TDWI’s Post

https://lnkd.in/gH49RUjQ #Neo4j® announced at #Snowflake's annual user conference, Snowflake Data Cloud Summit 2024, a partnership with Snowflake to bring its fully integrated native #graphdatascience solution within Snowflake AI Data Cloud. The integration enables users to instantly execute more than 65 graph algorithms, eliminates the need to move data out of their Snowflake environment, and empowers them to leverage advanced graph capabilities using the SQL programming languages, environment, and tooling that they already know. #graphdatabases #snow #datascience #datascientist #ai #artificialintelligence #ml #machinelearning #machinelearningalgorithms

Unlock the future of data analytics with Snowflake Cortex! ❄❄ In the rapidly evolving world of data analytics, staying ahead requires leveraging the best tools available. Introducing Snowflake Cortex, the next-generation data processing engine designed to seamlessly integrate with Snowflake’s robust cloud platform. This cutting-edge solution brings AI and ML capabilities right to your fingertips, redefining how we handle data and extract insights. Snowflake Cortex offers unparalleled performance and scalability, making it the ideal choice for any data strategy. Whether you're looking to perform real-time analytics, manage semi-structured data, or utilize large language models (LLMs) for deeper insights, Cortex has you covered. Explore the latest blog to discover how Snowflake Cortex can revolutionize your data strategies and drive innovation like never before. Embrace the future of data analytics today! Click here to learn more: https://lnkd.in/g_c8HuC9 #Kipi #Snowflake #DataScience #AI #MachineLearning #DataAnalytics #SnowflakeCortex Jason Small, Babu Sridharala, Veeranjaneya Ashok Tiruveedhula, Rakesh Reddy Velidandla, Drew Brigham, Prakash Jaya (JP) Nellore, Jason Ling, Madhivanan Anbalagan, Vijaysai Turai

Many companies must integrate data from Databricks, Snowflake, and even on-prem systems to feed AI/ML. They are entrenched in multiple platforms. This creates the need for platform-agnostic tools that integrate distributed data using multiple styles, including ETL, ELT, data streaming, and data virtualization. This reality was an important sub-text of Databricks' Data + AI Summit this week and Snowflake's Data Cloud Summit last week. I think it will drive renewed interest in data virtualization in particular. The large data platform providers - Snowflake, Databricks, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud - are expanding their toolsets to help data teams manage everything on their software. But IMO most companies will stick with multiple platforms thanks to technical debt, data gravity, sovereignty requirements, and of course the cost and complexity of migrations. Data virtualization becomes especially compelling because it reduces or eliminates the need to move data across platforms. So AI/ML models that require diverse, distributed datasets - but not complex transformations - can consume data through a virtual layer. This layer performs the following functions, using the example of structured data for predictive machine learning. > Create virtual views of data wherever it sits, using pointers to the actual source data. > Aggregate views so that data scientists or AI/ML models can consume the data in a consolidated fashion. > Control access by user, application, and dataset to enforce governance policies. > Transform data virtually to create merged, reformatted, filtered, and/or cleansed views. > Catalog metadata, including views and their lineage to physical source data. Together these capabilities enable data engineers, data scientists, and ML engineers to manage the lifecycle of features that train and prompt predictive ML models. I'll further explore data virtualization, along with ELT and streaming, in a webinar with Nick Golovin 🇺🇦, CEO and founder of @Data Virtuality (now part of CData Software), on June 18 at 1pm Eastern Time US. Join us to learn more and share your thoughts💥 https://lnkd.in/gQfjtZZT #dataaisummit #ai #data #datavirtualization Shawn Rogers Timm Grosser Jacqueline Bloemen Florian Bigelmaier Joo-Ang "Sue" Raiber Lucia Santamarina Anna van Evert Jana Branz

