Can Hadoop and Enterprise Data Warehouse Go Hand-in-Hand?
The introduction of Hadoop has brought about a major motivational revolution to the enterprise computing environments. The adaptability to Hadoop is growing day-by-day amid all types of business ventures. Justifiably so, because the economics of this software provides an ability to the user to implement a comprehensive range of management architectures with minimal barrier to the entry and to incremental investments as the needs for processing increase. But, does this mean that ventures must abandon Enterprise Data Warehouse and shift all their business intelligence efforts to Hadoop? Certainly not!
Hadoop V/s Enterprise Data Warehouse
Data Warehouse and Hadoop, both have their own set of different tasks to perform with different organizational advantages, and Hadoop must not be viewed as a replacement to the former. Hadoop adopters suggest quite a few challenges such as claims of the software being too technical and too slow for realtime analytics tracking.
Hadoop’s excellent MapReduce engine has been specially developed for conducting batch processing through its ability to carry out and distribute simple calculations. At the same time, it’s not a software users can turn their head to for carrying out ad hoc, interactive real-time data detection, and advanced analytics. Radical analysis asks for a software which has the process to help database nodes to communicate with each other for effective processing. Hadoop’s MapReduce certainly lacks this ability. Besides, Hadoop requires special skills which are quite different from that needed for working on Data Warehouse.
Hadoop and Enterprise Data Warehouse – Together
We vouch that companies must use these two software together in a complementary style. Let Enterprise Data Warehouse take care of the company’s structured and curated data while allowing Hadoop to work as a sandbox where users can experiment with new types of data including emails, Weblogs, text, and machine data. Traditional data types typically found in Enterprise Data Warehouse when combined with new data types can be quite useful in offering new insights. Businesses can also use Hadoop for cleaning and structuring data before it is populated to the Enterprise Data Warehouse. This helps the latter to pay more attention to the data with high value and leave behind the least important one.
Hadoop’s limitations of real-time analytics can be tamed by making use of in-memory or in-database analytics. Industry experts claim that this is exactly similar to what SAS High-Performance Analytics (HPA) technology offers. It allows users to explore complex data, develop necessary models step-by-step in order for the data to be processed in-memory or disseminated across a particular set of nodes.
Since these software pull data in the memory very quickly, companies can handle the request for running new analytical computations at a much faster rate and promise a better response time as well. Does this really help? Yes! This aids the businesses in making real-time decisions and creating more point-specific models. Also, because the data is stockpiled locally in Hadoop, users can process it without the need to move the data to a separate platform.
What’s more, Hadoop offers tons of leverages to users, such as the number of columns that can be added in a single table. The advanced analytics software makes use of an analytics-based table which can have thousands of columns. Since the number of variables can significantly impact the authenticity and accuracy of the results, Hadoop big data services helps support the advanced analytics, particularly well, as the data stored in it can be both, deep as well as wide.
The crux of the story, the use of both, Hadoop and Enterprise Data Warehouse together can help in commencing business activities in the most effective and efficient manner. While it is anticipated that Hadoop will Enterprise Data Warehouse, but for now, the need for both Hadoop and Enterprise Data Warehouse is requisite.