What is Big Data?
As the name suggests, Big Data is data that is very big in size. The data is so huge that it is practically impossible to analyze it manually. For instance, every minute, Amazon makes close to 280K dollars in sales; hence the customer data generated every minute for amazon is Big Data. Another example is Instagram, where every minute, around 46K posts are uploaded, so assume the number of posts uploaded in a day or in a month; these all are Big Data.
These big datasets can be structured or unstructured, and clearly, we cannot handle them with traditional methods. For example, we cannot download and store all the tweets generated to date on our local computers to analyze tweets.
There are many challenges associated with Big Data, such as:
- Capturing the User Data in real-time
- Storing the data as it comes
- Searching in the dataset
- Visualization of the data
- Analyzing the unstructured data
- Transferring or sharing the data
The Five V’s of Big Data
We use five V’s to characterize the big data:
- Volume: The size, amount, and quantity of the big data.
- Variety: The different formats of data (Unstructured, Structured, or Semi-structured) and the diversity in the data types.
- Velocity: The speed at which the data is being processed (received, stored, and analyzed)
- Veracity: The quality or the accuracy of the big data.
- Value: The business insights, quality results, and patterns extracted from the big raw dataset.
Big Data with Machine Learning
Machine Learning algorithms tend to perform more accurately if large amounts of data are provided; hence we can achieve great results after combining ML with Big Data.
When integrated with cloud networks, Machine Learning can easily generate predictions on big datasets, even in real-time. For example, if a big pharmacy decides to process its customers' data on-premises, it will have to create an entire storage infrastructure. The pharmacy will require servers to store the data, networking, and security assets; these expenses can be avoided if they utilize the services on a cloud network such as AWS.
After storing the data on cloud servers, machine learning models can be run with GPU & heavy processors to generate predictions.
Frequently Asked Questions
1. What are the Five V’s of Big Data?
The five V’s of big data are Volume, Variety, Velocity, Veracity, and Value.
2. List some challenges that come with Big Data.
Big data has many problems and challenges, such as capturing, searching, analyzing, transferring, and extracting useful insights from big data.
3. What are the applications of Big Data with Machine Learning?
There are many applications of applying ML to Big Data:
- Personalized Recommendations
- Exploring Customer Behaviour
- Market Research & Segmentation
- Decision Making
- Finding Patterns in the Data
4. What is Supervised Learning?
Supervised Learning is a machine learning technique in which we map the inputs against some specific output.
5. What are the types of Supervised Learning?
The supervised learning-based tasks are classified broadly into two parts classification-based and regression-based; among these two, there are different machine learning algorithms.
Conclusion
The data in the world is growing at a really fast pace. When combined with Big Data and Cloud Computing, Machine Learning can help businesses optimize their working power & increase productivity.
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