Itay Maoz’s Post

Are you interested in learning more about Elastic Search and its components? Check out this informative article discussing nodes, clusters, shards, and indices. The article breaks down the complex concepts into easily understandable pieces. Gain a better understanding of Elastic Search and its capabilities by reading this article today! Link: https://lnkd.in/d9MHhwwe

𝑴𝒚 𝒍𝒆𝒂𝒓𝒏𝒊𝒏𝒈 𝒕𝒐𝒅𝒂𝒚 - 𝒅𝒂𝒚 𝟖 📚 Today, I learned about 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐎𝐩𝐞𝐧𝐒𝐞𝐚𝐫𝐜𝐡, a powerful vector and text search database. Almost all GenAI applications depend on Retrieval Augmented Generation (RAG) to feed proprietary data to GenAI models for additional context. For efficient search and augmentation of such proprietary data, there is a need of a vector database like OpenSearch. You can scale OpenSearch clusters for any search performance requirement. However, there are some best practices to keep in mind including count of shards in an index, shard to CPU ratio, primary vs replica shard count, optimal shard size to keep, etc. Learn more: https://lnkd.in/grwNfCMF #learningoftheday #rag #vectordatabase #opensearch #aws #search #performance

If you’re an #AmazonNeptune Amazon Web Services (AWS) user, you’ll be excited to know about the benefits of our #OpenCypher support. It offers: 💡 Familiar SQL-like syntax: OpenCypher’s intuitive language minimizes the learning curve for #SQL, #Cypher, or #GQL users, making it easy to start querying graph data. ⚙️ Simplified query optimization: Write, optimize, and run efficient queries effortlessly—even for non-technical users. 🔎 Efficient querying for insights: Explore complex data relationships, detect fraud, or map dependencies with advanced pattern-matching and Linkurious’ intuitive #graphvisualization. Not sure whether OpenCypher or Gremlin is right for your needs? Check out our article: https://lnkd.in/ePZuQRpU #Technology #GraphTechnology

more zero-ETL integration options with AWS Databases..

Exciting News! Excited to announce that OpenSearch Serverless now supports 30TB collection for timeseries workload! Read all about the details and benefits of this upgrade in our latest blog post https://lnkd.in/gnHinrH5 co-authored by Milav Shah Qiaoxuan Xue Satish Nandi With this enhancement, you can Scale your log analytics workloads with ease to higher storage and, handle massive datasets with confidence Huge thank you to our incredible engineering and product team for making this happen! Michelle Xue Pavani Garimella Baddepudi Ashok Thirunarayanan Jon Handler Check out the blog post to learn more and stay ahead of the curve with OpenSearch Serverless! #OpenSearch #Serverless #search #logs #Analytics #Scalability #Innovation

🔍 Consistent hashing isn't just a clever trick; it's a game-changer in data management! Developed at Akamai in the late '90s, this method distributes data across nodes using a hash function, ensuring even load distribution and avoiding hotspots. 🔄 It's not just about spreading data randomly; consistent hashing forms a keyspace continuum, making scaling and replication more predictable and efficient. Think of it as slicing a pizza into equal parts—every node gets a fair share, reducing the chance of overburdening any single point. 🚀 For distributed databases and cache farms, this means better scalability and availability. Systems like Memcached, Amazon Dynamo, Cassandra, and Riak have all embraced consistent hashing to enhance performance. Curious about how this impacts key ordering or want to dive deeper into Riak? Stay tuned for more insights! #DataManagement #ConsistentHashing #Scalability #DistributedDatabases #TechInnovation 🤔 How do you ensure even load distribution in your systems? Share your strategies below! https://lnkd.in/gTD8Avht

Real-world data is complex, and traditional large language models fall short when using structured data like prices and product categories. With Superlinked and Redis, you can integrate structured and unstructured data for high-quality information retrieval. This means faster, more accurate recommendations that truly resonate with your customers' needs. Read more here. https://lnkd.in/erngxz-n

Found an interesting article on how Amazon achieves amazing durability in their S3 product. One of the techniques that I found interesting is the data replication and how they make use of Erasure Coding. Fun term that I had not heard of till I read this article. Per the article, erasure coding splits a data object into chunks called data shards. You're able to re-create the original data from the chunks. That and other topics are discussed in the article.

🚀 Redis and HyperLogLog: Counting Data Efficiently at Scale! 🚀 I recently came across a Redis command-PFADD and PFCOUNT-and it got me really curious! 😲 I was wondering how Redis could count huge amounts of data without using too much memory. That’s when I discovered HyperLogLog. 📊 Here’s why I find it so interesting: 1. Super Efficient: With just 12 KB of memory, Redis’ HyperLogLog can estimate the count of hundreds of millions, maybe even over a billion, unique elements. That’s amazing for such a small memory footprint! 🤯 2. Error Rate: The best part? The error rate is only 0.81%, which is a tiny trade-off for such efficient memory usage. 👌 I found a great blog from Facebook that explains the math behind HyperLogLog. It’s a really interesting read if you want to dive deeper into the theory (https://lnkd.in/ghqX6dUy)! 🧠 Why Redis chose HyperLogLog?? It allows counting large datasets without using much memory. Perfect for things like real-time analytics or tracking unique visitors across big data. 🔍 Want to explore more? Check out the hyperloglog.c file in the Redis codebase to see how it works. 😉 Redis keeps impressing me with how it solves performance challenges while staying fast and lightweight! 💥 #Redis #HyperLogLog #SoftwareEngineering #meta

Do you who have infrequently queried logs on Amazon S3 and are looking for a simple way to analyze them? Amazon OpenSearch Service has a new zero-ETL integration with Amazon S3 which will keep you in OpenSearch Service without jumping between tools or creating one-off ingestion pipelines. Huge thanks to the customers and multiple AWS teams who contributed in bringing the integration to fruition. #amazonopensearchservice #amazons3 #zeroetl