In this first edition of a new weekly post, we will revisit the past week to make sure you are completely up-to-date in the world of NoSQL.
New Releases This Week:
We’re pleased to announce the availability of CDH4.1. We’ve seen excellent adoption of CDH4.0 since it went GA at the end of June and a number of exciting use cases have moved to production. CDH4.1 is an update that has a number of fixes but also a number of useful enhancements.
Available immediately, Neo4j 1.8 offers a delightful experience for reading and writing graph data with the simple expressiveness of the Cypher language. Whether you're just discovering the social power of Facebook's Open Graph or are building your own Knowledge Graph for Master Data Management, speaking in graph is easy with Cypher. It is the key to making sense of data with Neo4j.
This Week's Top 10 NoSQL Links:
Here are some of the amazing geo-social visualizations you get when you compare two projects.
Since with graphs we can represent real-life problems it’s almost clear why we would need an efficient algorithm that calculates the shortest path between two vertices.
A quick tip for those of you who are using Cypher with Spring Data Neo4j (SDN): If you're using the @MapResult way in your Neo4j repositories, be careful of what you use in the corresponding @ResultColumn annotations.
Next up, after GitHub and Bootstrap, in the session I presented last month during a company trip, is MongoDB.
Last month I presented a webinar introducing the basics of application development using VoltDB. We had a lot of great questions and I thought it would be handy to write them up and share the answers with everyone.
Probably the closest database object-relationally to Postgres is Informix. Informix in fact got its object-relational approach with the purchase of Illustra, a Postgres fork.
I was working on a section on the gooey innards of journaling for The Definitive Guide, but then I realized it’s an implementation detail that most people won’t care about. However, I had all of these nice diagrams just laying around.
How do you create the best index for a complex MongoDB query? I'll present a method specifically for queries that combine equality tests, sorts, and range filters, and demonstrate the best order for fields in a compound index.
New NoSQL Books This Week:
By Henry H. Liu
"This textbook mainly focuses on teaching Hadoop MapReduce programming in a scientific, objective, quantitative approach. Rather than heavily relying on subjective, verbose (and sometimes even pompous) textual descriptions with sparse code snippets, this textbook uses Hadoop Java APIs, Hadoop configuration parameters, complete MapReduce programs and their execution logs and outputs to demonstrate how Hadoop MapReduce framework works and how to write MapReduce programs. Specifically, this text covers the following subjects:
- Introduction to Hadoop
- Setting up a Linux Hadoop Cluster
- The Hadoop Distributed FileSystem
- MapReduce Job Orchestration and Workflows
- Basic MapReduce Programming
- Advanced MapReduce Programming
- Hadoop Streaming
- Hadoop Administration"
by Alex Holmes
"Hadoop in Practice collects 85 Hadoop examples and presents them in a problem/solution format. Each technique addresses a specific task you'll face, like querying big data using Pig or writing a log file loader. You'll explore each problem step by step, learning both how to build and deploy that specific solution along with the thinking that went into its design. As you work through the tasks, you'll find yourself growing more comfortable with Hadoop and at home in the world of big data."
See you next week, same NoSQL time, same NoSQL place!