Eran Lador

Observe natively integrates with Snowflake Trail and continuously collects telemetry data. Observe for Snowflake provides complete visibility into the performance of Snowflake Data Cloud, UDFs, and Snowpark Container Services apps. In episode ten of of our #observability trends webinar series, Kyle Champlin and Daniel Odievich will explore how you can maximize performance and achieve compliance readiness in Snowflake with Observe. Register now: https://lnkd.in/gftXxx_4 (P.S. - If you can’t attend on the day, register anyway and we’ll send you the recording.)

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I often get asked by well-intentioned teams how they can apply AI to marketing. In many cases, the team is missing key data maturity milestones, and I have to deliver the difficult news that they haven't met the prerequisites. Tameem Iftikhar does a great job articulating the core components of the data strategy I often recommend for those teams. Take a look 👀

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Join PyTorch at Snowflake's #DataCloudDevDay on June 6 in San Francisco to learn how to accelerate GenAI performance with Native PyTorch. Register: https://hubs.la/Q02xQStt0

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Databricks is “doubling down” on working with its systems integrator partners, with co-founder and CEO Ali Ghodsi viewing services partners as an important part of the vendor’s go-to-market and widespread adoption of artificial intelligence tools. “The AI revolution is not going to happen without the SIs,” Ghodsi said during a Q&A session at the Data+AI Summit. Here’s what else he said during #DataAISummit:

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🚀 KAWA Analytics is the easiest way to build enterprise-grade data apps on Snowflake From risk management to dynamic pricing, ESG to market abuse surveillance, KAWA AI Co-builder makes it effortless. 🎥 Watch our product manager Narjisse Bouri demonstrate how to build a market abuse monitoring system in no time.

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Step into the lakehouse by tuning into our latest interview: The Databricks Data Lakehouse with Josh Miramant 📼 ☑ Discover how Lakehouse architecture merges the best of data lakes and warehouses to offer enhanced governance, scalability, and accessibility: ☑ Understand Lakehouse basics and its transformative benefits. ☑ Explore essential tools like Delta Lake, Apache Iceberg, and Apache Hudi. ☑ Learn best practices for effective implementation in your business. 🔗 https://lnkd.in/e9PQNEVr #DataManagement #Data #LakehouseArchitecture #Innovation #Databricks #DataScience

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Read this great story from hipages Group on how they overcame challenges including legacy data systems slowing productivity and insight, requiring days to transition data between multiple warehouses. Partnering with Databricks hipages overcame these hurdles by transforming their data architecture into a single lakehouse architecture, enhancing both speed and reliability.  “We were able to tackle all our platform, productivity and insights challenges by migrating to a single platform, Databricks. Our architecture is more streamlined and our data users are more autonomous.” — Minas Kamel, Senior Product Manager, hipages #data #ai #lakehouse https://lnkd.in/gfXSqNSy

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Databricks is best known for its data engineering, processing, and data science capabilities that make it easy for enterprise data teams to ingest, process, and store large amounts of data before operationalizing it in AI/ML applications or building data warehouses to enable SQL querying. All of these capabilities can be leveraged inside the customer’s #cloudobjectstorage without moving the data into a proprietary storage platform, which would result in high data egress fees, high storage costs, and undesirable data retention trade-offs at scale. ☁️ But when it comes to using Databricks for log and event analytics, customers who want to leverage the deeper capabilities of specialized third-party tools will often need to ship data outside of their cloud object storage and into those platforms. This can result in high data egress and storage costs, especially for organizations that generate large volumes of log data. Enterprises may attempt to reduce short-term data storage costs by restricting the data retention window in third-party tools, but this also reduces the viability of long-term log analytics use cases like application performance trend analysis or security incident root cause investigation. Power log analytics use cases with ChaosSearch on Databricks. ChaosSearch can now be deployed in Databricks to deliver our simple, powerful, and cost-effective log analytics capabilities on the Databricks platform. Learn more: https://bit.ly/4eXFHxU

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We just launched a native Snowflake connector that cuts processing speeds AND costs in half by integrating SQream Blue's GPU-accelerated technology. Benchmarked by the TPCx-BB analysis, SQream Blue outperformed Snowflake, handling 30 TB of data at 2X the speed and 1/2 the cost. This connector allows users to seamlessly integrate SQream’s platform into existing Snowflake workflows, reducing data transfer needs and optimizing cloud spend. Experience efficiency like never before with SQream Blue.

