Jean-Philippe Player

Gemini in BigQuery changes the game for data engineers and analysts! More productivity ✔️ Improved query performance ✔️ Optimized costs throughout the analytics lifecycle—from ingestion and pipeline creation to deriving insights with AI ✔️ #GoogleCloudNext

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🔐 Boost Your Snowflake Security with Key Pair Authentication In our new blog, we highlight how key pair authentication is transforming data security for Snowflake users. This method offers a robust, efficient way to manage access and protect sensitive information. Learn how you can implement this feature to enhance your security framework.

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Exciting news! Snowflake has unveiled a cutting-edge system for large language models, featuring the powerful Llama 3.1 405B from Meta. This innovation brings a new level of efficiency and capability to AI applications. At 7Rivers, we're excited to harness this technology, enhancing our Data-Native™ approach to deliver unparalleled AI solutions for our clients. Our commitment at 7Rivers is to drive business value through use of intelligent data and advanced AI. Snowflake's latest offering, which allows real-time AI processing with minimal infrastructure, perfectly complements our mission. This advancement enables us to provide our clients with scalable, cost-effective AI applications, enhancing operational efficiency and driving impactful outcomes. The integration of Meta’s Llama 3.1 405B within Snowflake Cortex AI aligns with our Data Native™ Model, empowering us to create transformative solutions, optimizing processes, and creating a competitive advantage. Together with Snowflake, we're revolutionizing data usage to create intelligent, augmented business experiences. #AI #DataNative #7Rivers #Snowflake #GenerativeAI #BusinessTransformation

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Nate Nichols has been researching, building and deploying AI systems for 15 years. He does a great job breaking down the basics to help you with an AI roadmap to get started in your organization. Watch Nate and reach out if you want to learn more!

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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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A great read in iTnews today highlighting how Seven West Media leveraged Databricks to forecast audience view patterns on both a seven-day and 28-day horizon. The company is now scaling its use of Databricks to support additional analytics use cases. Andrew Brain, Audience Intelligence and Growth Director, emphasized the significance of this advancement, stating, "We can now understand what our audience is going to be from a value perspective on volume and also what they’re going to be watching." This insight is pivotal for their business. #databricks #ai Read more about Seven West Media's predictive analytics success here: https://lnkd.in/e4rb3_mp

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Open has become the state-of-the-art (for now), Meta have changed the game - yet again - with the release of the Llama3.1 suite of models. The acceleration in this industry is breathtaking. The headlines: * Llama 3.1 models are immediately available in Cortex today. ✅ * 405B packs a punch - out performing other leading flagship models across a variety of benchmarks. 📈 * 8b & 70b models have been updated too. The already capable models have seen significant jumps, particularly in math and coding, in just 3 months. 🚀 * Meta's 3.1 suite will be made available for serverless fine tuning. 🤯 * We've also released Snowflake Cortex Guard (GA) to safeguard LLM applications. 🛡

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We are excited to announce the launch of Ursa engine, our Kafka API-compatible data streaming engine that is built on top of Lakehouse. Ursa is not something entirely new; it is built on our many years of experience building and operating #ApachePulsar and #ApacheKafka. To learn more about the #Ursa engine, I recommend our #PulsarSummit keynote presentation and announcement blog post. Here, I’d like to delve deeper into some of its features: 1. Making Kafka API a first-class citizen: In #ApachePulsar, we developed a unified messaging model combining queuing and streaming semantics into one protocol, aimed at enhancing data sharing between business applications. Although powerful and increasingly adopted by enterprises and unicorns, the requirement for application rewrites is impractical in today’s economy. Instead of building data gravity around a protocol, we open up access to the robust stream storage layer by extending support to the Kafka protocol, transitioning from a single protocol to a multi-protocol platform, facilitating greater flexibility and integration. 2. Achieve Stream-Table duality via Lakehouse Storage: Data streams commonly end up in data lakehouses, where integrating and maintaining them can be complex and resource-intensive. Ursa simplifies this by storing data streams as lakehouse tables in object storage. This integration not only streamlines operations but also allows seamless access to operational data for analytical processes. Thanks to its multi-modal storage engine which fosters data sharing across teams and departments. 3. Object storage as the primary storage solution: To better align with cloud economics, particularly for large workloads, Ursa makes ZooKeeper and BookKeeper optional and utilizes a cost-optimized write-ahead log based solely on object storage with leaderless and stateless brokers. This architecture leverages object storage for both data replication and storage, minimizing inter-AZ data transfers and reducing costs for latency-relaxed, high-volume workloads. By integrating these features at the multi-tenant level rather than at isolated cluster levels, Ursa eliminates costly data duplication and movement across clusters, allowing teams to map directly to tenants and be able to share data across teams and departments more efficiently. We believe that the future of data streaming platforms lies in being open and multi-faceted, fundamentally different from traditional data platforms. They are designed not just to store and process data, but to enhance organizational capabilities by fostering data sharing across the organization and integration with other systems. More and more data streaming platforms will look more like Ursa, with multi-protocol, multi-tenant, and multi-modal capabilities. Check out our announcement blog post about Ursa: https://lnkd.in/ghEKTYE6 #ApacheKafka #Lakehouse #DataStreaming

