|Welcome to issue 233 of NoSQL Weekly. We have a packed issue this week. Enjoy it!
MarkLogic raises $102 million in new funding
MarkLogic Corporation announced that it has closed a $102M round of funding to fuel growth to satisfy global market demand for the MarkLogic Enterprise NoSQL database platform. The funds will be used to accelerate the company's global market expansion across Europe, Japan and Asia Pacific, expand its thriving partner ecosystem, and to support continued innovation of the industry's only new-generation database platform purpose-built for the enterprise.
Articles, Tutorials and Talks
Using Neo4j Spatial with Mapbox / Leaflet.js to search for businesses by location
A common use-case for database queries is to search for things that are close to other things or within some specified geospatial boundary. Geospatial indexes and queries are offered by NoSQL databases, such as MongoDB and relational databases such as PostgreSQL. But what about graph databases? In this article, I show how to create a web application to search within a user-defined boundary powered by the Neo4j graph database.
Beating the Traffic Jam Using Embedded Devices, OPC-UA, Akka and NoSQL
The Norwegian Public Roads Administration is building a new infrastructure for road traffic measurements, and the system will provide near real-time information as publicly available open data. Kristoffer Dyrkorn presents the experience gained underway, such as architectural choices, tradeoffs and mistakes - and knowledge from integrating an unusual combination of software, protocols and hardware.
Thinking in Documents: Part 2
In part 1 of this 2-part blog series, we introduced the concept of documents and some of the advantages they provide. In this part, we will start to put documents into action by discussing schema design. We will cover how to manage related data with embedding and referencing, we'll touch on indexing and the MongoDB transaction model.
Getting Started with the Node.js LoopBack Connector for ArangoDB
We've already covered Couchbase Server using loopback-connector-couchbase and RethinkDB with loopback-connector-rethinkdb. Today, we'll be discussing connecting LoopBack and ArangoDB using another community contributed connector - loopback-connector-arango.
Using Python and Neo4j for Data Analytics
In this talk, you'll learn how to use Python to collect data from Twitter's API, Neo4j to easily and reliably store this highly-connected data, and Python again for quick analysis and visualization.
Connecting and Working with CouchDB with Node & Express
In this article we will look at how to connect to your CouchDB instance and then we will go all the way to building models which implement your business logic.
How to Load Test MongoDB with JMeter
When performance testing you usually need to check the entire application to measure the end-user experience. But it's sometimes easier to test each database component separately if you want to check the response times for certain queries, the efficiency of horizontal scaling, replications, failovers and resilience, and to ensure the database is properly tuned you won't hit a bottleneck or be denied service. This post is going to guide you through MongoDB load testing with Apache JMeter, highlight frequently faced problems along with their workarounds, and explain and demonstrate some of MongoDB's most common tasks.
What Finance can learn from Dating Sites
In this presentation, you will see how in many ways dating sites are getting better performance and more value out of their data than financial institutions by using Neo4j.
How-To Backup Cassandra Hosted on the AWS Cloud - Part 1
In this two part series, we will introduce Cassandra's architecture to you and describe it's inherent backup mechanism for a single and multi node Cassandra setup on AWS cloud. The part one shows you a quick way to backup and restore a Cassandra database hosted on AWS using inherent Cassandra mechanisms.
Retail Reference Architecture Part 2: Approaches to Inventory Optimization
In part one of our series on retail reference architecture we looked at some best practices for how a high-volume retailer might use MongoDB as the persistence layer for a large product catalog. This involved index, schema, and query optimization to ensure our catalog could support features like search, per-store pricing and browsing with faceted search in a highly performant manner. Over the next two posts we will be looking at approaches to similar types of optimization, but applied to an entirely different aspect of retail business, inventory.
Document Classification with Neo4j
In this talk, Neo4j Developer Evangelist Kenny Bastani will discuss using Neo4j to perform document classification. He will demonstrate how to build a scalable architecture for classifying natural language text using a graph-based algorithm called Hierarchical Pattern Recognition. This approach encompasses a set of techniques familiar to Deep Learning practitioners. Kenny will then introduce a new Neo4j unmanaged extension that can train natural language models on Wikipedia articles to determine which articles are most related based on a vector of shared features.
Learning Node.js: Building A Simple API Powered By MongoDB
Performance doubling with message coalescing
Apache HBase Performance Tuning
Clustering customers for machine learning with Hadoop and Mahout
CouchDB Best Practices
Hadoop Summit Europe 2015 Videos
HBase Design Patterns
If you are an intermediate NoSQL developer or have a few big data projects under your belt, you will learn how to increase your chances of a successful and useful NoSQL application by mastering the design patterns described in the book. The HBase design patterns apply equally well to Cassandra, MongoDB, and so on.
Interesting Projects, Tools and Libraries
Not Just A Notepad! (golang + mongodb)
A blazing-fast datastore and querying engine for Go built on Redis.
Fauxton is a modular CouchDB dashboard and Futon replacement.
Allows developers to quickly create RESTful APIs using a mix of MongoDB, Express and NodeJS.
PouchDB plugin for running migrations.
This library provides atomic counters using Amazon DynamoDB. Each increment request increments a counter value that is stored in a DynamoDB table (named "AtomicCounters" by default). Multiple increment requests can be sent simultaneously but each request will receive a unique value, therefore this library can be use to generate auto-increment ids.
Reactive type-safe Scala DSL for Neo4j.
A simple implimentation of a queue on top of riak.
Upcoming Events and Webinars
Couchbase Connect 2015
Join over 1,000 technology leaders, architects, developers and administrators for three full days of web, mobile and IoT app development. Get a deep dive into Couchbase's cutting edge innovation and the big data architectures of some of the world's leading brands.
Webinar: When to Use MongoDB
In this webinar you will learn when to use MongoDB and how to evaluate if MongoDB is a fit for your project. You will see how MongoDB's flexible document model is solving business problems in ways that were not previously possible, and how MongoDB's built-in features allow running at scale.
Webinar: Migrating from MySQL to Neo4j
Join us for this webinar as Nicole White, Data Scientist for Neo4j, demonstrates how simple it is to migrate from MySQL to Neo4j. In this webinar, you will learn: Building the data model and data relationships, Exporting from MySQL and importing into Neo4j with ease, Transforming SQL queries into Cypher for fast query performance.
Azure DocumentDB: Massively-Scalable, Multi-Tenant Document Database Service - New York, NY
In this session, we will discuss the design of DocumentDB storage system and why this makes developers' lives better.
How Medium Uses Neo4j - San Francisco, CA
Medium has built their own system of managing their data, using Go and Neo4j. They use Neo4j for their "Social graph". Join us as Nat Felsen, Engineer at Medium talks a bit about this system, lessons learned from their deployment, and why they chose Neo4j/Go.
Storm: A Big Data tool for your Small Data problems - New York, NY
Apache Storm is usually discussed in the context of solving "big data" problems, overlooking its characteristics that make it applicable to more mundane "small data" problems. This talk focuses on how Storm can help you write code that is: Easier to understand, Easier to monitor, Simpler to scale and Fault tolerant.
Cleveland Big Data and Hadoop Meetup May 2015 - Cleveland, OH
There will be following talks
- IPython Notebook as a Unified Data Science Interface for Hadoop
- Hadoop Security
- Modern Machine Learning: Free Form Modeling for Real World Problems
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