Additional Funding For Elasticsearch To Help Company Complement Its RealTime Search And Analytics Stack
Elasticsearch – whose Elasticsearch, Logstash and Kibana products for discovering and extracting insights from structured and unstructured data were discussed earlier this year here – has raised $70 million in Series C financing from New Enterprise Associates (NEA). Benchmark Capital and Index Ventures also participated in the round. That brings the total to $104 million over the past 18 months.
“Nearly all companies, start-ups and Fortune 500 enterprises alike, need to be able to slice and dice rapidly expanding data volumes in real time,” says Steven Schuurman, co-founder and CEO. The funding, Schuurman says, will be applied to enhancing sales, marketing and support personnel and efforts, as well as investing in development to build more complementary products that work with the ELK stack.
“Ultimately, this round of funding will help us get to our goal, faster, of making the ELK stack the de facto platform for businesses to gain actionable insights from their data,” he says.
The company says it has seen 7 times growth in its customer base since 2012, including adding names such as Comcast, eBay, Facebook, Mayo Clinic, TomTom and Tinder, variously exploring use cases around search, analytics and logging. Facebook, Schuurman says “uses Elasticsearch as its search engine for a lot of its internal products (wiki, CRM, CMS), as well as its public community help site. They switched to Elasticsearch because it is a lot easier for their development team to implement, and scales better than other search solutions they used previously (their help site can get up to nearly 4,000 queries a minute).”
The Guardian, meanwhile, used Elasticsearch to build an in-house analytics systems that lets its editors, journalists, search optimization team, and developers see how users are interacting with the content on its website in real-time. Previously, it was experiencing a four-hour delay, which had an impact on maximizing its news coverage. “Thanks to Elasticsearch, they have data to support when stories should publish, what social channels they should be shared on, where on their website they should be featured,” he says.
On the logging front, Bloomberg uses the Elasticsearch ELK stack to centralize 1.5B log lines/day being generated across 3,000 production machines. “The ability to centralize them in one place to troubleshoot any potential issues saves their 2,000 programmers an immense amount of time,” Schuurman says.
This spring, ElasticSearch also partnered with MapR Technologies. to help customers add real-time search and analytics capabilities to their MapR Hadoop Distribution clusters. Since the relationship was formed, Schuurman says, “MapR is seeing a lot of demand from their existing customers to utilize Elasticsearch to perform real-time search and analytics across the tremendous amounts of information they store in MapR.”