouses based on real-time demand 💰 Eliminates costly idle time ⚡️ Maintains your existing query performance 🤖 Works 24/7 in the background Sound too good to be true? Watch the demo below to see it in action 👇 #Snowflake #dbt #AnalyticsEngineering #DataEngineering

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I know many data teams and developers are actively building prompts to SQL convertors via LLMs - for the love of all things holy rethink this. I am all for experimentation, but there are no good evals for this use case (something Eugene Yan continues to advocate for), the "work" you are taking in can't be bounded in a meaningful way so your SQL database crunches data patterns it wasn't scaled for, and you must guard against injection attacks (yes that's still a thing) I think data access patterns for LLMs must be re-imagined. This "wrap a database with an LLM is handy", but it doesn't really work in practical terms. APIs and meta-data still offer a lot of value in this space and #function-calling is emerging as a durable pattern.

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Tired of outdated systems holding up you and your budget? Stop using paper in your daily operations and find more operational flexibility. See how Dragonfly Data Science tackled their data-collection needs: https://lnkd.in/eunniSRH

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Ramp's data team is growing! We just opened up analytics engineering role with a focus on solving quantitative problems across our growth and product surface areas. https://lnkd.in/es73XKk8 There are a few reasons why this role is exciting: The mission, opportunity and product. Corporate spend management is frustrating, reactive, and time consuming. Ramp gives finance teams visibility, control, and the ability to automate. Ramp enables companies to save money, in an industry where cards and spend management software have not historically been linked. You can see anecdotes all over from satisfied customers and companies. The analytics challenges. There’s a wealth of first party transaction data, second party spend and accounting data, and third party data from across the financial ecosystem. The opportunities are fascinating, and it won’t work without a world-class analytics platform. Finally, the team. Jenny Young, Ricky Meyers, Riley Waters, Ryan Stevens, and Ryan Delgado have built an incredible foundation for Analytics Engineering + Data Science. The speed of execution speaks for itself. There's a lot of very cool problems and a ton to build! Come join us 🚀🚀

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Optimize your Retrieval-Augmented Generation (RAG) pipeline for enhanced performance with Large Language Models (LLMs). This detailed guide covers strategies to improve data quality, chunking, embedding, query handling, and retrieval processes. By refining each component, you can significantly improve response accuracy and efficiency in LLM-driven applications. Perfect for anyone looking to enhance AI-powered systems! Read the full article here: https://lnkd.in/ghz3yFKp #generativeai #llm #rag

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Real-Time #Analytics News from the Past Week: 1. Acante announced the launch of Acante Data Access Observer. The new solution offers Databricks users automated and consolidated visibility into all data accesses within their environments. 2. During its Data Cloud Summit 2024 last week, Snowflake announced that Polaris Catalog, a vendor-neutral, open catalog implementation for #ApacheIceberg, will be open sourced in the next 90 days. 3. Fivetran announced the general availability of the Fivetran Managed Data Lake Service. The Fivetran Managed Data Lake Service simplifies #DataLake management by automatically converting customer data to popular open formats (i.e., Apache Iceberg or Delta Lake) before landing it in the data lake. When combined with Fivetran’s ongoing table management and maintenance, customers get the easy queryability and ease of use of a #cloud data warehouse, with the flexibility and scale of a data lake. 4. Immuta announced new data governance and audit capabilities for Retrieval Augmented Generation (RAG)–based #GenAI solutions across multiple cloud platforms. With this release, Immuta offers a multi-layer architecture for securing, monitoring, and auditing sensitive data accessed by RAG-based AI applications. 5. RelationalAI announced the public availability of its #Snowflake Native App on Snowflake Marketplace. Built with the Snowflake Native App Framework and Snowpark Container Services, RelationalAI enables customers to combine knowledge graphs with GenAI to get more value from their data and make better decisions. Read the rest on RTInsights ➡️ https://lnkd.in/eJ9agHt6

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In my experience as a data leader at companies of all scales (Target, Ask.com, Loom, Shipt & GoFundMe), these are the most common challenges with data analytics. That’s why I built Zetta. What’s your biggest challenge with data analytics?

