𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗖𝗢𝗠𝗣𝗔𝗡𝗜𝗘𝗦 𝘄𝗶𝗹𝗹 𝗱𝗶𝗲 Sooner rather than later Why

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Post Link: https://www.linkedin.com/posts/scgupt... • 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗖𝗢𝗠𝗣𝗔𝗡𝗜𝗘𝗦 𝘄𝗶𝗹𝗹 𝗱𝗶𝗲! • Sooner rather than later. Why? • Because most SQL and NoSQL databases will implement Vector “Store”. Many popular databases are already supporting vector similarity search. • Some examples of popular DBs adding vector index/search: • 🔹𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟: https://lnkd.in/gUz8BwGS • 🔹MongoDB: https://lnkd.in/gk5WaMVr • 🔹Redis: https://lnkd.in/guda3Y8r • 🔹𝗘𝗹𝗮𝘀𝘁𝗶𝗰𝗦𝗲𝗮𝗿𝗰𝗵: https://lnkd.in/gFaSrvrm • 🔹Apache Cassandra: https://lnkd.in/gF5RVcAb • 🔹Neo4j: https://lnkd.in/gz3befpa • 🔹Databricks: https://lnkd.in/g_YSr_-M • 🔹Snowflake: https://lnkd.in/gQa3cgCX • Apart from these, cloud vendors are adding vector search support to their proprietary databases and services: • 🔸Google Cloud: AlloyDB (PostgreSQL-compatible), Firebase, BigQuery, Spanner, Vertex AI • 🔸Microsoft Azure: SQL DB, Cosmos DB, AI Search • 🔸Amazon Web Services (AWS): Neptune, DocumentDB, MemoryDB • 𝗪𝗵𝘆 𝗱𝗼 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗕 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗵𝗮𝘃𝗲 𝗻𝗼 𝗱𝗲𝗳𝗲𝗻𝗱𝗮𝗯𝗹𝗲 𝗺𝗼𝗮𝘁? • There are technical reasons and business reasons. • 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗥𝗲𝗮𝘀𝗼𝗻𝘀: • Vector Search is just an index! It is a derivative of the original data. Most DBs/SaaS will add/build it (again, sooner rather than later), along with convenient similarity comparison functions. • 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗥𝗲𝗮𝘀𝗼𝗻𝘀: • You will not be dumping any database that you already use. Then, why would you add one more DB if the DBs you are using already support Vector Search? The extra vendor, training, and engineering cost has to offer a compelling need and clear RoI. • That’s why I think enterprises are unlikely to give truckloads of money to Pinecone, Chroma, Qdrant, Milvus, Weaviate, etc. • What do you think? Share your take in the comments. • 𝗨𝗣𝗗𝗔𝗧𝗘𝗦 • 1. Capitalized the word COMPANIES in the first line. • Ignoring that word leads to a strawman. To refute the hypothesis, please explain why the first-mover advantage of stand-alone vector DB businesses will lead to a sustained tech or market share moat. • 2. Discovered that I am not the first to have this idea. I should have thought and googled. Thanks Prithivi for the pointers. • The first comment on Vector DB being product vs. feature is this tweet by David Fowler: https://lnkd.in/g2BuRq8k • The first commentary on Vector DB business is this blog post by Yingjun Wu: https://lnkd.in/gb_ktDsz • Both in May/June 2023. If you know of any earlier references, please let me know. • __________ • 👉 Subscribe to hashtag#ML4Devs newsletter: ml4devs.com/newsletter • 🚀 Contact me for 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆, 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀, and 𝗠𝗟 consulting. • 👋 Follow me Satish Chandra Gupta and ring 🔔 for notifications. • hashtag#BigData hashtag#DataEngineering hashtag#DataAnalytics hashtag#Data hashtag#Analytics hashtag#DataScience hashtag#MachineLearning hashtag#ML hashtag#AI hashtag#MLOps hashtag#Cloud hashtag#CloudComputing hashtag#Microservices hashtag#SQL hashtag#AWS hashtag#Azure hashtag#GCP hashtag#VectorDatabase hashtag#LLM hashtag#GPT hashtag#GenerativeAI hashtag#VectorDB

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