Don Dengel’s Post

Learn how #graph #databases and knowledge graphs offer advantages over standard/relational databases. https://lnkd.in/grEJF4Ci

Learn how #graph #databases and knowledge graphs offer advantages over standard/relational databases. https://lnkd.in/exQDCaS5

Learn how #graph #databases and knowledge graphs offer advantages over standard/relational databases. https://lnkd.in/eUQ3JkVh

Learn how #graph #databases and knowledge graphs offer advantages over standard/relational databases. https://lnkd.in/gNz8TfM8

Vector, Graph and Relational databases all have benefits for how they enable data analysis and workflows. But when it comes to GenAI efforts, it can be difficult to figure out which database model is right for your use case.  George Lawton with TechTarget compares and contrasts the different databases and shares how enterprises can consider using them harmoniously.  Full analysis here:  https://lnkd.in/eXCyd65C  #Databases #DataAnalytics #LLMs #GenAI #DataManagement #DataStorage 

Meet Hannes Mühleisen, 2024 Big Data Leader to Watch, Full article link 👇🏻👇🏻 https://lnkd.in/deMpEbrH Introduction Hannes Mühleisen: The Power Behind DuckDB The emergence of DuckDB has been a significant development in the world of databases over the past couple of years. Hannes Mühleisen, a professor of data engineering, developed DuckDB, which has been gaining popularity due to its fast and powerful SQL performance without the complexity of distributed database […] #artificialintelligence #machinelearning #ML #AI

Meet Hannes Mühleisen, 2024 Big Data Leader to Watch, Full article link 👇🏻👇🏻 https://lnkd.in/deFesMgZ Introduction Hannes Mühleisen: The Power Behind DuckDB The emergence of DuckDB has been a significant development in the world of databases over the past couple of years. Hannes Mühleisen, a professor of data engineering, developed DuckDB, which has been gaining popularity due to its fast and powerful SQL performance without the complexity of distributed database […] #artificialintelligence #machinelearning #ML #AI

Vector Databases the cornerstone of RAG. When scoping data design solutions this is a key consideration for future approaches to LLMs and other modern tooling. #data #database #architecture #datadesign #llm #ai #rag #analytics https://lnkd.in/eyHD5wHv

As data scientists, one of our most thrilling quests is the search for rich, versatile datasets to fuel our analytical endeavors. Whether crafting sophisticated algorithms or forging predictive models, the foundation of groundbreaking work lies in the quality and diversity of the data we harness. I'm thrilled to share a curated list of stellar resources that are goldmines for data practitioners. From the extensive archives of academia to the dynamic repositories of industry leaders, these platforms offer open access to a vast spectrum of data, waiting to be explored: 1- AWS Data Registry - https://lnkd.in/eYe9iKzZ 2- Data Hub - https://datahub.io/ 3- Data Science 101 by Ryan Swanstrom - https://lnkd.in/eN-vmaRq 4- Data Science Resources by Jonathan Bower - https://lnkd.in/eC_fK-Zf 5-Deep Blue Data - https://lnkd.in/eUKkMum8 6- EU Open Data - https://data.europa.eu/en 7- Fun Datasets to Analyze by Springboard - https://lnkd.in/eZpZD_bY 8- German Government Data - https://www.govdata.de/ 9- Google Dataset Search: - https://lnkd.in/esF7frTu 10- Hugging Face Datasets - https://lnkd.in/esBFDWkv 11- Kaggle Datasets - https://lnkd.in/efuWRR3k 12- KDnuggets Datasets - https://lnkd.in/esfw6z4a 13- More from Hugging Face - https://lnkd.in/eUwDRrqW 14- Open Power System Data - https://lnkd.in/enbsDWZ2 15- OpenEI - https://data.openei.org/ 16- Oracle Cloud Open Data -https://lnkd.in/ecarPpJG 17- Papers with Code Datasets - https://lnkd.in/e9Ry7uX7 18- Public APIs - https://lnkd.in/e7PyMRYm 19- Quora on Finding Datasets - https://lnkd.in/eKzHGXtA 20- Reddit Datasets Community - https://lnkd.in/exRaZb8v 21- Top 20 Open Data Sources on Data Science Central - https://lnkd.in/eYi8_G96 22- UCI Machine Learning Repository: Browse Data - https://lnkd.in/eGFkWqzf 23- US Government Data - https://www.data.gov/ Dive into these repositories to discover datasets ranging from energy consumption metrics to cutting-edge AI research materials. Each dataset offers a unique opportunity to solve real-world problems, test theories, or kickstart your next big project. Let’s harness the power of data to transform insights into action! 🌟🔍 #DataScience #MachineLearning #BigData #OpenData #AI

Ever wondered what underlying data structures are used to perform distinct count functions on Spark DataFrames, caching in Redis, or select queries in BigQuery tables, which help to work efficiently on larger datasets This efficiency is largely due to the use of probabilistic data structures. Probabilistic data structures are used to achieve space-efficient approximations or estimations of certain properties of a dataset, such as cardinality, frequency, and data processing tasks. They rely mostly on hash functions and bit arrays to perform these operations while ensuring optimal memory consumption. Popular probabilistic #data structure algorithms include bloom filters, count-min sketches, and hyperloglog. Some of the advantages of using such algorithms in big data applications include ease of handling large amounts of data, memory efficiency, and providing approximate answers to queries in real-time. Reference - https://lnkd.in/gAxW7wwK #dataengineering