The tech growth in the last decade has been so great that the things once considered inconceivable are now mainstream and the tasks too difficult that required a special skill set can now be completed by almost anyone. In the midst of it all, huge volumes of data are being produced every day, and, as a result, Big Data Analytics has grown into something very important for businesses looking for leverage over their competitors. In between all of it, Hadoop has made a place for itself as a cloud-based or an on-premise solution for all data needs. While it has lived up to its potential, there are certain scenarios where using a relational database might be a better option.
For the organizations which is wondering what would suit their big data needs, here are a couple of factors that need to be considered when choosing between Hadoop and traditional databases.
Is your data unstructured or structured?
Data that is produced by various sources, for instance, consider sources such as text documents, videos, audio files, social media posts, and emails, it is unstructured data. Being huge and complex as well, traditional databases are unable to efficiently and effectively handle this unstructured data. Hadoop is can easily take care of this voluminous amount and can efficiently aggregate, join as well as analyze these huge volumes, without the data having any sort of structure. It allows organizations to siphon off insights off of the data very effectively and in a timely manner. Thus, Hadoop is the best choice for unstructured Big Data.
Data which resides within a file or is generated by a program to be stored in the database is known structured data, even huge amounts of it, it is still structured data. It can easily be stored, queried as well as analyzed in a very simple way. This type of data requires a traditional database.
Is there a need for scalable analytics infrastructure?
The organizations whose influx of data always varies, need to employ Hadoop’s scalable infrastructure for effectiveness and efficiency. The scalability of the platform allows servers to be added according to the demand of growing workloads. As a cloud-based service, Hadoop offers more flexibility allowing to accommodate fluctuating workloads. On the other hand, the organizations whose workloads and seem to follow a pattern are definitely better served by traditional databases.
Do you require fast data analysis?
Hadoop has been designed for huge data processing jobs which address every file in the database, and that type of processing takes up time. Hadoop is able to handle such data intensive jobs with impressive efficiency. Tasks where analyzing such high volumes of data is required with efficiency, Hadoop is ideal, especially if your data is unstructured.
On the other hand, if your data is structured and you require quick data analysis on structured data, a traditional database. Also, hybrid systems are a good bet as they allow organizations to get the best of both worlds.
So what is best?
This all depends on your needs and your requirements. While the sheer benefits of Big Data and the competitive advantages are real, those benefits can be truly be reaped when companies employ both the platforms effectively and efficiently. Both the platforms have their benefits but only if you use them according to their purpose.Some Hadoop Use Cases >>