SFRails - LT | NoSQL in SQL: Have It All | LT | Working w/ Time Series Data

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Agenda

6:30 Food/Beer/Networking
7:00 Intros/Sharing of Tips and Tools

7:15 Talks

• Lightning Talk : Alyssa Ravasio
• Talk 1: NoSQL in SQL: Getting to Have It All - Andrew Geweke
• Lightning Talk : Lucas Dohmen
• Talk 2: Working with Time Series Data - Paul Mestemaker

8:30 End

Lightning Talk : Hacking Webforms with Phantom.JS!

About the Speaker

Alyssa Ravasio is the founder and CEO of Hipcamp.com, a startup that helps you discover and book epic campsites. She is a graduate of Dev Bootcamp and also has a degree from UCLA in Digital Democracy. Her deepest passion is helping shape how the internet changes our humanity and our planet.

Talk 1: NoSQL in SQL: Getting to Have It All

The last few years have seen an explosion of interest in NoSQL data-storage layers, and then some retrenchment as the limitations of these systems became increasingly apparent. (It turns out they’re not magic, after all!) Today we seem faced with a choice. On one hand, we can reach for some of the potential “big wins” of NoSQL systems, but many of them are still relatively immature — at least when compared to the RDBMS — and the things we give up (transactionality, durability, manageability) we often discover to be very painful losses. On the other hand, we can reach for the security of a traditional RDBMS; we get incredibly well-understood, robust, durable, manageable systems…but we often sacrifice a lot of potential future growth.

We’ll show you how to have some of your cake and eat some of it, too. We’ll introduce a combination of architectural patterns and software (including two brand-new RubyGems) that let you build schema-free, scalable data storage inside a traditional RDBMS, and show you how just changing your deployment options lets you scale the same codebase and database design from a site that’s just barely getting started to one under extremely high load. (These are the same techniques used by sites like Scribd, which happily serve dozens of pages per second from a RDBMS.) These techniques also open up an easy migration path for you to move appropriate data sets to a NoSQL system when desired, allowing you to form a “blended” system that gives you the best of both worlds.

About the Speaker

Andrew Geweke has been building Rails code for over eight years, and has been a professional software engineer for 16. In the last few years he’s managed teams of engineers and technology platforms at Scribd and Couchsurfing, both very large Rails sites. Recently, he’s been working more on evangelizing the technology platforms he’s developed and communicating more to the broader Rails community.

Lightning Talk : Guacamole for ArangoDB

Guacamole is a Ruby ODM for the open source NoSQL database ArangoDB that uses the Data Mapper pattern.

About the Speaker

Lucas Dohmen is a member of the core team of the NoSQL database project ArangoDB where he works on the graph functionality, tools for the Ruby community to use the database, and Foxx. He contributes to various open source projects like hacken.in (an event calender for nerds in Cologne, Berlin and Munich) and projects related to ArangoDB.

Talk 2: Working with Time Series Data

Over the last few years, there has been incredible growth in the amount of sensors producing data as well as a sharp increase in frequency in which each sensor emits data. Temperature & pressure gauges, heart beat monitors, GPS devices, Fitbits, analytics platforms all produce time series data -- e.g. key: #{timestamp}, value: #{value}. The sheer amount of data generated can be costly to store in its most granular form. So businesses often choose to aggregate and discard the raw data in order to reduce costs or to prevent scaling issues.

The problem with that is that aggregating data is inherently lossy, so you could lose valuable insights. If you want to keep the raw data, you could store it in something simple like flat files, but then it is difficult to analyze. If you store it in a relational database, you'll quickly run into performance and scaling issues. One of the most common problems is that the more data you capture, the longer it will take to read that data out.

In this talk, Paul will give an overview of how to deal with loading, reading, and graphing large time series datasets using Ruby, JavaScript, D3.js, and TempoDB (a purpose-built time series database).

About the Speaker

Paul Mestemaker is the founder of CleverPoint LLC, a technology consulting firm based in San Francisco. He and his team help companies and nonprofits accomplish their goals through clever applications of technology. Previously, Paul was a PM at Microsoft, co-authored a book on SQL Server Performance Tuning, and helped a couple of startups build their teams and launch their products.

About the Sponsor/Host

The Climate Corporation helps people and businesses manage and adapt to weather events and climate change. The company’s unique technology platform enables the real-time pricing and purchasing of customizable weather risk management products using proprietary global weather simulation modeling and local weather monitoring systems.

Other Sponsors

Iron.io is the maker of IronMQ, a scalable cloud-based message queue, and IronWorker, an elastic task queue/worker service. Iron.io's services are designed for doing things asynchronously and operating at scale. (@getiron (http://twitter.com/getiron))