Till now, we saw many articles regarding what is meant by Big data? What are its advantages and application? But the concept of Big data does not end there. There is something additional was available in the big data. One of those challenging topics is challenges of Big data. The term Big data seems to be very simple while explaining. And most of the people (especially youngsters) think that is a framework that processes the large (bulk) amount of data parallely. But in addition to that, there are certain challenges that the big data needs to maintain. And one should think that big data has become more popular because of meeting those challenges. And most of the small-scale companies were interested to move to big data because of the following challenges of Big data.
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Challenges of Big Data
Dealing with Growth:
As days pass on, data generation is increasing day-to-day. And we are experiencing the data growth in terms of petabytes. Additionally, we are experiencing double the data for every two years. And the developers think that this data may reach the moon Moreover, enterprises have responsibility for that much amount of information. And the requirement of this generated data cannot be predicted. So this data must be stored in a particular order for the immediate retrieval of the data.
Generating insights in Timely Manner :
People must remember one thing w.r.t big data, this is not only used for storing and retrieving of data, but it also is used for generating insights using that data. Because in today world, only generating and maintaining of data is not sufficient .additionally, we need to analyze the data before processing. And the general databases (or) software were not capable to analyze this bulk amount of data. So we used to take the help of tools to process this bulk amount of data.
Recruiting and retaining big data:
As data increases day to day, we need some additional manpower to process and maintain all those data. so companies started recruiting the new candidates to process all those data. And the candidates with this technology has more demand in the IT market. But the companies were not thinking to pay the amount asked for the right candidate.
Integrating disparate data sources :
As mentioned above, this big data refers to the bulk amount of data and we don’t get this bulk amount of data from a single source. It means we were experiencing this bulk amount of data from various sources in different formats. so we need to collect all those data and integrate it. So we use different kinds of tools to process the data in different ETL (extraction, Transformation, and Loading ) tools. This makes the data scientist analyze the data in a simple and easy manner. Today in the IT market, we can find the same number of job opportunities for both Hadoop developers and Data Scientist.
Validating the data :
Today, these data processing systems were not storing all the data that we're generating today. Because during the data generation ., we may some redundant data. So we need to eliminate those redundant and must store the unique data in the database. For example, consider an example of a hospital patient. Under his name, he may have one phone number at admission and another phone number at pharmacy and rest of the things were same. so we need to club those two numbers before processing of data rather storing the entire record with the same table name. this reduces the amount of data that we need to store and maintain. The remaining space will use for storing and maintaining the unique data.
Likewise, there are many challenges of big data that we were facing today. so get those challenges from the real-time experts of OnlineITGuru through Big data Certification
Recommended Audience :
There is nothing many prerequisites for learning Big Data Hadoop. Its good to have a knowledge on some Oops Concepts. But it is not mandatory. Our Trainers will teach you if you don’t have a knowledge of those Oops Concepts