Database Camp

An Exploration of Open Source Database Technologies

Sun. Nov. 19 from 09:00 am to 06:00 pm

Database Camp is a grassroots, community-run open-source conference focused on open source database technologies.

Our content is geared towards developers seeking to learn more about enterprise open source best practices, insights and emerging technologies.

Sponsors

If you or an organization you know may be interested in sponsorsing Database Camp please see out prospectus Prospectus and reach out to us by email

Presenters

Database Camp features a dynamic mix of speakers from across the open source enterprise database community.

Tague Griffith

Head of Developer Advocacy (Redis Labs)

Jason Plurad

Open Source Developer (IBM, Apache TinkerPop, JanusGraph)

Jeremy Mikola

Software Engineer (MongoDB)

Marty Schoch

Principal Engineer (Couchbase)

Ryan Boyd

Engineer and Director of Developer Relations (Neo4j)

Yi-Hong Wang

Software Developer (IBM)

Schedule

We will be adding schedule details in the next few days which specify the detailed order/timing of presentation on Sun. Nov. 19, between 9am - 6pm.

Presentations

Database Camp includes an interesting mix of presentation on best practices, emerging techniques, recent research and case studies regarding open source databases & datastores.

Building a High-Performance Key/Value Store in Go

In this talk we explore the internals of a high-performance key/value store written in Go. The audience will learn the basic design used to store and retrieve data, as well the techniques used to achieve high performance.

Marty Schoch

Principal Engineer (Couchbase)

DOs and DON'Ts of MongoDB

There are no "best practices" without "worst practices". This presentation will look at common use cases for MongoDB, covering topics such as schema design, querying, and methods of data aggregation. Tips and caveats will be sprinkled throughout as we discuss DOs and DON'Ts applicable to applications and drivers right down to operations and deployment of the database itself.

Jeremy Mikola

Software Engineer (MongoDB)

Performance study for JanusGraph storage backends, Cassandra, HBase, and Scylla

NoSQL databases are built to provide high performance and scalability. While JanusGraph works with a number of storage backends, we set our goals to understand the performance behaviors of Cassandra, HBase, and Scylla and identify the best performing database for high volume graph workloads. In this talk, we will share the performance results and our lessons learned from operating these open-source NoSQL databases.

Yi-Hong Wang

Software Developer (IBM)

Graph Computing with Apache TinkerPop

Graph databases are emerging as a better way to model highly connected data. Learn the story of how Apache TinkerPop, a graph computing framework, evolved as an open source project to become a standard for graph databases from a variety of vendors. Using open airline route data, we will demonstrate how to create a graph model and query it using Gremlin, the graph traversal language. We will discuss several TinkerPop features that enable developers to build graph-based applications quickly.

Jason Plurad

Open Source Developer (IBM, Apache TinkerPop, JanusGraph)

Graph Algorithms on ACID

When most data scientists think of Graph Algorithms, they think of batch analytical processes running computation on a graph in R/iGraph, Gephi, etc. These tools certainly provide great insight into your data, but they don't provide the ability for applications to make real-time decisions based on these algorithms. For that, you need to store your data in an OLTP. Neo4j is a graph database which combines the ACID data guarantees you expect from SQL, with the schema flexibility you expect from NoSQL, and the performance for traversing connected data that you expect from a native graph database. It excels at querying your data using graph patterns. More recently, we've added the ability to run graph analytics on top of the database to make real-time decisions. Why settle for your classic data analysis tools and making decisions in offline processes? This session will be a fast-paced intro to the power of graphs for transactions, storage, traversal, and analysis: Graph Algorithms on ACID. Oh, and if you have your data in Spark, I'll also give a brief preview of Cypher on Apache Spark!

Ryan Boyd

Engineer and Director of Developer Relations (Neo4j)

Serving Predictive Models with Redis

One of the difficulties in deploying a machine learning strategy is ensuring the performance of real-time decisions made using predictive models. The demands for reliability and speed to support real-time predictive systems have increased. Those challenges are made more difficult by the increasing size and complexity of models and algorithms used to improve the accuracy of decisions. There are many different systems that are available to build the learning part of a machine learning pipeline, but may of these systems leave the decision making system as an exercise for the reader. Building customer services to support real-time decision making can be difficult to do reliability and with scale. Instead of building a custom system, we will look at how Redis 4.0 and the Redis-ML module can be used out of the box to provide a real-time decision making service. Starting with a machine learning pipeline implemented using scikit-learn, we will walk through the types of predictive models (decision trees, regressions, etc.) supported by Redis-ML, and the code to load models into Redis, and finally how to implement real-time decision making.

Tague Griffith

Head of Developer Advocacy (Redis Labs)

Location

The venue for Databse Camp 2017 is Convene's midtown NYC location at 730 3rd Ave, where it will be hosted along with other Open Camps events. We'll be posting further venue details here as the event approaches, including access and check-in procedures.

Team

Database Camp is organized by a volunteer team from the open source database community.