Platform · 2010–now · Cloud Scale
Elasticsearch
Full-text search and log analytics at scale. Powerful, hungry, and everywhere in observability stacks.
Elasticsearch stuck because grep does not scale and product search is revenue-critical. Cluster tuning is a career; that complexity is the moat. It fails when teams index everything without retention discipline.
Case studies
Context
Split everything, then pay for the glue
Hyperscale patterns escaped the companies that needed them. Containers unified packaging; Kubernetes became the cloud OS; microservices and NoSQL were sold as defaults. Mobile-first stuck because screens changed. Cargo-cult distributed systems stuck around as invoices. The durable move was packaging and ops maturity — not rewriting every app into a mesh.
Compare with
Related
Practice · 2009–2019
NoSQL for Everything
Schema-optional databases sold as a lifestyle. Right tool for some jobs; default for none.
$ Startups ditched Postgres for document stores "because scale," then rebuilt relational integrity in application code — and paid twice when joins came back as a product requirement.
Platform · 2006–2018
Hadoop Everywhere
MapReduce as lifestyle. The elephant in rooms that needed a spreadsheet.
$ Enterprises stood up Hadoop clusters for gigabytes of data that fit on one Postgres instance. Hadoop admins, ZooKeeper nightmares, and ETL rewrites burned millions before Spark and cloud warehouses retired the elephant.
Platform · 2009–now
MongoDB
Document DB that became a punchline, then a grown-up product. Survived by getting serious about transactions and ops.