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Same Chicken (Iceberg), different Curries (Engines) Cloudera: Read Snowflake Data from Polaris External Sync Catalog I am able to "read" and integrate Snowflake data through Polaris with other data in Cloudera Spark. Cloudera Snowflake

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This week marked a watershed moment for Snowflake: We launched #SnowflakeArctic, a state-of-the-art large language model uniquely designed to be the most open, enterprise-grade LLM on the market. Arctic not only outperforms many leading open models, but we brought it to market at one-eighth of the cost and in less than three months compared to other models. Arctic will significantly enhance our ability to deliver reliable, efficient AI to our customers. By delivering industry-leading intelligence and efficiency in a truly open way to the AI community, we are furthering the frontiers of what open-source AI can do. https://lnkd.in/eueT5Wcs

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Discover how Wavicle helped Pilot Company transition to a unified data framework with Amazon S3, Databricks, and Snowflake, cutting latency and reducing costs for faster, cost-effective analytics and better decision-making. https://hubs.la/Q02KNPYD0 #cloudmigration

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🔈 New Startup Project Podcast: “Building RelationalAI: AI Coprocessor for Snowflake” Molham Aref joins Nataraj Sindam to discuss his long journey in AI and machine learning, the decision to build RelationalAI on the Snowflake AI Data Cloud, enterprise GenAI powered by business ontology and Compound AI, and the lessons he has learned as a founder. 🎙️ Why Listen? Gain candid insights into Molham’s mission to simplify intelligent application development on a data-centric foundation and to create a comprehensive platform for enterprise AI. 🌟 Learn from his journey, the challenges he overcame, the pivotal role of mentorship, and the many lessons learned as a three-time startup founder. https://hubs.li/Q02Zs80f0 #IntelligentApplicationDevelopment #GenAI #Ontology #Snowflake #Podcast #AI #Leadership #Innovation #ModernDataStack

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Navigating the Databricks landscape can be challenging, even for seasoned data engineers. Our resident expert Robert Leach wrote about the common mistakes people make, and offers practical advice on how to: 👉 Tailor your clusters to drive for performance and cost efficiency 👉 Ensure consistency, compliance, and reliability with proper data governance policies 👉 Boost your productivity by mastering the Databricks Command Line Interface 👉 Implement robust access controls, encryption, and regular security reviews 👉 Make the most of the platform’s capabilities You’ll want to bookmark this post! 🔖 https://bit.ly/45DZqhY

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Databricks CEO Ali Ghodsi has transformed his startup into a Silicon Valley giant now valued at $62 billion. Founded 11 years ago, Databricks started by giving away its software for free but shifted gears under Ghodsi’s leadership in 2016, introducing paid upgrades. His “strategic surgeries,” such as creating the AI bot R2-D2 to boost efficiency, have helped Databricks scale while meeting investor demands for productivity. Today, its software powers data-driven innovation at companies like Walgreens, which forecasts prescription inventory, and Rivian, which enhances EV battery performance—underscoring its growing role in the AI era. #Databricks #StartupSuccess #AIInnovation #DataScience #Leadership https://lnkd.in/e6sVm_hV

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"The new Columnar capabilities enable rapid real-time intelligence for data analysis and quick insights, along with further opportunities for traditional ML and AI" ⚡ Check out this write up on Capella columnar from private preview user Will Thayer, MBA Technologist 📝 https://bit.ly/3JGo8UR

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