If we talk about the conventional data analytics strategies, they were very time consuming and experts has to investment large amount to collect all types of data. Yet there are chances that they have missed out something. So what is more effective and easy data collection solution? Is it Hadoop? Yes, it is Hadoop that handles everything from data collection to data storage.
If we talk about big data projects, data collection, storage and integration is always difficult and needs skills and more attention. Hadoop ETL developers have all that skills and experience that they can handle big data projects with ease and convenience. The complete process of big data collection, data storage and data integration may sound crazy but it is easy to handle when you have to deal with small data sets and data is break down into chunks.
Three popular categories to collect data are described below here –
First of all, data is collected from traditional sources by Hadoop ETL developers and ETL processes are one of the most popular approaches opted by every individual organization. You can collect data manually by simply copy and paste procedure or take help of modern software techniques for effective results.
Secondly, data collection is possible through Internet f Things IoT. The main issue is that data is voluminous and have to be collected wisely while working with IoT. This is standard form of data and does not require any immense transformation.
Next category is unstructured data when data is collected from media files or other similar resources. It is not that much complex but you have to sure that you have not missed out anything. Even after right tools, techniques and approach, data collection is most difficult equation for Hadoop ETL developers.
Next is data warehousing or data storage that can be done quickly if data has been collected wisely. If you still face any problem related to data collection or storage contact expert Hadoop ETL developers now.
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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.