Hadoop has been a noteworthy development in the software technology sector, often touted as the greatest and must have solution in recent times.
Unfortunately some cracks are now starting to show: the inability to obtain superior business insight faster than before due to tedious and complex data preparation and costly upkeep effort of the vast infrastructure. Hadoop’s inflexibility for ad-hoc queries hampers the promised route to advanced, easy-to-use Analytical solutions.
The elephant in the room
A common experience is one where the initial Hadoop system works acceptably, until data integration requirements increase in size and complexity and data volumes starts to surge.
This is when Hadoop based solutions turn out to be a limited, poorly performing, cumbersome to manage and a maintenance nightmare of environments not delivering on superior enablement promises for Data Scientist and BI professionals.
The complex data architecture built around Hadoop battles to bring quicker business insights. Increased data volumes demand expensive nodes that need intensive support. Placing more strain on already stretched resources. Slow data pipelines coupled with the complex Hadoop ecosystem where each business case usually needs different technology, raises alarm bells.
As predicted – failing to address business needs first and disregarding criticalness of data preparation functions such cleansing, quality checks, integration and de-duplication exasperates a faltering technology based solution. Any solution for this matter, will fall short if organizations do not address critical issues including data context, user autonomy and data governance.
Surrounded by an abundance of data the challenges to incorporate diverse BI traditions, ancient data integration practices, and sacred rituals to help cultivate Analytical alignment with business growth, quadrupled in the last two years.
Even though Data Science and Analytics are at the focal point of business transformation and play a critical role in unlocking innovation, there are mixed views on whether it’s desirable to implement advanced tools and technologies too early. Since most since most of these are still in the early adoption phase and are failing on the promised Analytic-Panacea.
To circumvent this situation, businesses can now benefit from the current cloud services war and may consider moving away from their Hadoop infrastructure.
Ongoing searches for cost effective Business Intelligence /Analytics and the increased need to support accumulating data-intensive workloads necessitates clever interconnection between structured and unstructured information.
The power of solutions in the Cloud and benefits of BI modernization are ample: reduced technical debt, options to embrace a hybrid cloud solution, support for expanding capacities, improved data security and compliance, enhanced application developer productivity are all prevailing reasons to upgrade from the Hadoop infrastructure sooner rather than later.
Business Intelligence visionaries will continue to prioritize modernization initiatives that can help shed the burdens of legacy infrastructure and boosts operational efficiency. Navigating disruptive trends and being faced with complex technology choices to build a stable, scalable platform that supports business growth and innovation, is no walk in the park.
Fortunately previous forceful demands for Hadoop, while it was perceived to be the next best thing, are falling by the wayside. Forcing IT to replace good old traditional solutions instead of elegantly extending to include the new, seldom proves successful.
Promises of easier analytics are thwarted by disappointing user experiences.
Although it is not necessary to throw the baby out with the bath water, it is certainly necessary to understand the role, strengths and weaknesses of Hadoop and admit it may not be suited for all analytical requirements.
For the want of staying in the loop,
All jumped through the hoop
Maybe now is the time
To leave this complexity behind
Time to get out the loop of
Hooray! No more dupe.
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