Brian Sam-Bodden
Brian Sam-Bodden is a developer advocate at Redis Labs as well as an author, instructor, speaker, and hacker who has spent over twenty years crafting software systems. He holds dual bachelor’s degrees from Ohio Wesleyan University in computer science and physics. Brian is a frequent speaker at user groups and conferences nationally and abroad and is the author of “Beginning POJOs: Spring, Hibernate, JBoss and Tapestry”, co-author of the “Enterprise Java Development on a Budget: Leveraging Java Open Source Technologies” and a contributor to O'Reilly's “97 Things Every Project Manager Should Know”.
Presentations
Probabilistic Data Structures are the big data, cloud era, and streaming solution to efficiently storing counts. Especially when you are paying somebody else for disk space! Let me introduce you to Redis Bloom, a Redis Module that natively implements the most useful PDS.
The RedisBloom module provides four data structures: a scalable Bloom filter, a cuckoo filter, a count-min sketch, and a top-k.
Probabilistic Data Structures are the big data, cloud era, and streaming solution to efficiently storing counts. Especially when you are paying somebody else for disk space! Let me introduce you to Redis Bloom, a Redis Module that natively implements the most useful PDS.
The RedisBloom module provides four data structures: a scalable Bloom filter, a cuckoo filter, a count-min sketch, and a top-k.
In this session, we will explore the most common applications of these data structures in the context of a Spring RESTful Web Services application.
Brian will introduce you to the power of RediSearch to query structured and unstructured data in Redis and show how RediSearch helps to narrow the SQL to NoSQL gap by allowing common SQL patterns to be implemented in a key-value and document data store like Redis.
Do you automatically reach for a relational database for your application’s data needs? There is an unspoken impedance mismatch between expected rates of maturity between an application’s model and its data model. NoSQL/NewSQL and now “beyond SQL” solutions are often met with incredulity from the die-hard SQL crowd. Brian will introduce you to the power of RediSearch to query structured and unstructured data in Redis and show how RediSearch helps to narrow the SQL to NoSQL gap by allowing common SQL patterns to be implemented in a key-value and document data store like Redis.
Using the RediSearch module, you’ll learn:
How to query data in Redis with SQL-like flexibility
How to use boolean logic, full-text search, numeric ranges, geo radiuses, and more
How to create secondary indexes for your existing Redis data
How to build reporting and analytics queries using aggregations (COUNT, SUM, etc.)