Since last few years, enterprises across private and public sectors have taken a strategic decision to leverage on big data. And the challenge to extract value from their big data is like other conventional problem of filtering business intelligence from transactional data. Enterprises take help of the process that is used to extract, transform and load data into warehouse for subsequent analysis. This process is also known as hadoop ETL.
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| Hadoop ETL |
Big data analysis offers various significant benefits to the business. Major business analysts with global market and complex supply chain have vast insight into customer demand. They do it by analyzing data points collected from business transactions and market information. Moreover, the data required by the company is embedded in economic reports, news sites, discussion forums, social sites, wikis, tweets, blogs and transactions.
By analyzing all available data, decision-makers are able to assess competitive threats, strengthen supply chains, anticipate changes in client behavior, enhance the effectiveness of marketing campaigns and improve continuity of business.
Many of these benefits are not new to companies having mature processes for incorporating BI and analytics into their decision-making. The cost of technologies required to store and analyze big data has dropped because of open source software used by companies. Now, companies don’t have to focus on relevancy of data, but the way to extract the most value from all available data.
By analyzing all available data, decision-makers are able to assess competitive threats, strengthen supply chains, anticipate changes in client behavior, enhance the effectiveness of marketing campaigns and improve continuity of business.
Many of these benefits are not new to companies having mature processes for incorporating BI and analytics into their decision-making. The cost of technologies required to store and analyze big data has dropped because of open source software used by companies. Now, companies don’t have to focus on relevancy of data, but the way to extract the most value from all available data.
To ingest, store and process big data, companies need a cost-effective infrastructure through which they can easily scale with data amount and scope of analysis. The traditional ETL process helps companies in extracting data from multiple sources, and then cleanses, formats, and loads data into warehouse to perform analysis.
Core component of Apache hadoop is an open source distributed software platform used for storing and processing data. New Hadoop update brings loads of features for hadoop operators and developers. Let’s learn about these features in detail-
For hadoop operators, it brings-
Core component of Apache hadoop is an open source distributed software platform used for storing and processing data. New Hadoop update brings loads of features for hadoop operators and developers. Let’s learn about these features in detail-
For hadoop operators, it brings-
- Smart configuration
- YARN capacity scheduler
- Customized dashboards
For hadoop developers, it brings-
- Fast and simple SQL Editor for Hive
- Complete new experience for Apache Falcon
- Easy Pig editor and web based HDFS browser
Most companies are facing new challenges related to data integration, i.e. incorporating data from social media and unstructured data into BI environment. With Hadoop ETL technology, companies can avail cost-effective and scalable platform for including big data and preparing it for analysis.
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