Before start discussing small data, let’s take a look at ‘Big data‘. Big Data is a very common term you might have heard from every second person in the industry. In most of the blogs and books, you see that Big data consists of 3V’s; Volume is for the amount of data, Variety is Type of Data, and Velocity is how fast data is getting processed in a Big Data Application.
Are these three V’s the only definition of big data?, or it seems like it’s infinite and raw data, which need to be:
- Collected from different sources
- Processed for filtration
- Analyzed to get Small data
‘Small Data’, Yes it is ‘Small Data’. Now, there must be a question arising in your mind, that what is small data. Now, let’s go through the following steps in this blog, we will have the answer to it.
Overview of Small Data
Small Data generally refers to “Informative data”. The depiction of analytics reports is completely based on Small data. We can use this informative data to find out solutions for specified problems. By applying these solutions, we can achieve fruitful results.
Small data helps to take quick decisions related to strategy. These changes can bring smart change in strategy followed in the organization. It also reduces the costs required for implementing those smarter strategies. The most importantly, the fact of small data is your target audience for which strategies are decided. You can also use it for recalculating risk portfolios and optimizing its surroundings accordingly.
Difference between Small Data and Big Data
There are various differences between Small Data and Big Data, these differences are:
What is Small Data and Big Data
Small data refers to a small dataset, which is easily understandable and interpretable to us. Whereas, Big data refers to a large dataset containing relevant and irrelevant information. Traditional databases can not process these datasets. A number of algorithms are needed to be executed on a big dataset to extract informative data from this Big Data.
Data sources of Small data and Big Data
Small Data is the data which relates to a specific target. This target data is actionable to achieve a goal and centric to a purpose. It is customizable and real-time data, that can be pushed to get the result. On the other hand, big data is pulled from various systems. Hence there is a high percentage of irrelevant data, which needs to be filtered through different processes.
Volume of Small data and big data
Small data deals with 10’s and 100’s of GB only. In rare cases, it turns to 1000 GB, which equals to 1 TB only, whereas Big data deals with many of terabytes and sometimes peta and exa bytes too.
Data flow in Small Data and Big Data
As small data is a centric data so, there is not too much data needs to be processed. It needs controlled and steady flow of information to process into the system. Data accumulation is also slow in case of small data. On the other side, big data is to collect data from various sources so data arrives here at a very high speed. As distributed systems work here so all enormous data get accumulated within a very short duration.
Type of data in Small data and big data
There is structured data used in small data. This structured data exists in a tabular form with a fixed schema. Small data also uses few types of semi-structured data such as Json Format and xml format. In contrast with Big data, there are a variety of data used, for example:
- Tabular data
- Text files
- Images
- Screenshots
- Video clips
- Json and XML files
- Sensor data
Quality of Small Data and big data
Small data contains very less noise as data collected in a controlled manner. In Big data, data contains too much noise because it gets collected from various sources at very fast speed. Hence, this noisy data needs to be processed through a proper filtration method.
Usage of Small Data and Big data
Small Data is very valuable for the Business Intelligence system, which contains reporting feature to show analyzed data. To find out the actual value of big data, it needs to be processed through a very complex process of Data mining, to find out Pattern and recommendation.
Integration of Big data and Small data
As we all know, the amount of data is growing day by day. As per the reports shared by IDC, there was a growth of 40% and which grows every year and is supposed to grow faster in the upcoming years. It may cause to work with big data in the upcoming year even though we are working with small data currently. To achieve such a goal, we should use Big data framework such as Hadoop for processing our Small data and start preparing that for the future.
With the use of Hadoop, there is a very good scope of transition from small data to big data in Data service industry. Data services can not only refer to big data, small data can also be part of it. Any data which is nearby or under or about to petabytes can be processed through Hadoop.
Key advantages of Hadoop like volume handing, fast processing and dealing with a variety of data can work for processing of small data. Let’s see, why we should use Hadoop to process small data:
To avoid system hanging
Small data also contains a variety of contents. In case this content gets into the system at a very fast speed then it can hamper query execution on MySql by hanging it. Hadoop can solve such situation when small data is processed through it.
To integrate a variety of Data Types in Traditional Application
Apart from Big data, there is a number of traditional applications where data comes from different sources such as Video files, Images, SNS data, Emails and logs generated by Web Server. For these applications, it is required to integrate these data too. Hadoop helps to integrate all these various data types at a faster speed.
Speed up the processing
Map- Reduce, one of the most important parts of Hadoop, process different data sets concurrently. This concurrent or parallel processing way can speed up the processing of small data too.
It results in improvements in case of data redundancy, and failure management. Map-Reduce can also be used for data transformation and batch processing. Hence, Hadoop helps in reducing the time of processing window of various processes.
Saving Cost and Efforts
While processing data in a traditional application, a number of servers and machines are required. If we have Hadoop to process small data for a traditional application then we can save lots of maintenance cost of this hardware and machines. Hadoop uses a number of commodity servers for processing on a cloud environment. It saves the cost of purchasing and installing the number of machines. Amazon’s EMR service works with Hadoop on the cloud is a more affordable and measurable manner.
So, instead of working with an infrastructure of small data only, just collectively use the advantages of big data framework with small data. Use of such a framework with small data approaches us from smaller to bigger things.
Why big data is not preferable always?
We can say that Big data is collecting and analyzing past data. It is a game of variety, velocity, and volume. Although big data is popular but small data has started to touch heights again. The reason behind this is few unneglectable aspects of big data. Big Data is not preferable always for all types of businesses due to its weak security feature.
Processing of data is one side of the coin and security of data is another important side. Security is an important fact of any data, which seems to be neglected in big data. For example, transaction data is very critical for any business. Security of transaction data is too important, big data can not serve that type of security to such data.
So, just to overcome these disadvantages of big data, Small data can take place of it. Small data already get maintained with applying required business logic and theorem over it. Hence, Big data and small data both have plus and minus points. We should use one’s positive to overcome another’s negative. This approach helps us to get something good for our business.
Although small data is different from big data, but it has few important aspects such as controlled and quality form of information. Hence, Small data can work together with the outstanding features of big data. i.e., variety, volume, and speed. In this digital world, small data can help to minimize the effort of making big data more valuable.
Conclusion
The most noteworthy is the integration of Small data and Big data. It can help to enrich our knowledge to innovate new method to smartly breakdown huge flows of information into small, short and meaningful flows. Most importantly, it helps to identify isolate the parts of our business where we need to work more. It may result in saving efforts required to utilize various resources of the organization in an effective way.
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