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Snowflake: Iceberg Dynamic Tables https://lnkd.in/gtCpgHgu

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I'm excited to share #MosaicAI fine tuning #GenAI models is in public preview on #Azure #Databricks. it is the most integrated data processing and model training capability on cloud, enables you seamlessly ingest, transform, and preprocess your data, and pull directly into model training, all on the same Databricks platform without switching in and out of different products and tools. https://lnkd.in/gS87GaAT

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Now you can stream data into Snowflake using Amazon Data Firehose and Snowflake Snowpipe Streaming. Customers have been asking us for a direct integration with #Snowflake Snowpipe Streaming enabling them to stream clickstream, application, and #AWS service logs from multiple sources, including Kinesis Data Streams, to Snowflake. As with all #firehose integrations, this is super easy to setup and use. https://lnkd.in/eZ3T-wEa #Data #streaming #streamingdata #datastreaming

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Learn the ins and outs of data latency with this expert guide from Oracle. https://lnkd.in/g2P89X38

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Databricks Summit in San Francisco was an incredibly exciting experience. The energy and enthusiasm from like-minded professionals, all passionate about data and innovation, was truly inspiring Wednesday is shaping up to be the big day for Databricks news. CEO Ali Ghodsi will take the stage to deliver his keynote address starting at 8:30 a.m. PT. Unity Catalog is currently not open source, there is speculation that the company may change that to address concerns over lock in, in addition how Databricks builds products around Iceberg will be crucial, because it will have to court the open-source users who have been drawn to the product. Practitioners say the purchase of Tabular gives it a valuable strategic asset to abstract and connect to several data-management platforms and services.

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Databricks Lakehouse Monitoring is now GA A unified approach to data and AI monitoring allows you to easily profile, diagnose, and enforce quality directly in the Databricks Data Intelligence Platform. Built on Unity Catalog, Lakehouse Monitoring requires no extra tools.

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#Snowflake summit 🤜🤛 #Databricks data+AI summit: Snowflake's platform keynote and Databricks keynote today are worth checking out.

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This was a huge interview with Databricks CEO cofounder Ali Ghodsi and he gave a bunch of new nuggets of insight that we haven't heard before. New AI Infrastructure Advancements will usher in the next data platforms for GenAI apps. The market and product shifts from Analytics to Data Platforms is evident in the market landscape and the data is showing the first signs of visibility that this is happening big time. Key drivers: Open Table Formats: There is a growing interest in adopting open table formats, which allow any compute to operate on any data. This flexibility is crucial for organizations looking to optimize their data workflows and integrate various data platforms seamlessly. Governance Layer Shift: The control point is shifting from traditional database management systems (DBMS) to the governance layer. This shift underscores the increasing importance of data governance, security, and compliance in the modern data landscape. AI-Enabled Intelligent Data Applications: AI is enabling the development of intelligent data applications that automate business processes and drive unprecedented productivity. These applications are becoming critical for organizations aiming to leverage AI to gain a competitive edge.

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Thank you Databricks for hosting our folks at the Data+AI Summit. The team has been so excited to attend. The topics shared at the event reminded me of a Databricks article from 2021 that celebrated the progress of Arrow: “Apache Arrow has recently become the de-facto standard for columnar in-memory data analytics, and is already adopted by a plethora of open-source projects.” (Read it here: https://lnkd.in/gUEcsgRZ) Arrow recently crossed the 1 BILLION annual download mark. Voltron Data is the leading maintainer and contributor to the Arrow project, and numerous other open standards, and we are incredibly proud to see this. Arrow is actively used for building new integrations with #GPU acceleration that are driving forces for Machine Learning Pre-Processing and Analytics at scale, thanks to the amazing foundation of RAPIDS.ai. Our query engine, Theseus, is built with Arrow and RAPIDS and is a prime example of how powerful tools built with Open Standards are. Theseus can process 100TB queries in SECONDS. Check out the numbers in our benchmarking report: https://lnkd.in/gMKbxcrU

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