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 and create the same tables in Hive, what would be
the next step to get to a consolidated data model in Hive on HDFS. The entry
tables will be tabular tables in line with source, correct? How many ELT steps
need to apply generally to get to the final model. Will ELT speed up this
process
I understand this is a very broad question. However, any comments will be
welcome.
Regards
On Friday, 18 December 2015, 22:27, Jörn Franke <jornfra...@gmail.com>
wrote:
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 aware of
how it can bring benefits to a specific business domain. You could explore the
potentials together in workshops, design thinking etc. once you know more
details, both sides decide on potential ways forward you can start doing PoCs
and see what works and what not. It is important that you break old ties
created by more traditional data warehouse approaches in the past and go beyond
the comfort zone.
On 18 Dec 2015, at 22:01, Ashok Kumar <ashok34...@yahoo.com> wrote:
Gurus,
Some analysts keep asking me the advantages of having Hive tables when the star
schema in Data Warehouse (DW) does the same.
For example if you have fact and dimensions table in DW and just import them
into Hive via a say SQOOP, what are we going to gain.
I keep telling them storage economy and cheap disks, de-normalisation can be
done further etc. However, they are not convinced :(
Any additional comments will help my case.
Thanks a lot
