Introduction to Big Data
Large sets of data used in analyzing the past so that future prediction is done are called Big Data. The main concepts of these are volume, velocity, and variety so that any data is processed easily. Both structured and unstructured data are processed which is not done using traditional data processing methods. It provides information needed for anyone from the streams of data processing. This is used in research, analytics, medical field, education and the places where huge data is processed. It is evolved from social media, machine data, and transactional data.
Understanding the V’s of Big Data
1. Volume
Handling and processing a large amount of data is a common problem. It makes use of other technologies such as Hadoop, Apache Spark, and HDFS to perform the tasks at ease.
2. Velocity
Organizations collect data at high speed to process instant outcomes. It can cope up with this to provide seamless processing and results. Stock exchanges and Weather reports are some of the real-time examples.
3. Variety
- Structured
The set of data with a preset format, derived from a relational database. For example, an employee’s salary sheet with a predefined schema of things.
- Unstructured
These are random data without proper format or alignment. They require more processing time. Examples include Google searches, social media polls, Video streams.
- Semi-Structured
It is a combination of both Structured and Unstructured data. They have a proper structure yet lack the definition required.
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How is Work Made Easier?
Before this came into existence, linear and a line-by-line analysis was done on the data available. Later with the introduction of computer life was made easy with Excel spreadsheets. The users needed to tabulate the different record and perform the required study to derive a meaningful report. It was a game changer in many different ways. Extensive data sets up to terabyte can be processed and analyzed. Complex queries and algorithms are applied. Reports are generated with a better outcome with almost zero failures. All these in a matter of minutes to hours depending on the size of data fed.
Top Companies
It is employed in a wide variety of domains such as Manufacturing, Healthcare, Energy, Insurance, Sports, etc. Some of the top companies are listed below:
- IBM
- Microsoft
- Amazon
- HP enterprise
- Teradata
Components
There are various third-party tools as listed below, available to perform the analysis on the data that is available from sources. They are capable of performing as Standalone and with a collaboration of other components too.
- Hadoop
- HDFS
- Sqoop
- Map Reduce
- Apache Spark/Storm
- Google Big Query
- Amazon Kinesis
Use Case
- Management can take better decisions.
- To recognize the trends of customer needs and stay relevant.
- Low-risk outcomes.
- Decision Validation
- Target audience is identified.
Working with Big Data
With the help of third-party tools such as Hadoop, Spark we can load large data sets on to external storage. The data is processed based on the humanly written queries. The business intelligence team makes use of these reports to understand the predictive pattern and rectify previous mistakes. The data can be visualized to make useful decisions.
Advantages
- Business objectives can be understood completely.
- Learn the meaning behind numbers.
- Analyze the root causes of previous failures.
- Insights on future outcomes using easy-to-understand language
- Contribute to making perfect decisions.
Pre-Requisites
There are no pre-requisites for using its tools. Basic knowledge of programming languages such as Java or Python would be helpful. Understanding how databases work and primal queries are sufficient. There are other High-level languages such as Spark, Pig that are easy to learn and use. The User should be technically sound in the way of using these to get the desired output.
Why it is Used?
It is used to improve the applications and services to provide better outcomes. Various cost-efficient solutions can be derived. With the rapidly changing environment, it is essential to understand customer demands.
Scope
Data is never getting old fashioned and with the cutting edge technologies, it is increasing exponentially. There is a huge requirement for professionals in the field of this. It is evolving with huge potential for growth. Analysts become the decision-makers of the companies with proper usage of these technologies.
Needs
Nowadays data come in different forms. Many of the analytical solutions were not possible in the past due to the cost of implementation and lack of professionals. With this, we are capable of performing complex algorithms on machine data within a time interval. These have many real-time use cases such as fraud-detection, targeting audience on a global platform, web advertising, etc.
Target Audience
Organizations which make use of its components to achieve the following:
- Predict future trends and behavior patterns of customers
- Analyze, understand and present data in useful ways
- To keep up with competitors and stay relevant in the market
- Make powerful decisions
Conclusion
With growing demand and competition it is essential for a professional to remain updated. By efficiently using both the individual and the Organisation can gain in several ways. The analysts get a better understanding of the industry, conveying the same to the workers. A decision can be made based on reports rather than relying on guesses and intuitions.
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