Software developer Cloudera's ($NYSE:CLDR) first full quarter post-Hortonworks deal went poorly and now the stock is down 40%.
“We conclude based on our field checks that Cloudera hasn’t managed the architectural transition well from on-premise Hadoop-focused data analytics projects to cloud/AWS-hosted workloads,” Deutsche Bank ($NYSE:DB) analysts wrote Thursday morning, as they also sliced their target.
CEO Thomas J. Reilly stepped down after the earnings miss; Cloudera shares have fallen from a 2018 peak of $21.58 per share to about $5.20 today, and data highlights recent challenges some one-time Hadoop heroes are facing as they struggle to justify jumbo valuations.
Not Growing Anymore?
Our data tracked separate LinkedIn ($NASDAQ:MSFT) employee headcounts at Cloudera and at Hortonworks, and merged their numbers to find a slight drop-off in job postings beginning this year, right when the companies completed their merger.
Prior to the deal at the end of 2018, the two companies combined had 3,440 people identifying as their employees. But, by the beginning of June, they only had 3,400 combined employees.
The decline isn't so precipitous that it warrants concern ordinarily, even after an M&A transaction. However, both companies were on a solid, upward job posting trajectory before the deal completed and the data spells it out.
Our second chart is job listings; after hitting an all-time recent high, the merged entities began posting fewer jobs online.
Open Source - Shutting Down?
When Hadoop startups started gaining big funding rounds five and six years ago, it was with the expectations open source data platforms would help them generate big valuations through corporate clients' outsized data sets. But things don't always go that way.
Another example of a boldface-Hadoop-name recently gone bust is MapR. The startup, which gathered nearly $300 million in private funding, saw job postings plummet from 54 down to 7 over a space of barely more than a month.
Thinknum tracks companies using various metrics - job postings, social and web traffic, product sales and app ratings - to create data sets that measure factors like hiring, revenue and foot traffic. Data sets may not be fully comprehensive (they only account for what is available on the web), but they can be used to gauge performance factors like staffing and sales.