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Big Data Analytics: Tools, Challenges & Future Trends

In today’s world, there are smart machines are all around us. Our smartphones, iPads, laptops, smartwatches etc are all continuously generating a huge amount of data every second. This continuously growing ocean of data can be chaotic in terms of volume. Big data analytics is the study of these large volumes of data. All organizations are coping with a lot of data coming from their customers, audiences and distribution channels. To refine the data and develop insights from it, they hire data analytics professionals. It is a booming career these days and there are several data analytics courses online which give professional training of big data analytics.

Big data can help a marketer find new product lines. It can help business managers reduce the cost of production or the time taken to solve customer complaints. In general, it makes decision making smarter and faster. Because of its relevance, it was already in use in industries like telecom, banking, healthcare, manufacturing, retail, to name a few.

As an analyst, knowledge of this field will help you in dealing with big volumes of data by the help of various tools meant especially for big data. There are several tools in the market. They are used to store and analyse data. Some of the key tools are Apache Hadoop, Microsoft HDInsight, NoSQL, Hive, Sqoop and Polybase. These tools can help an analyst store a big amount of data and structure them. Once they structure it they can analyse it to make some sense out of it.

There are some challenges in this field too. As of now, there are still improvements needed in the tools that we are using. Because these tools are still not perfect, there are often errors while processing the data files as some data goes missing or is inconsistent.

Another issue is the gap in demand and supply of big data scientists. Every company wants to hire big data professionals but since it is a new industry, the number of professionals is still not enough.

Finally, there is also a risk of data security. This data is often private and customer-specific and companies need to find out a way to ensure its privacy is maintained.

As this field is maturing, there are few trends emerging. Big data analytics will not just be about handling huge volumes. It will mean handling a variety of data sets which are being generated at high speed. Firms are also beginning to focus on storing data into the RAM of the machines instead of putting it in hard drives. This will make computing faster. Finally, big data coupled with artificial intelligence will make machines intuitive enough to learn and adapt to human needs.

More Information:

Future Proof your career with Big Data course

How to Build a Successful Career in Data Science?

Why Enrolling for an Online Big Data Course is an Ideal Career Move?

Executive Post Graduate Programme In Data Science from IIIT BANGALORE

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