Empowering data analytics with our data lake management software platform
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Maurizio Colleluori looks at the five major reasons behind data lake failure, pinpointing what businesses need to do to get back on the path to success. The reality is that data lakes are failing to support the time-to-market requirements new analytics-driven innovation requires, and it is safe to say that in many companies, data lakes are widely perceived to be expensive and ineffective. So why is it happening? In this article, we look at some of the common culprits turning data lakes into data swamps, and at the same time deliver advice based on experience to help companies from experiencing data lake disasters.
Over the past several years, forward-thinking companies have been creating custom engineered data lakes in order to store large volumes and different varieties of enterprise data in an efficient way. To get the job done, many companies have tried to use complex, custom-engineered and Hadoop-enabled open source solutions in-house. However, while the software may be free, the engineering expertise this approach requires means that most companies are looking at multi-million dollar investments right from the start.
For many companies, working with data lakes has become a frustrating and unsuccessful experience: instead of being focused on building analytics and improving the quality of the data lake, engineering teams often spend most of their time dealing with requests to ingest new data sources or wrangle data. As a result, they have little time left to focus on data improvement and delivering value from big data analytics. Using Kylo, our open source data lake management platform, companies are able to generate valuable insight from their data lakes faster, bringing innovation via products and services to the market at unparalleled speed.