![]() ![]() “The key challenge of the 21st century is to get real value out of data. Enterprises worldwide are looking for easy ways to improve business insights, use new analytics methods, and advance their AI usages,” said Claudius Weinberger, CEO and co-founder of ArangoDB. “ArangoDB’s goal from day one is to make it extremely easy to handle data of any kind. Our Series B funding will allow us to accelerate our mission to make it even easier to generate real value from data, as well as enter new markets.” Graph and beyond enable everyone to combine graphs, structured, unstructured, and more kinds of data in one solution, at enterprise-scale with advanced graph analytics. Nosql arangodb 27.8m capitalsawersventurebeat series# Since its founding, ArangoDB has natively supported graph in combination with additional data formats, including JSON documents, key-value, and full-text search. Over time, graph technologies have continued to become increasingly important and adopted by businesses of all sizes to extract value from data, despite most pure graph databases not being able to scale to support large volumes of data from various sources. Nosql arangodb 27.8m capitalsawersventurebeat series#.I'm assuming that traversing down to data1 of event1, back up to profile, and back down event2 data1 and so on would be very inefficient. Would this query be faster simply querying on a document collection with indices rather than via graph traversal? The document collection structure could put a hash index on the dataAttr and skiplist on the event (they will be sequentially ordered with string sorting). Is this probably the best way to structure the data for a query like this? Performance will be critical as I potentially will be loading 20 charts on a page that each are fed by this query. This would mean creating an additional graph that looks something like this: data1 (of event1) => data1 (of event2) => data1 (of event3) => data1 (of event4)ĭata2 (of event1) => data2 (of event2) => data2 (of event3) => data2 (of event4)Įach dataAttr is connected to its cousin in the previous event, thus after traversing to the most recent event in the first graph, the second graph would be used to traverse n-layers to past events (practically 10-20). I'm wondering if this is also a better approach in ArangoDB. ![]() When investigating this problem in Neo4J, they recommended directly connecting sequential events to each other. I am likely to run this query for every dataAttr in a given report, to effectively create a time-series result set for each dataAttr on a particular profile for the last 10-20 events. ![]() An analogous tabular structure of the result set I would like looks like this: event dataAttr value The chart I would like would effectively present data1 sequentially, by associated event, sorted by an attribute of the event. The existing graph structure looks like this: profile => event1 => reportA => data1 The report is simply a group of data points from a given event. Between the profile node and the data node, there is a report node and an event node. The data is associated with a particular profile, but one collection is used for all the data for all profiles. I have some time-series data (roughly on the order of 1-5 points per day) I need to be able to quickly access in a webapp using ArangoDB. ![]()
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