Big data is a widely used and powerful technology. Now the next set of questions you’ll be having are: what is big data? Is it useful to learn this technology? Why is everyone talking about it? Well, big data is a phrase that depicts the large volume of data – both structured and unstructured. That engulfs businesses on a day-to-day basis. The amount of data isn’t important. Important is what organizations decide to do with the data. Big data can be studied for insights that give rise to better decisions and strategic business moves.
Why Do We Need Big Data?
The importance, as well as need of big data, doesn’t revolve around the amount of data you have, but what you do with it. Data can be taken from any source and be analyzed. To find answers that allow:
- Cost reductions
- Time reductions
- New product development and optimized offering
- Smart decision making.
It is being used in various fields to get some insight. Fields like banking, agriculture, chemistry, data mining, cloud computing, finance, marketing, stocks, healthcare, etc.
The healthcare sector has lagged behind other sectors in the usage of big data, part of the problem arises from resistance to change providers are accustomed to making treatment decisions independently. Using their own clinical judgment, rather than relying on rules-based on big data.
Technologies you should know to make a future in this field
NoSQL is a crucial part of big data and databases that support NoSQL thus become important as well. Cassandra and MongoDB are the two most commonly used NoSQL databases. Hadoop, Hive and Apache Spark are the prominent tools used to analyze data. There are other tools available as well like Rapid Miner, Apache Storm and so on. Programming languages like R are also common.
What Are The Challenges Faced?
With the ever-increasing number of IoT devices and the gigabytes and petabytes of data they generate it’s hard to manage, store and analyze this data. Thus, making it one of the most common challenges faced by big data. Dealing with huge amounts of data is one but migrating these existing data workloads to a platform where analysis can be performed on it is also considered as a challenge.
As you don’t want to avoid errors and loss of data and dealing with huge amounts of it we always run a risk of losing some of it. We also need powerful streaming data pipelines. Pipelines that are elastic enough to take the data and load it properly. These datasets are large and scaling it can be a challenge. In order to analyze this data, we need to deploy some sort of machine learning algorithm. The job has to be done properly.
Roles to make a the organization successful
Good data engineers are required to build pipelines that fetch clean data. A Decision maker who decides how far you want to go with your data-driven application. An analyst to explore the data and get insights and potential relations which can be useful in the ML model. Applied Machine learning Engineers who have real-world experience building production ML models from the latest and best information by the researchers. Data scientists who have mastery over statistics and ML.
Analytics manager who can lead the team. Social scientists and ethicists who ensure that quantitative impact is there for the project you’re working on and is the right thing to do. All there play an important role in making a the organization successful. Thus, they need to work together in harmony.
Big Data is a powerful tool that makes things ease in various fields as said above. Its applications are applied in various fields like banking, agriculture, chemistry, data mining, cloud computing, finance, marketing, stocks, healthcare, etc.