You already know more about Elasticsearch than you think. If you’ve ever used a spreadsheet, you’ve seen the basics: rows as documents, columns as fields, headers as rules. Elasticsearch takes that familiar model and supercharges it—storing data as JSON, splitting it into shards for scale, and applying mappings so it knows how to search every piece. In this week’s episode, Michael Heldebrant shows how Elasticsearch turns simple building blocks into fast, distributed search. Watch now: https://go

tech + code = technical coder on Instagram: "Ever wondered how apps like Uber, Netflix, and Wikipedia deliver lightning-fast search results? Let’s break it down! ⚡ ⸻ 1️⃣ Distributed Architecture (Handles Massive Data) 🌍 ✅ Tech: Cluster of Nodes ✅ How? • Divides data across multiple nodes (shards) for parallel processing • Ensures high availability with data replication • Scales horizontally as data grows 🔹 Example: Uber uses Elasticsearch to match drivers & riders instantly ⸻ 2️⃣ Inverted Inde