It isn’t easy to point out exactly when Data began to swell into immeasurable quantities. But, by the 21st century, technology became every organization’s key priority. Its evolution revolutionised the way of life, creating an expanding internet population and a massive generation of Data. Organizations have used data long before its boom to make better and more calculated decisions in order to enhance its performance in the market. From the multitude of sources like images, videos, blogs and more, data gets generated and needs to be stored so as to be usable in the future.
The three primary challenges that large companies faced were:
Increasing capacity of data: It was difficult to handle such volume through their traditional databases
Diversity of data: The earlier sources could have been numbered to texts or tables, but new data arose with blogs, videos, images and more
Rapid data changes: With ever-changing data and their sources, a platform had to be created where this data could be available for use at the correct time to increase its effectiveness
To meet the demands of this unstructured requirement, Hadoop became the only answer. Known for its flexible, scalable and unswerving characteristics, Hadoop is easy on the pocket solution providing large-scale data storage facilities.
Now, organizations can use big data to develop new products and offerings to their customers especially when they use the online space for their products and services.
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The many kinds of Hadoop solutions for effective Data Analysis are:
- HDFS (which is a storage) – when you upload a pent byte file onto your Hadoop high bandwidth cluster, the data would be broken down into different blocks and spread it across the same cluster, also making 3 copies in case you lose a node.
- Map Reduce (which is retrieval and processing): This is a 2 step process for retrieval and processing of the uploaded data. The first step includes a mapper function that directs you to the cluster where your requested data point is. The second step is the reducer function that will collect this data and aggregate it for use.
- Pig: built by Yahoo as high-level data language to collect data from clusters
- Hive: built by Facebook where anybody who understands SQL can pull out data from clusters
With an open source framework like Hadoop, many technologies are built and added to its ecosystem. If you are interested to add to this Data frenzy, you also could learn and understand Data Analytics with scrum master certification.
We see several progressive advancements in the data technology space hence bringing in diverse insights from experts about the effective use of this data analysis technology. Today, nothing is too big or complicated for Hadoop. It is indeed a revolutionary solution that has enhanced business decisions and created the optimal use of data that was once deemed useless. It is considered one of the best data science programs that would continue to grow and evolve, in the days to come.