Tyler Hutcherson

⚖️ Tech Comparison: Redshift vs Druid https://lnkd.in/e3ZCD3xb Like {hashtag|\#|Snowflake}, {hashtag|\#|Redshift} was built for traditional BI and infrequent reports. Not having multi-level indexing and time-based partitioning means inefficient compute with more data processed than necessary. When the analytics use case requires high performance on large data sets or concurrency requirements and streaming data, then Apache Druid is ideal.

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Developers can now store, index, and query vectors in PostgreSQL using pgvector, eliminating the need for a dedicated vector database. This blog from Yugabyte expert Brett Hoyer deep dives into pgvector, and explains how to store text embeddings as vectors, execute similarity search, choose between indexes, and scale with distributed SQL.💡 Find out more!⬇️ https://hubs.la/Q02JyYrM0 #YugabyteDB #pgvector #vector #AI #Postgresql #cloudnative #distributedsql

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📣 At Databricks, we want to help customers build more #inference-friendly #llms. Our MixAttention architecture demonstrates that reductions in KV cache size make it possible to maintain model quality while improving inference speed and reducing memory footprint. Read our latest blog post to learn more: https://lnkd.in/gcHxM4DX

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We recently started a new LinkedIn Live series for everything Apache Hudi and Open Lakehouses 🎉 In the very 1st episode, Dipankar went over the fundamentals of Apache Hudi and showed hands-on examples of running transactions with compute engines like Apache Spark. If you missed the live, here's the recording: https://lnkd.in/dm-24JzC Check it out and share with the Hudi community! #dataengineering #softwareengineering #lakehouse

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Just out! Chapter 2.3, Domain modeling, explores how you can defend against poorly thought out, poorly understood architectures. In this chapter of the playbook, we walk through applying domain modeling to our model use case. https://bit.ly/3z8Sc9y #modeling #DDD

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Uncover the magic of self-querying retrievers with Snowflake Cortex AI in the latest blog post from #DataSuperhero Fabian Hernandez! Imagine your natural language queries transforming into super-smart, structured searches—effortlessly pulling out the info you need.

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Check out Postgres expert, Cady Motyka's new blog where she delves into what Automatic DDL replication entails and how it can be leveraged effectively with #pgEdge. Read the "Using Automatic DDL Replication with #pgEdge Distributed PostgreSQL" blog today! https://hubs.la/Q02wxl7S0 #postgresql #postgresqlprofessionsals #ddlreplication

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Check out Postgres expert, Cady Motyka's blog where she delves into what Automatic DDL replication entails and how it can be leveraged effectively with #pgEdge. Read the "Using Automatic DDL Replication with #pgEdge Distributed PostgreSQL" blog today! https://hubs.la/Q02Ffk5L0 #postgresql #postgresqlprofessionsals #ddlreplication

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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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Interested in building production-level data agents with LlamaIndex? Our new blog post by Jerry (Saisai) Shao and Lisa N. Cao shows step-by-step how you can use Apache (incubating) Gravitino to build your index and vector stores directly on top of your data catalog. Check it out below! https://lnkd.in/g642hUew

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Unlock the Power of Data Lake Table Formats with Alex Merced, Developer Advocate at Dremio! 📊 Join Alex Merced as he delves into "The Who, What, and Why of Data Lake Table Formats." 📅 Save the Date: https://lnkd.in/eFZ6m5HV 🔍 Key Learnings: - Explore how data lake table formats handle large datasets. - Learn how these formats optimize query performance on extensive data. - Understand the importance of data durability and recoverability. - Discover how these formats ensure data is auditable and compliant. Don't miss out on this opportunity to level up your data analytics game! 📈 #DataAnalytics #DataLake #Dremio #TechTalk

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Llama3 on Databricks. Databricks is changing the game with making open models easily usable.

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How to break down compute costs by individual queries In Snowflake Notebooks Chanin Nantasenamat, PhD, Snowflake Senior Developer Advocate, walks through the process of breaking down compute costs by individual queries in Snowflake Notebooks, thereby allowing teams to identify high-cost operations. 📺 Watch the Tutorial: https://okt.to/fbcPah 📓 Demo Notebook: https://okt.to/SG8Q4P

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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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New feature release - Job metrics timeline view! Ever wonder why your Databricks job performance changes over time? You may ask yourself questions such as: “Why is my runtime increasing since last week?” “Is the cost of this job also increasing?” “Is the input data size changing?” “Is my job in danger of crashing?” Understanding why your job performance is changing is critical to both optimize costs and prevent crashes. In this post we show a new visualization timeline features to help engineers find root causes quickly. Try it out today with Gradient! #dataengineering https://lnkd.in/ebPjtU7y

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A common misconception with AI and machine learning: “all of the resources, infrastructure and technology" it takes to get started. With Databricks ML Flow, that’s simply not the case. In the video ⬇️ Kenny Shaevel breaks down how Databricks makes it easier to manage and track your models while ensuring full transparency, traceability, and governance—without the headache. Kenny covers everything from: - Simplifying Machine Learning Implementation - Utilizing AutoML for Quick Data Evaluation - The Importance of Feature Engineering - Leveraging Feature Store for Reusability - Governance and Traceability with the Model Registry - Seamless Integration and Application of Models Excited for more? Kenny will be diving even deeper into Databricks in our upcoming live stream series. Keep an eye out for details coming soon! 👀 #machinelearning #databricks #dataanalytics #MLFlow #AutoML #featurestore #dataengineering #techinnovation

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