Hadoop open source project was released to the public the project was based on the storage tech called MapReduce developed by Google to house the entire worldwide web what it did phenomenally well was offer an open door to data while allowing organizations to scale that data footprint on cheap Hardware up until that point organizations had been paying through the nose to store data on expensive enterprise class hard drives so there was real value in using Hadoop.
To store this massive quantity of data this was Hadoop strength its weakness at the time however was security orchestration query speed and the complexity of queries over time those weaknesses have been addressed but query speed still remains a thorn in the side of many Hadoop deployments this is largely because of the way Hadoop stores its data and we try to give you a tangible example imagine I gave you a college history book but in this book.
There are no titles no chapter headings no subheadings just 500 pages of textbook size reading now imagine I asked you to try and find out how much World War two cost each country it would certainly take you quite a bit of time to come up with the answer especially because I’m asking a deeper level of specificity I don’t just want to know where World War two is in the book I want to know exactly how much it cost each country but if I add those chapter.
Headings and everything else back into the book it suddenly takes a lot less time to come up with that answer albeit imperfect this is a good parity to why many corporate customers were disappointed when they tried to use Hadoop for enterprise wide analytics I’m not saying that Hadoop doesn’t have Enterprise wide success stories to tell but where we find those success stories we find the tried-and-true practices of data aggregation and data schemas but Query speed is only half of the equation of why Hadoop la steam.
The cloud is at the root of its demise the big vendors in the cloud space have been offering their own storage layers which now basically do everything Hadoop does but without the hassle of managing a ton of hardware interestingly enough most of these cloud vendors were housing Hadoop deployments all the while creating their own cheaper alternatives over the last five years .
Alternatives don’t require its users to do much in terms of managing redundancy server uptime etc now just like Hadoop these storage layers are not super great at high-speed high frequency queries but that’s okay because there are other solutions within the cloud space which are good at such queries and they integrate seamlessly with their native cheap storage layers so what we’re seeing now is organizations trying to leave
Hadoop Many of these organizations are already in the process of offloading Hadoop sequence files into Amazon s3 and other cloud storage platforms because they’re cheaper to administer and scale however this introduces its own problems see Amazon s3 can’t natively read Hadoop sequence files without spinning up another.
Hadoop instance because of the sheer number of companies offloading Hadoop to Amazon s3 intricity created a solution called read Seq which allows companies to read a doop sequence files right from Amazon s3 additionally it allows organizations to choose the format type they would like to convert their data into such as JSON Avro or park’ if you’d like to learn more about intricity z’ read seq solution click on the link in the middle of this video or in the video description below and if you’d like to discuss your scenario with a specialist you can reach out to us at any time