Small data and Big Data are two phrases that are often used interchangeably in the world. But what is the difference? In this post, we’ll explore the difference between small data vs big data, highlighting some of the key points between them. Data is often just associated with major corporations collecting large amounts of data.
However, big data is also collected by small businesses. The difference between big data and small data is the amount of data being collected. Big companies are in need of more information to make their decisions whereas small businesses rely on a smaller amount of data for their decisions. What is the difference for you?
Let’s start this blog article with the main difference between small data vs big data. But before jumping to the main point let’s start with the definition of both terms.
What Is the Difference Between Small Data and Big Data?
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When it comes to data, there are two main categories: small data and big data. As the names suggest, small data is a smaller set of data while big data is a larger set of data. But what exactly is the difference between these two types of data?
Small Data:
Small data is typically defined as data that can be easily managed and processed using traditional methods and tools. This type of data is usually not too complex or time-consuming to analyze. Because small data sets are less complex, they can be easily cleaned and organized for analysis. Additionally, small data sets can be stored in one database or file, making it easier to manage and process.
Big Data:
Big data, on the other hand, refers to datasets that are too large and complex to be processed using traditional methods and tools. Big data sets are often too messy and unorganized for regular analysis techniques. Instead, special tools and techniques must be used to clean, organize, and analyze this type of data. Additionally, big data sets are often distributed across many different databases or files, making them more difficult to manage and process.
Small Data vs Big Data: Difference Table
Below is a table of differences between Small Data and Big Data:
| Feature | Small Data | Big Data |
| Technology | Traditional | Modern |
| Collection | Typically, it is collected in a systematic manner before being put into the database. | Big data is collected utilizing pipelines with queues like AWS Kinesis or Google Pub/Sub to balance high-speed data. |
| Database | SQL | NoSQL |
| Volume | Range of tens or hundreds of Gigabytes | The size of Data is more than Terabytes |
| Query Language | only Sequel | Python, R, Java, Sequel |
| Quality | contains less clutter since less data is being gathered systematically. | Normally, data quality is not guaranteed |
| Structure | Structured data in tabular format with fixed schema(Relational) | Numerous data sets including tabular data, audio, text, images, JSON, video, logs, etc.(Non-Relational) |
| Processing | It Needs batch-oriented processing pipelines | It has both batch and stream processing pipelines |
| Hardware | A single server is enough | Needs more than one server |
Small Data vs Big Data: How You Analyze Both Data?
In order to analyze small and big data, you will need a few tools. For small data, a simple Excel spreadsheet should suffice. For bigger data, you might need a more sophisticated tool like SPSS or SAS. Once you have your data, it is important to understand what it is saying. This means looking at the numbers and trying to make sense of them. What do they mean? What are the trends? Are there any outliers? After you have done this, you can start to draw conclusions from your data. What does it all mean? What can you learn from it? What are the implications of your findings? By analyzing small and big data, you can gain a lot of insights into your business or topic of interest. With the right tools and approach, you can uncover hidden patterns and relationships that can help you make better decisions and improve your results.
How Much Data Do We Process in a Given Day?
The average person produces about 1.7GB of data every day. That might not seem like a lot, but it adds up. In a year, the average person produces about 657GB of data. And that’s just the average person.
Now, let’s look at some businesses. A small business might produce around 10TB of data in a year. But a large enterprise can easily produce hundreds of petabytes (PB) of data in a year. For example, Facebook has been reported to process more than 500PB of data every day.
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How Do We Store Large Amounts of Data?
There are a few ways to store large amounts of data:
Distributed file systems: A distributed file system is a file system that allows files to be stored on multiple servers. This can be useful for storing large amounts of data, as it can help to distribute the load and improve performance.
Hadoop: Hadoop is an open-source framework that helps to store and process large amounts of data. It uses a distributed file system, which means that files are stored on multiple servers, and it also has tools for processing and analyzing data.
NoSQL databases: NoSQL databases are designed for storing and retrieving large amounts of data. They are often used for big data applications because they can scale more easily than traditional relational databases.
Conclusion
There is no exact answer when it comes to the difference of both small data vs big data. The best way is to use a mixture of both, depending on the type of business you have. Small data can be more effective and easier to work with, while big data can give insights that would be otherwise impossible to get. Ultimately, the goal is to use the right tool for the job at hand in order to get the best results.
We hope you have a clear understanding of both small data vs big data. In case if we missed something or you need more information you can leave a comment below.