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Unlock the full potential of your data with advanced LLM operations in Snowflake. Our latest case study dives into how organizations can leverage Snowflake’s platform to efficiently manage and scale large language models. Whether you're looking to optimize your AI capabilities or streamline complex data processes, this article offers valuable insights and practical strategies. Discover how you can elevate your data operations to the next level. https://hubs.ly/Q02NSh4V0 #DataOps #AI #Snowflake #LLM #CaseStudy

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Managing complex Snowflake environments by hand can lead to inefficiencies, manual errors, and “institutional knowledge” silos. Maximize your engineers' productivity by automating your Snowflake environment setup with Continuus' Infrastructure as Code (IaC). Our IaC solution automates the creation, modification, and deletion of key Snowflake objects like warehouses, roles, and databases. Using GitOps and CI/CD pipelines, we ensure consistent, version-controlled environments that eliminate configuration drift. Read this solution brief and contact us today to learn more about how IaC can streamline your Snowflake management. https://hubs.la/Q02R1G8w0

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🛡️ Data Security for Snowflake 🛡️ Proactively protect sensitive data and mitigate risk. PlainID is designed to give you full visibility and control across Snowflake Data Cloud, enhancing your security posture and improving the way you create, manage, and monitor data access policies PlainID's integration with Snowflake: ✅ Enables row-level security through data filtering and masking. ✅ Simplifies compliance with flexible policy creation and management. ✅ Supports policy governance, audit reporting, and lifecycle management. ✅ Offers both UI-based policy management and policy-as-code (REGO). ✅ Provides investigation and analytics tools to assess data access and identity connections. 👉 Click here to learn more: https://hubs.ly/Q02-kn6Z0 #PlainID#Authorization#DataSecurity#Snowflake#CyberSecurity#IAM#IdentitySecurity

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Have you noticed how technologies like portals, SharePoint, and custom-built apps are being replaced by newer solutions? Isn't it interesting how developing these new solutions has become far easier, leading to reduced development time? The trends we've seen with Snowflake and Palantir show how they disrupt different categories. Do you see how ServiceNow is disrupting the enterprise workflow space? Did you know that you'll also find a lot of RPA features within ServiceNow? Comment on the questions above based on your experience. Watch the full episode to find out: https://lnkd.in/g9dsXx24 #ERP #WBSRocks

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Have you noticed how technologies like portals, SharePoint, and custom-built apps are being replaced by newer solutions? Isn't it interesting how developing these new solutions has become far easier, leading to reduced development time? The trends we've seen with Snowflake and Palantir show how they disrupt different categories. Do you see how ServiceNow is disrupting the enterprise workflow space? Did you know that you'll also find a lot of RPA features within ServiceNow? Comment on the questions above based on your experience. Watch the full episode to find out: https://lnkd.in/g9dsXx24 #ERP #WBSRocks

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Real-world data is complex, and traditional large language models fall short when using structured data like prices and product categories. With Superlinked and Redis, you can integrate structured and unstructured data for high-quality information retrieval. This means faster, more accurate recommendations that truly resonate with your customers' needs. Read more here. https://lnkd.in/erngxz-n

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Do you struggle with securing identities? Over time, lost context, vague naming conventions or role sprawl can make it impossible to figure out what access you’re actually granting. See how the team at Snowflake used Veza to get visibility into the real access users had, and how they harnessed that knowledge to optimize their permissions for least privilege. “I think once a customer gets to the data that’s in Veza: the visibility, the actionability, they’ll question how they were able to live without that.” - Brad Jones, Chief Information Security Officer | Snowflake Thanks Brad Jones and Cameron Tekiyeh! Proud to work with your teams and all of Snowflake.

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