The company has struggled to secure the cash that will allow it to continue operations. Despite investments of $280 million over the years, it has so far failed to convince investors that it deserves another round of funding. If the current trajectory holds, some 122 employees will end up terminated by June 14, according to the San Francisco Chronicle.
However, the company’s remaining executives are still trying to avoid the ultimate end. “MapR is actively pursuing a strategic transaction that might allow it to complete this transition and avoid closing its Santa Clara site,” the company wrote in a statement. “In fact, MapR received more than one letter of intent from interested parties, and today is engaging in the due diligence process in a transaction which, if consummated, may eliminate the need to close the Santa Clara site.”
If you’ve been in tech long enough, you’ll no doubt recall how, just a few years ago, Apache Hadoop was the focus of enormous hype. An open-source framework that allows firms to run so-called “Big Data” applications on large hardware clusters, Hadoop was supposed to allow companies using lower-cost (i.e., commodity hardware) to better wrangle projects that required huge datasets and towering computation challenges. By relying on a distributed framework, Hadoop also offered a certain degree of redundancy in data operations.
In addition to MapR, a number of tech firms (including, but not limited to, IBM, Pivotal, and Cloudera) all entered the space with their own specialized Hadoop distributions, which all promised to execute faster than the “vanilla” open-source version of Hadoop. Very quickly, “Hadoop” became one of those inescapable buzz-words, especially if you worked in any technology segment that leveraged huge datasets; even executives who could barely operate a keyboard seemed to chant it over and over again.
But Hadoop (and its various distributions) is complex to set up and use, which may explain its relatively slow adoption rate (despite the hype). By 2017, a report from research firm Gartner stated that only 14 percent of enterprises were using the technology. Last year, a report from McKinsey Analytics suggested that Hadoop would continue to grow, but that its share of the overall “Big Data” market would remain pretty tiny.
There is a silver lining here: Complex, niche technologies require highly specialized technology professionals to operate—and those specialists can make quite a bit of money as a result. When Dice crunched the numbers last year, it found that jobs that leverage Hadoop easily earn six-figure annual salaries.
But it’s more difficult for any large company—much less a “unicorn” once valued at roughly $1 billion—to sustain revenues on a cluster (pun intended) of niche technologies, especially when there are lots of competitors in the space. And to be fair, concerns over MapR’s potential profitability have drifted through the tech ecosystem for years. Now a reckoning is at hand.