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Building a Startup
 with NoSQL

Building a Startup
 with NoSQL

These slides are from a talk I gave at NoSQL Matters 2014 in Barcelona. It's not very technical per-se and more focussed on what kind of decision making is important to choose NoSQL technology (especially) when you are a startup.


Sebastian Cohnen

December 01, 2014


  1. Building a Startup
 with NoSQL Sebastian Cohnen, @tisba, @StormForgerApp

    NoSQL Matters Barcelona 2014
  2. About me • Sebastian Cohnen, M. Sc. • Developer &

    Founder from Cologne, Germany • (Web-) Architectures, Performance & Scalability • Founder of
 (services around load testing HTTP-based systems)
  3. Startups • agile & open minded • small teams, pragmatic

    • solve new problems
  4. NoSQL? "A NoSQL (often interpreted as Not Only SQL) database

    provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases." "Motivations for this approach include simplicity of design, horizontal scaling and finer control over availability."
  5. NoSQL • Ease of use (development & operations) • Very

    special problem
  6. StormForger

  7. StormForger • load and performance testing of HTTP APIs •

    SaaS product • plan, configure and execute tests • analyze and compare results
  8. We are a startup!

  9. Our Needs • handle highly structured, complex data • ingest,

    process and query time series data • caching of structured data • centralized logging and log analysis
  10. How to find the
 perfect solution?

  11. There isn't one :-/

  12. Polyglot Persistence (NoSQL)
 Technology Problem

  13. …but be careful • What are the downsides and tradeoffs?

    • Availability of tooling? • How mature is the technology? • What about (community or commercial) support?
  14. Back to StormForger's needs…

  15. • SQL-ish, declarative query language • easy to get started

    with • powerful background/stream processing Time Series Data
  16. Caching • very, very fast • primitive functions to work

    with data structures • awesome community
  17. • ELK (Elastic Search + Logstash + Kibana) • Centralized,

    aggregated logging • Great analysis and search features Logging
  18. Structured Data • Not really tackled yet • For now:

    serialize and store as TEXT in RDMS
  19. Structured Data (2) • we already evaluated some solutions •

    great query language (AQL) • support for JOINS & Graphs
  20. Conclusion • focus on ease of use aspects • be

    pragmatic and think agile • pick the right tool for the job
  21. And if you think you found the right solution… …there

    are always alternatives…
  22. What about using a SQL DB for NoSQL?

  23. PostgreSQL • hstore: Key/Value • json & jsonb: JSON data

    • just use your existing tools • query via SQL, indexable, …
  24. None
  25. Thanks! Sebastian Cohnen (@tisba) Questions?