Small Data vs Big Data: What Are The Main Differences?

In recent years, the IT sector has experienced rapid expansion. Companies have implemented new approaches and modified existing methodologies to achieve their desired goals. The data has played a major role in the achievement of these goals. The data has grown significantly, giving rise to new tools for data analysis and mining as well as management strategies. 

It helped businesses, government, and individuals in better decision-making. Everyone is familiar with the definition of data, but most people confuse big data and small data. They are not aware of how big data and small data are different from each other. Therefore, today, we will discuss small data vs big data in detail. 

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What Is Small Data?

Data that is “small” enough that humans can understand is called small data. The small volume of this data makes it easy to access, understand, and take action on this data. Small data mainly offers information that responds to a particular query or solves a particular issue.

Some examples of small data include driving records, inventory reports, sales data, biometric measurements, baseball results, search history, weather forecasts, and usage alerts. Small data is extremely easy to handle as it is made us of only useful units. It has definite and particular dataset attributes that can be used to examine present circumstances. 

Small Data can also refer to the useful datasets found after filtering through enormous amounts of data. Many organisational difficulties require fast and immediate analysis, which can be done with small data. 

What Is Big Data?

Big Data are information assets with high volume, high velocity, and high variety that requires advanced processing techniques to improve decision-making, insight discovery, and process optimisation.

As the name implies, big data is a collection of enormous data sets that conventional computing methods cannot process. The new advancements in technology and increasing people’s interest in using digital devices have challenged organisations to find new ways of handling and processing big data. Today, everyone uses the internet, cloud storage, social media, plays online games, etc. These things are increasing the volume of data. 

Until 2003, there were only five billion terabytes of data. In 2011, the same amount of data was produced in just two days. This volume was produced every ten minutes by 2013. Therefore, it is not unexpected that 90 per cent of the world’s data was generated in the last few years.

Small Data vs Big Data: What Is The Difference

The following describes the distinctions between Small data vs Big data:

Data Collection

Small Data is typically included in OLTP systems and is gathered more carefully before being added to the database or caching layer. In case immediate analytics queries are required, databases will have read replicas. 

On the other hand, the big data gathering pipeline will have queues like Google Pub/Sub or AWS Kinesis to balance high-velocity data. Downstream will have batch jobs for processing cold data and streaming pipelines for real-time analytics.

Data Processing

Data processing is important to consider in small data vs big data. As transaction systems create the majority of small data, Analytics built with the small data are typically batch-oriented. Only in rare cases, Analytics queries run directly on top of transaction systems. 

At the same time, Pipelines for batch and stream processing are both included in big data environments. Real-time analytics applications like stock price forecasting and credit card fraud detection utilise streams. Advanced algorithms and data are used in batch processing to implement complex business logic.

Scalability

Small data systems often scale vertically. The vertical scaling increases system capacity by adding more resources to the same machine. The vertical scaling is expensive, but it is easier to manage.

 The majority of Big Data systems rely on a horizontally scalable architecture, which offers greater agility at a lower cost. Horizontally scalable systems are now even more economical due to the availability of preemptive virtual machines.

Data modelling

The transaction systems generate small data in normalised form. ETL(Extract Transform Load) data pipelines transform it into a Google BigQuery or star schema in a data warehouse. Since the data is more structured, the schema is always upheld here while writing data, which is rather simple. 

On the other hand, tabular data only makes up a small portion of big data. Here, data is copied more frequently due to a number of factors, such as failure handover or a limitation of the underlying database engine (For example, some databases only support one secondary index per dataset). When writing, a schema is not enforced. Instead, a schema is verified as data is being read.

Storage & Computation Coupling

Storage and computing are tightly coupled in traditional databases, which primarily manage Small Data. The provided interface is the only way to add and remove data from the database. Data cannot be inserted directly into the database storage or accessed by other DB engines for existing data. The integrity of the data is actually substantially enhanced by this architecture. 

In the case of big data, there is a very loose coupling between storage and computation. Data is typically stored in a distributed data storage system like AWS S3, HDFS, or Google GCS, with a compute engine afterwards selected to query the data or perform ETL. For instance, interactive queries using Presto(Link) and ETL using Apache Hive might be run on the same data.

Data Science

Machine learning algorithms need a properly encoded and well-structured data input. Mostly the input data come from both transactional systems, like a data warehouse and big data storage like a data lake. Since the data preparation stage is narrow, machine learning algorithms that use small data will be simple to implement. 

In the case of Big Data, data preparation and enrichment take substantially longer. Due to the large volume and diversity of data, big data offers a wide range of opportunities for data science experiments.

Data Security

Data security is also important to consider in small data vs big data. The security of small data that mainly resides on the transaction systems or enterprise data warehousing includes the features like data encryption, user privileges, hashing, etc. The corresponding database providers provide these security features.

On the other hand, Big Data system security is far more difficult and complex. Examples include isolating cluster networks, encryption of data in transit and at rest, strict access control policies, and other security best practices.

Small Data vs Big Data: Head-To-Head Differences

Below is the head-to-head information about small data vs big data in tabular form:

Conclusion

We have looked at every crucial thing about small data vs big data. Unlike big data, which has a volume in petabytes or exabytes, small data refers to data that is small enough to be easily understandable by humans. 

Big data is characterized by 3V’s: volume, variety, and velocity of data. Together, these three characteristics make big data particularly challenging to handle. On the other hand, small data has only useful units that are very easy to handle. Hopefully, now you are clear with small data vs big data. 

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