CIO OPINION
Big vendors including Amazon , Microsoft and Google offer managed cloud environments for data lakes replacing the capital cost of an on-prem Hadoop environment with an elastic consumption model , where organizations pay for what they use . They also mitigated some of the security and management challenges , allowing businesses to focus on data usage , rather than maintaining the environment .
The consumption model encouraged users to avoid dumping all their data into a central lake and instead load what they need for analytics , leading to smaller , purpose-built cloud data lakes or cloud-based data ponds . The rise of data science and Machine Learning platforms , as well as availability of SQL-based analytic services made accessing and analyzing stored data easier . This has meant data insights are made available faster to business users .
The challenge of making real-time analytics-ready data available to data consumers however remains . Traditional data integration approaches slow the data pipeline , making data outdated even before it is processed and ready for analysis . Then there are challenges around data trust and accessibility to data consumers .
Extracting value from data lakes : How to fill and refine your data lake
In the rush to build a data lake , it is easy to focus on hydrating the data lake and overlook how to make that data actionable for analysis . But storing data in the data lake is just the first step . The value of data lakes comes not just from their ability to quickly and cost-effectively store all types of data , but also from processing and
In the rush to build a data lake , it is easy to focus on hydrating the data lake and overlook how to make that data actionable for analysis . But storing data in the data lake is just the first step .
www . intelligentcio . com INTELLIGENTCIO APAC 45