Reasons Not to Use Hadoop for Analytics(quantivo.com)
quantivo.com
Reasons Not to Use Hadoop for Analytics
http://www.quantivo.com/blog/top-5-reasons-not-use-hadoop-analytics
5 comments
A mistake many groups tend to make.
Hadoop is good at what it's designed for (data processing parallelization built by programmers) and bad at what it's not (ad hoc queries by people who are not programmers).
Move along here folks, nothing to see here.
Move along here folks, nothing to see here.
I'd constrain the description of what it's good at even further. The kind of data processing parallelization it's designed to support is map/reduce. That's only one out of a wide array of possible ways to organize a distributed processing job, and not all tasks are well-suited to it.
I work at Quantivo and we didn't post this but appreciate the shout out. Joe added a technical whitepaper written by another Quantivo staffer to his blog post, check it out.
What does ad-hoc queries on extremely large datasets easily and in a reasonable amount of time?
Google BigQuery (disclaimer: I work on the BigQuery team): https://developers.google.com/bigquery/
Blog post from my brother (we both work at Quantivo) on BigQuery: http://www.quantivo.com/blog/3-big-technical-limitations-goo....
This blog post would be more compelling if it made a case for how Quantivo avoids these BigQuery "limitations." Or if it were possible to find any technical detail about Quantivo's architecture without having to give out personal information first (http://www.quantivo.com/resources/wp-behavior-analytics-tech...).
BigQuery is based on Dremel, which is an open book: http://research.google.com/pubs/pub36632.html
BigQuery is based on Dremel, which is an open book: http://research.google.com/pubs/pub36632.html
how is this different from hive on hadoop?
Check out "What Hive is NOT" from this page: http://hive.apache.org/docs/r0.8.1/
"Hive is based on Hadoop, which is a batch processing system. As a result, Hive does not and cannot promise low latencies on queries. [...] Hive queries response times for even the smallest jobs can be of the order of several minutes. However for larger jobs (e.g., jobs processing terabytes of data) in general they may run into hours."
BigQuery OTOH can query over billions of rows in seconds. It is based on a system called Dremel (http://static.googleusercontent.com/external_content/untrust...) that is specifically designed for interactive use, as opposed to being a query language built on top of a batch system.
"Hive is based on Hadoop, which is a batch processing system. As a result, Hive does not and cannot promise low latencies on queries. [...] Hive queries response times for even the smallest jobs can be of the order of several minutes. However for larger jobs (e.g., jobs processing terabytes of data) in general they may run into hours."
BigQuery OTOH can query over billions of rows in seconds. It is based on a system called Dremel (http://static.googleusercontent.com/external_content/untrust...) that is specifically designed for interactive use, as opposed to being a query language built on top of a batch system.
Awesome, thank you, I have some reading to do :)
Sounds like an ad for Quantivo. You can pry MapReduce out of my cold, dead hands.
Maybe I'm missing something, but it sounds like the author tried to fit a tool to his needs rather than choosing a tool that already does so.