The complexity of modern analytics needs is outstripping the available computing power of legacy systems. With its distributed processing, Hadoop can handle large volumes of structured and unstructured data more efficiently than the traditional enterprise data warehouse. Because Hadoop is open source and can run on commodity hardware, the initial cost savings are dramatic and continue to grow as your organizational data grows. Additionally, Hadoop has a robust Apache community behind it that continues to contribute to its advancement.
Ability to Ask New Questions
Hadoop’s ability to integrate data from different sources, systems and file types allows you to answer more difficult questions about your business:
- If you could test all of your decisions, how would that change the way you compete?
- Could you create new business models based on the data you have within your company?
- What if you could drive new operational efficiencies by modernizing ETL and optimizing batch processing?
- Are you ready to harness the hidden value in your data that until now has been archived, discarded or ignored?
Single Source of Truth
With the enterprise data warehouse approach, organizations find their data scattered across many systems and silos. This decentralized environment can result in slow processing and inefficient data analysis. Hadoop makes it possible to consolidate your data and business intelligence capabilities within an Enterprise Data Hub. The ability to save all organizational data at its lowest level of granularity and bring all archive data into an Enterprise Data Hub gives business users greater and faster access to data – resulting in deeper analytics using more data points.View larger image
Faster Data Processing
In legacy environments, traditional ETL and batch processes can take hours, days, or even weeks, in a world where businesses require access to data in minutes, or seconds – or even sub-seconds. Hadoop excels at high-volume batch processing. Because of its parallel processing, Hadoop can perform batch processes 10 times faster than on a single thread server or on the mainframe.
Likewise, when used as an Enterprise Data Hub, Hadoop can ease the ETL bottleneck by establishing a single version of truth that can be accessed and transformed by business users without the need for IT setup.
Get More for Less
The true beauty of Hadoop is its ability to cost-effectively scale to rapidly growing data demands. With its distributed computing power, Hadoop configures across a cluster of commodity servers, or nodes. By augmenting its EDW environment with Hadoop, the enterprise can decrease its cost per terabyte of storage. With cheaper storage, organizations can keep more data that was previously too expensive to warehouse. This allows for the capture and storage of data from any source within the organization while decreasing the amount of data that is “thrown away” during data cleansing.