Big Data Hadoop: How to reduce costs with big data

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Discover what the new Big Data-driven Hadoop system architecture is like and what its perfect complement is. Take note of how and why to optimize the data warehouse using Hadoop as a business data center.
Remember data processing and storage before Big Data Hadoop ? Traditional data storage environments have been transformed in response to the growing volumes and wide variety of data coming from the cloud, mobile devices, social media, connected objects, and other sources.


As data continues to grow, businesses must find ways to address common issues such as performance degradation as warehouses approach capacity and require costly upgrades . However, an upgrade is not the most effective way to manage a glut of underused data.

To keep pace with exploding data volumes, the data warehouse itself must evolve . One emerging strategy is data warehouse optimization using Big Data Hadoop as a business data hub to augment the infrastructure of an existing warehouse.

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Big Data Hadoop and the new systems architecture
By implementing the Hadoop framework to organize and process raw or rarely used data, the traditional warehouse can be reserved for high-value information that business users need to access frequently.

But this approach requires a new architecture , which will make it possible:

Accelerate time to value creation.
Maximize productivity.
Reduce costs.
Minimize the risk.
This system architecture allows you to capitalize on the value of Hadoop Big Data for the business, and it does so by growing legacy warehouses on two levels, on the one hand, by increasing their capacity and, on the other, by optimizing their performance.

In this process, the open Hadoop framework enables fault-tolerant parallel processing and storage of large amounts of multi-structured data on clusters of low-cost commodity servers. Thus , Big Data Hadoop is arguably a good solution for large-scale data processing, storage, and complex analytics , often requiring only 10% of the cost of traditional systems.

But while Hadoop enables enterprises to reduce infrastructure costs, Hadoop's limited availability and high developer costs ultimately undermine its value proposition. There is a solution, though.



The perfect complement to Big Data Hadoop
Informatica and Cloudera formed a partnership to create data integration tools for Hadoop. Today , Informatica offers a set of native Hadoop tools that address the following needs:

Codeless ETL development and execution.
Data quality flows.
Data integration.
With this new technology, companies have significantly improved developer productivity, while at the same time eliminating errors associated with manual coding.