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When should we take advantage of big data and data warehousing technologies / Specifications
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When should we take advantage of big data and data warehousing technologies / Description
There have been lots of convincing arguments within and outside the big data community about what the big data trend means for each one’s current data warehouse. And the answer here is using the best tool for the job. Each system is doing what it is best designed to do. For instance, in the case of Hadoop, it is processing large amounts of social networking data rapidly in parallel. In the case of the data warehouse, it is availing that data to business users, knowledge workers or data scientist who are utilizing that data to make business decisions.
Use the best tool for the job As there are exceptions to every rule, big data and data warehousing technologies are adapted for various purpose. The goal is to take advantage of these solutions for what they were designated to do.
1.Discovery of unexplored business questions
Such big data technologies as Hadoop are very ideal for quick pattern recognition, making “exploring” work very rapid in a big data environment. While data warehouses can also do discovery work, relational databases and the corresponding SQL language are not as effective for comparisons across and between large and often unstructured data suites.
2.Clean, consistent, high quality data
Regarding to data quality, lots of data warehouses have integrated data quality functions built-in. The data that is utilized for analytics must be able to make sense to end users. In big data environments, nevertheless, there could be a reason to provision data in its unstructured format. Data quality can take place inside Hadoop, yet there are not many data quality solutions for Hadoop so that data cleansing might include manual coding. Nowadays, most companies tend to make data quality a function of their analytics environment.
3.Low latency, interactive reports
Data warehouses have long been known as the answer for low latency or interactive reporting. Yet with new data visualization tools, big data technologies are offering an interesting new option for reporting against big data platforms.
4.Raw, unstructured data
Big data technologies are primed to process raw and unstructured data. Although they can not process it rapidly, yet they are able to store it cheaply as well avail it to a range of projects locally. On the other hand, data warehouses often deal with aggregated data. Thanks to the process of “late binding”, Hadoop is capable of keeping raw data and making it make sense at the time of the query. In other words, it might apply various rules to the data for various purposes.
5.Analysis of preliminary data
This relates to data which might be in the midst of being processed. You might be pre-processing the data prior it is stored in the data warehouse or Hadoop. As you might want to discover the data before you load it into another environment, Hadoop offers a specific environment in which structured and unstructured data can be discovered. Meanwhile, with data warehousing, the data modeling and loading of data might take more time.
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