Cloudera has been the talk of the town since a long time. Though, it was regarded as one of the strongest big data analytics solutions earlier. But, in the present days, there are a few problems being faced by Cloudera. Though, it is not like Cloudera will be discontinued soon or anything, but yes, there have been some discussions around Cloudera. Though, not most of them are concrete discussions, but many of them have led to a rise in the rumors as well. But, at the end of the day, if Cloudera has to regain the top position in the market, the company would have to upgrade and evolve with the period of time.
Though, initially, Cloudera was quite popular as it has a wonderful interface for interacting with different solutions like Kafka clusters and Hadoop. Though, at times Cloudera may not be considered as the perfect solution for using with Hadoop. At the same time, Cloudera is considered a little bit costlier than some of the other big data analytics solutions as well.
Cloudera Big Data Solutions conveys the cutting edge platform for machine learning and advanced data analytics based on the most recent open source innovations. However, Cloudera has a few of limitations and shortcomings.
Cloudera is not able to generate enough revenue?
In the recent times, Cloudera has not been able to generate enough revenue. And, when it comes to the failure of generating revenue, there can be various reasons behind that. It has been disappointing that Cloudera has not been able to get enough and expected returns for the stockholders. And, this is surely one of the scariest factors. At the end of the day, if the stakeholders are not able to get enough returns, the investment in Cloudera won’t be very prosperous for them. Hence, there can be some of the investors who might consider investing in other products like MapR. The stakeholders won’t really look under the hood but they would rather prefer something that gives them more revenue.
Cloudera helps to solve their most challenging business issues by productively storing, preparing, analyzing, and capturing tremendous measures of data.
Cloudera’s Heavy Costs
The core purpose of every marketing and sales team is to get more venues. If through the initiatives, the company is able to generate more revenue and get more out of the existing customers, then only the marketing and sales campaigns are touted to be successful. Otherwise, of the company is not able to convert heavy marketing spend into revenue, then the cost or investment in marketing is of no use.
In the recent times, it is observed that Cloudera has been facing a few of the challenges related to costs. They have sliced their full-year guidance, also, they haven’t seen any growth. And, at the same time, there are issues related to bloated cost structure. And, this high cost as now become a major concern for the overall financial status of Cloudera.
Cloudera has to be cost-effective if it wants to see some growth and success in the near future. Therefore, the company will have to shake up a bit and try out new things. First of all, they will have to cut their costs in order grow more. Rather, the investment should be done in a streamlined manner. As, at the end of the day, if the investment is done in a planned way, then in that case, the finances won’t have a big impact. Also, the company will have more chances to climb up the ladder of success.
Though, Cloudera hasn’t faced too many challenges in the recent time, but the cost and the investment can be a bit of an issue. And, if the company overcomes this big challenge, then there is no doubt about the fact that Cloudera will have huge chances to grow. One key methodology Cloudera takes with Data Science is that it aims to empower the researchers to work in a genuinely open space that can extend its compass to utilize.
Cloudera empowers to quickest way to blend omic data with clinical/phenotype information from any innovation in the market. Many associations have already chosen Cloudera Big Data Solutions as accuracy precision storehouse or venture variation store for unlimited data scale.