Answer / chinni

1tera data is huge amount of data so if we use normal load
type it takes so much of time....to overcome this better to
use bulk loading...and also taking the large commit
intervals we can get good performance....and one more
technique is external loading.....

Answer / james

I agree with you Chinni.
But my suggestion is instead of loading huge amount of data
at a time,we can always split the source if it is a file
using unix and extract the data from all the source files
using indirect type and load into target.

Please let me know if you have any suggestions.

Thanks.
James.

Answer / vasu

Always Partition is the best option for huge volume

Answer / chiranjeevi k

We can use push down optimizationto to handle the huge
volume of data.

Thanks
Chiranjeevi

Answer / bakshu shaik

one more thing we need to take into consideration, along with above suggestions:

If the target having an index, Loading time (huge amount of data) increases/performance hazard......so it's always better to drop the index before loading(Stored procedure transformation) and create again after loading completed...!

Answer / sudheer113

Along with the above suggestions......

Better to use partitioning (single or multiple thread) and and increase the commit level to 50 lacs. Better to load first flat file instead of loading into table.
then if u r db is oracle better to use sql loader to load data from file to table.If DB2 than use db2 loader.

Answer / upendra

large amount of data in source file.So we can devide partitions and commit level ur own wish,then connected to different target table, each one target table better for bulk load.

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rank() over (partition by opt2.dim_plat_site_id, opt2.dim_site_opt_sid order by case when opt2.dm_market_flg in ('Y', 'U') then 1 else 2 end, lkp.contact_rank) as rank1, case opt2.contact_type when 'Buyer' then row_number() over (partition by opt2.dim_plat_site_id, opt2.dim_site_opt_sid, lkp.contact_rank order by has_name_flg desc, ship_to_flg desc , last_order_dt desc) when 'Decision Maker' then row_number() over (partition by opt2.dim_plat_site_id, opt2.dim_site_opt_sid, lkp.contact_rank order by has_name_flg desc , last_quote_dt desc , mailability_score desc , source_ranking desc) when 'Influencer' then row_number() over (partition by opt2.dim_plat_site_id, opt2.dim_site_opt_sid, lkp.contact_rank order by has_name_flg desc, mailability_score desc, source_ranking desc) when 'Payer' then row_number() over (partition by opt2.dim_plat_site_id, opt2.dim_site_opt_sid, lkp.contact_rank order by has_name_flg desc, mailability_score desc, source_ranking desc) --elu 05/28/2013 else row_number() over (partition by opt2.dim_plat_site_id, opt2.dim_site_opt_sid, lkp.contact_rank order by has_name_flg desc, mailability_score desc, source_ranking desc) end rank2 row_number() over (partition by opt3.dim_plat_site_id, opt3.dim_site_opt_sid order by rank1,rank2) as "rank", case when "rank"<= opt3.maximum_value then 'Y' else 'N' end as include_flg

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