Hi,
Thanks for the info. I understand ELT (Extract, Load, Transform) is more
appropriate for big data compared to traditional ETL. What are the major
advantages of this in Big Data space.
Example. if I started using Sqoop to get data from traditional transactional
and Data Warehouse databases an
co.com]
Sent: 18 December 2015 21:51
To: user@hive.apache.org; Ashok Kumar
Subject: Re: The advantages of Hive/Hadoop comnpared to Data Warehouse
Yes, that is what I meant.
In practice, it is often not possible to reach a 100% perfectly denormalized
fact table (for instance, if you need
I think you should draw more the attention that Hive is just one component in
the ecosystem. You can have many more components, such as ELT, integrating
unstructured data, machine learning, streaming data etc. however usually
analysts are not aware about the technologies and it staff is not much
15 at 4:22 PM
To: "user@hive.apache.org<mailto:user@hive.apache.org>"
mailto:user@hive.apache.org>>
Subject: Re: The advantages of Hive/Hadoop comnpared to Data Warehouse
Thank you sir.
Can you please describe a bit more detail your vision of "A fully denormalized
columnar s
Thank you sir.
Can you please describe a bit more detail your vision of "A fully denormalized
columnar store"? Are you referring to get rid of star schema altogether in Hive
and replace it with ORC tables?
Regards
On Friday, 18 December 2015, 21:13, Grant Overby (groverby)
wrote:
You
You forgot horizontal scaling.
A fully denormalized columnar store in Hive will out preform a star schema in
Oracle in every way imaginable at scale; however, if your data isn't big enough
then this is a moot point.
If your data fits in a traditional BI warehouse, and especially if it does so
