~/talk

Make sense of your (big) data using Elasticsearch

Talkat PyData Berlin 2014 · 2014-07-25..07-27· [databases]
slides:none on file

Shows concrete claims and demos: Elasticsearch indexes real JSON documents, runs full‑text and structured queries, faceting and nested/parent‑child queries, and drives visualizations with Kibana and ingestion via Logstash while Python client code demos the flows. Aggregations execute in near real time (example query across the Stack Overflow dump finishes in 37 milliseconds), bulk ingest scales to enterprise loads (customer record reported 8 GB/s), and storage options trade memory fielddata for on‑disk doc_values to handle terabyte-sized datasets.

AI-written summary · about

  1. 0:00Introduction
  2. 1:14Elasticsearch core features
  3. 10:49Filtering in Elasticsearch
  4. 20:16Exploring data with aggregations
  5. 26:42Real-world demos
  6. 31:33Live coding Stack Overflow data
  7. 40:26Q&A
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