Graph Databases: Key Thoughts from Online Chat

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It’s pretty interesting to see graph analytics gain traction in the work of big data. We’ve been focusing on graph databases to round out Hadoop® and Spark™ ecosystems and allow for more advanced analytics — and enable people to uncover never-before-seen patterns. (Tell me that’s not cool!) From solving real-world problems such as detecting cyberattacks and creating value from IoT sensor data to precisely identifying drug interactions faster than ever before, graph has become a powerhouse in looking at complex, irregular and very large datasets to identify patterns in near real-time. On March 16, we hosted an online chat titled “Graph: The Missing Link in Big Data Analytics” with industry experts from Deloitte and Mphasis. Sixty-one ... [ Read More ]

How CGE Achieves High Performance and Scalability

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In our graph series so far, we have explored what graph databases are and when they are valuable to use, as well as the Cray Graph Engine (“CGE”), a robust graph solution. For this last installment, we dive into how hardware affects the performance of a graph database. Cray’s main product line, the XC™ series, is mostly used for scientific computing. From the point of view of an applications programmer, there is an important difference between scientific computing and the kind of computations done on a graph database. Programmers call it spatial locality. In a nutshell, if a computation has a lot of spatial locality, when a computation has to fetch some value from memory, the next value it’s going to need is usually stored nearby in the ... [ Read More ]

Graph: The Missing Link in Big Data Analytics

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Graph analytics is gaining traction in the world of big data and IoT. From solving real-world problems such as detecting cyberattacks and creating value from IoT sensor data to precisely identifying drug interactions faster than ever before, graph has become a powerhouse in detecting never-before-seen connections and emergent patterns. It’s critical to understand how graph can be added to traditional Hadoop® and Spark™ workflows for successful results. Join us Wednesday, March 16, for a live online chat, “Graph: The Missing Link in Big Data Analytics,” to learn and discuss all things graph analytics. You can easily participate using a Twitter, LinkedIn or Facebook account. Hear from industry experts from Deloitte, Mphasis and Cray who ... [ Read More ]

How the Cray Graph Engine Manages Graph Databases

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In part 1 of this series, we looked at why graph databases are worth considering. In summary, graph databases can answer complex questions that relational databases can’t. In this installment, we will look at how the Cray graph engine represents graphs and what makes it a robust solution for graph queries. Conceptually, graphs are easy to think about: bubbles and arrows; or bubbles and lines. They come in two main flavors, directed and undirected. In directed graphs, the bubbles (vertices) are connected by an arrow (directed edge). They are used for relationships that are not symmetric. In the above example, “parent-of” isn’t a symmetric relationship. If John is a parent of Susan, Susan can’t be a parent of John. Undirected ... [ Read More ]

Graph Databases 101

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Let’s talk about graph databases. Some industry watchers claim that they are the fastest-growing type of database. If so, maybe it’s useful to know more about them. Starting with the basics: What is a graph database, and what is it useful for? Here’s the short answer. Graph databases store data in vertices and edges versus tables, as found in relational databases. They are the most efficient way of looking for relationships between data items, patterns of relationships or interactions between multiple data items, while traditional relational database shine at queries looking for information about some item, or sums or averages of many items of the same type of information. Now let’s review what a graph database isn’t. The standard ... [ Read More ]