KNIME’s Path To Empowering Developers in the Evolving Data Science Landscape https://lnkd.in/dmN7tup7 In the rapidly evolving world of data science, companies are constantly seeking tools and platforms that can help them harness the power of their data. KNIME, an open-source data science platform, has been at the forefront of this revolution, providing a comprehensive environment for data preparation, machine learning, and analysis. I recently had the opportunity to catch up with Michael Berthold, Founder and CEO of KNIME, at the Snowflake Data Cloud Summit, where we discussed the company's journey over the past five years and its vision for empowering developers, engineers, and architects in the data science landscape. Evolving With the Times Over the past five years, KNIME has undergone significant changes to stay ahead of the curve. "We completely changed both of our technologies," Berthold revealed. The analytics platform is now browser-ready, and the KNIME server has been replaced with a cloud-native business hub. The company also recently launched a SaaS offering, allowing users to access KNIME's powerful features without the need for on-premises installation.

📊 Is Your Data AI Ready? As interest in AI continues to grow, many of our customers are eager to explore its potential. However, ensuring your data is ready for this next step is essential. That’s where our Data Architecture Review comes in. Collaborate with a Certified AWS Data Architect to dive into the three main pillars of your data pipeline: Data Ingestion, Data Storage, and Analytics (AI/ML, Business Intelligence). Together, we'll identify your challenges and opportunities, laying the foundation for a robust data strategy. Intrigued? Visit our webpage for more details! https://hubs.la/Q02xdNVz0 #aws #awscloud #data #analytics

Transform Your Data Experience with BigQuery Data Canvas! 🚀 Introduced at Google Cloud Next '24, this cutting-edge platform is setting new standards in data analytics. From seamless natural language integration to intuitive visual workflows, BigQuery Data Canvas empowers professionals to navigate complex data with ease. Read our latest article to learn more! Read here🔗 https://buff.ly/49HNRqq #BigQuery #DataAnalytics #TechInnovation #GoogleCloud #BusinessIntelligence #economize

The Limitless Potential of Neo4j Graph in the AI Data Cloud Neo4j brings the industry’s most extensive library of graph algorithms to Snowflake End users will have a complete graph analytics offering in Snowflake that doesn’t require navigation between multiple environments. #genai #graphdatabase #snowflake #algorithm #analytics #easeofuse #SnowflakeSummit #Neo4j #graphdatascience #ML

Persistent Achieves Premier Services Partner Status with Snowflake, Boosting Data Management and Analytics Capabilities Persistent Systems (BSE and NSE: PERSISTENT), a global Digital Engineering and Enterprise Modernization leader, today announced that it achieved Premier Services Partner status with Snowflake, the Data Cloud company..... To Read More: https://lnkd.in/dUgJduaQ Persistent Systems | Bidish Sarkar | Snowflake | Amy Hirst-Kodl | Anish Thambi | #PersistentSystems #Snowflake #PremierServicesPartner #DataCloud #DigitalEngineering #EnterpriseModernization #DataJourneys #DataManagement #Analytics #AI #MachineLearning #DataPlatforms #iAURA #AI #DataInsights #InformedDecisions

The latest update for #SnowflakeDB includes "New Snowflake Features Released in January 2024" and "Top 3 Data + #AI Predictions for Manufacturing in 2024". #analytics #BusinessIntelligence #BI #DataDriven https://lnkd.in/dH8h5ftY

What strategies are you using to optimize costs in your data operations? 🤔 The quest for efficiency and cost-effectiveness is real, especially in the ever-evolving world of data engineering. This insightful article from Overcast discusses practical approaches to cost optimization while harnessing the power of Databricks. The piece emphasizes the importance of understanding your workflow, leveraging serverless technologies, and optimizing resource usage. By being strategic about how we manage and scale data workloads, we not only save money but also enhance our overall data strategy. 🌟 Cost efficiency isn't just a trend; it's a necessity for sustainable growth in today's data landscape. How are you focusing on optimization in your organization? Let me know your thoughts! 💬 #DataOptimization #CostEfficiency #Databricks #DataEngineering #AI https://lnkd.in/gy4Pk62E