Re: ETL stuff, informatica

From: <sudheer1212.m_at_gmail.com>
Date: Mon, 12 Aug 2013 06:17:40 -0700 (PDT)
Message-ID: <43f5adf9-4e19-45b4-b250-b754113020ab_at_googlegroups.com>


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BIGDATA: daily we have a tendency to produce two.5 peta bytes of data - therefore ninetieth of the information within the world wide nowadays has been created within the last two years alone. This a lot of data comes from everywhere: like sensors wont to gather climate data, a post to social media sites and digital footage and videos and get dealing records, and cell phoneGPS signals to call many. This data is BIGDATA.Hadoop online training

HADOOP: may be a biggest frame work to method petabyets of data during a quicker and economical manner. Hadoop supports each structured and unstructured data. Hadoop online training

Whereas data Warehouse and presently fashionable metal Systems supports solely structured data. That too dig data from immense amount of information is basically causes high latency within the ancient data warehouse.

HDFS: may be a distributed filing system in Hadoop Frame work.

The HDFS design allows organizations to store bulk volumes of structured and unstructured data.

Example: for unstructured data is, Email messages, email server logs, face book messages, blog information log, images, videos, audios etc.

Map scale back…> Map Reduce may be a framework, to distribute the add to tasks across multiple nodes…., and allows the system to method all tasks parallel and collect leads to smart speed.

PIG: may be a dataflow language in Hadoop surroundings and it writes hidden Map scale back code once the pig decreased code compiled. (Ex: rather than writing a hundred lines of JAVA Map scale back Code, you'll win it by simplified script of PIG in ten Lines)

HIVE: is data Warehouse in Hadoop frame work

HIVEQL (Hive question Language) is employed, almost like Sql of RDBMS however slight variations area unit there.

HBASE: Is columnar databases is Hadoop Frame Work

SQOOP… Used for information connections, same vogue we have a tendency to export data from Hadoop to databases additionally.

NO SQL: may be a stunning thought, to figure with bulk data aggregations. Bcoz, in NoSql we have a tendency to store rows as columns. Received on Mon Aug 12 2013 - 15:17:40 CEST

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