Introduction to Real-Time Data

By Memgraph

8 min readJanuary 1, 2019

Have you ever wondered how raw data from social media and marketing campaigns can benefit us? How can it be translated into useful information for better decision-making? You would think that this task is simple and can be performed by a team of data scientists. The data processing can be done by implementing machine learning algorithms and artificial intelligence. But this nature of processing involves some time delay and lack of high speed. The delays in decision-making processes can incur opportunity costs in business. The challenge can be solved if the businesses make decisions with immediate insights drawn from data.

Here comes the need for real-time data that can be processed in a small time interval to produce a near-instantaneous output. Let's first understand what real-time data is. It is the data that becomes available soon after its creation. It is not being stored; instead, it is delivered to the users when collected. This way, it helps in the timely decision-making process without any lag. The real-time data finds most of its applications in businesses as they use it to manage products, optimize operations, and improve customer service. Let’s get started by comparing the two types of real-time data.

Dynamic vs. Static Data

Before moving on to dynamic and static data structure, let’s first understand what is data structure. It is a specialized format to organize, process, retrieve and store data so it can be efficiently used for programming operations. Data structures enable you to access and work with the data in appropriate ways; you can get information about the data and understand the relationship between the data values.

Data structures are classified into the following three types based on their characteristics.

Homogeneous or Heterogeneous - This feature depicts whether all data items in a given repository have the same type or different types, respectively.

Linear or Non-linear - This characteristic feature explains the ordered and the unordered arrangement of data items such as within an array and a graph, respectively.

Static or dynamic - This feature tells us about the compilation of data structures.

Let’s understand the static and dynamic data structures.

Static Data Structure

Static data structure means the set of data having a fixed data size and memory at the compile time. For instance, consider the array below.

int a[5] = {3,2,5,7,8};

The array is an example of a static data structure as we can modify the content of its elements without changing the memory space allocated to them.

Dynamic Data Structure

We can use a dynamic data structure when the number of elements to be stored is unknown. It has a variable data size, structure, and memory location, which means we can expand or shrink the data structure depending on the storage use. It is preferred to set a maximum size of data to avoid the problem of memory collision.

For instance, stacks and linked lists are examples of dynamic data structures as we can add or remove elements in them at runtime.

Dynamic vs Static Data Structure

Dynamic Data Structure	Static Data Structure
A dynamic data structure is efficient as the size can be randomly updated during runtime.	A static data structure is less efficient as its size can not be changed during runtime.
It is flexible, but we had to make a little effort to access the data elements.	It is less flexible compared to a dynamic data structure, but we can easily access its elements.
As the structure is dynamic, it can overflow and exceed the allowed limit.	The memory can get wasted if we do not know the number of elements that need to be stored in a static data structure.
These structures take more time to load and develop, but they are excellent for handling complex functionality.	These structures take a short time to develop and have faster loading times. But they are not interactive and are unable to perform complex functionalities.
It is difficult to program, but it makes efficient use of memory.	It is easy to program, but we need to estimate the maximum amount of space that will be required.

Examples of Real-Time Data and Data Sources

Multiple industries use real-time data to improve their performance and results. Below are some use cases showing the significance of real-time data.

Healthcare - Healthcare equipment like glucometers and blood pressure monitors, have sensors that check a patient's essential body functions and vitals. This equipment effectively monitors remote patients and provides immediate insights that improve patients' experiences and outcomes.

Social Media - The need to real-time monitor social media posts is increasing at an unprecedented rate with the cries of bullying and fake news. It is essential to take action on fake and offensive news. Therefore, social media platforms are creating tools that can process voluminous amounts of data in real-time and effectively prevent bullying.

Retail - With the use of real-time data, brick-and-mortar retail stores engage their customers and offer experiences that speak to their needs and preferences. Real-time insights help in:

Gaining trend insights
Location-based marketing
Improving operational efficiencies such as product freshness and product movement

The companies have a better chance to be successful if they understand and respond immediately to what the customer wants in micro-moments.

Transportation and Supply-Chain - Real-time data is important in making fleet operations effective and efficient. It powers the internet of trains and makes autonomous vehicles safer.

Finance - In an organization's finance department, it is crucial to understand and act on real-time information as it helps in performing financial functions by:

Processing transactions
Identifying fraudulent actions

Predictive maintenance - Real-time data can identify maintenance issues prior to breakdowns or system failures. This way, the companies can save their time, money, and other catastrophic effects on the business. In modern enterprises, real-time data monitors airplanes and trucks' performance to predict issues with complex manufacturing equipment.

Below are some free real-time data sources that you can use to get data insights.

Crypto - It offers a free API that allows you to get all pricing information on-demand.
Block-chain - It provides an API for getting notifications on blocks and transactions for Bitcoin.
Twitter - You can use the official Twitter API to access a part of its stream.
Reddit - You can use the real-time Reddit API to pull down comments and posts.

Ways To Do Data Processing

Below given are some of the ways to process data.

Real-Time Data processing - You use it when you need the information to be processed immediately, such as when using an ATM.

Near Real-Time Data processing - It is used when speed is essential, but you don't need it instantly, such as to produce operational intelligence.

Batch Processing - It is used when you can wait for several days for processing. Payroll is an excellent example of batch processing.

Let's delve into the steps involved in real-time data processing.

Real-Time Data Processing

Real-time processing is a system that processes data when it is acquired and produces a near-instantaneous output. This type of data processing needs the following:

Continual input
Constant processing
A steady output of data

The primary purpose of real-time data processing is to take raw data from marketing campaigns, social media, and other sources to translate it into useful information for better decision-making.

Steps Involved in Real-Time Data Processing

Converting the raw data into actionable insights involves the following six steps.

Collection - Data collection is the first step in the processing cycle. Data is collected from online databases, warehouses, data lakes, and other sources.

Preparation - In this step, the data is cleansed to remove the duplicate, inaccurate, missing, and corrupt values. It is converted into a suitable format for further analysis.

Input - In this step, the raw data is converted into a machine-readable format to be fed into the processing system.

Processing - This step uses Artificial Intelligence and Machine learning algorithms to process the raw data and generate the desired output.

Output - In this step, you pass the processed data to the user in a readable form such as audio, video, or documents.

Storage - You can retrieve the data when needed, or it can be used as input in the subsequent data processing cycle.

Real-time data processing is used in:

Bank ATMs
Radar systems
Customer Service systems
Data streaming

Conclusion

Real-time data processing is ideal for processing large amounts of data and immediately provides insights from the updated data. It can quickly anticipate security threats and other infrastructure issues in organizations before they significantly affect customers. When coupled with analytics tools and unified monitoring, real-time data provides teams with a comprehensive view of their environment. It can also give administrators insight into system health and other weaknesses. In short, real-time data is used everywhere to make better and informed decisions.