We know that private companies and Governments have been gathering big data which consists of media, texts, transactions, logs and many more. Today’s tech market is more viable only to those industries and organizations within these industries that possess the ability to handle and process big data.
Big data is basically the huge volume of data which cannot be stored or processed within a given time frame using traditional methods.
An organization should be well-versed and armed with skillful resources, software, hardware and tactics to process and route the flow of huge data.
Companies focus on certain parameters (a few of which are discussed below) in order to manage big data. These parameters play a key role in the advancement of those companies involved with big data projects within the tech world.
Compatibility Organizations dealing with big data should not only worry about storing of huge data but they must also ensure that they shape this data in such a way enabling it to be accessed and manipulated using various software.
After all, the very reason for processing and storing such huge data meets its purpose only when the user gets it in a state to play with in any given platform.
Compression and Distribution From both technical and commercial point of view, companies that deal with large data cannot afford to store them in an ‘as received’ state as it might be expensive and turn out to be less efficient. This introduces the need for compressing the data received prior to processing or storing it.
Compression of data is not a comprehensive solution by itself, as it cannot be stored in a single computer or in a single processor due to the limitations of data compression.
Therefore, to ensure that the huge data fits into a storage facility it would not only be required to compress the data but it would also have to be divided into storable chunks so as to distribute the same to these storage facilities.
Distributing data to various storage facilities also leave plenty of room for parallel data calculation. In layman’s terms parallel data calculation simply means simultaneous calculations run over different segments of a particular data which may be distributed and stored in different storage spaces.
Processing speed Crunching big data is also vastly about processing big data. Companies that are incapable of processing huge data will not be able to create any significance of existence in the tech market.
Moreover, companies will have to equip themselves with some of the best techs to catchup with their competitors in terms of processing speed.
Cloud computing is one of the emerging state of the art technologies that help companies handling big data to run faster calculations in data processing so as to achieve the desired results in minimum time.
Manipulation The data collected and stored is of lesser value if it cannot be manipulated so as to generate required trends or produce relevant results.
This simply means that companies dealing with big data should consider using tools such as Open Refine or similar tools to loosen up those stiff data so that it can be operated with / in various platforms.
Concatenation of data is also a vital aspect that needs to be looked into while we discuss about manipulation of big data. Concatenation is the linking of two or more data to display a merged result. It is essential to ensure that the data required to be concatenated supports this feature. This in turn reiterates the relevance of data manipulation.
Simplification and consistency Complex data received should be processed and displayed in the simplest form possible so that it can be interpreted easily. It is also important to work towards uniformity of data so as to maintain consistency and smoothen the onward processing of data.
Uniformity of data will also be helpful in trouble-free sorting, identification and segregation of big data.
Storage, Archiving and Retrieval Last but not the least, storage is the greatest challenge of big data. Companies depend on cloud storage facilities to face this challenge upfront. Archiving and retrieval of huge data is an equally important concern for the organizations involved with big data projects. These companies need to identify data that may not have been of use for a long period of time and archive them accordingly. However, storage and archiving of big data makes sense only if they can be retrieved whenever needed.
Big data has become the genre of those companies which are ambitious enough to pursue projects that involve big data.
These companies utilize data processing tools which are currently in demand such as Apache Beam, Apache Airflow, Apache Cassandra, Apache Carbo Data, Apache Spark, TensorFlow, Docker and Kubernates and similar Hadoop / map-reduce tools for projects with huge data.
