Real-time Big Data Analytics: The Complete Guide

Luke Smith
Enterprise Solutions Architect
April 1, 2023
Matt Tanner
Developer Relations Lead
April 1, 2023
Luke Smith
Enterprise Solutions Architect
April 1, 2023
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With large volumes of data comes the need to be able to store, process, and analyze it. Over the last decade, big data platforms have become a cornerstone for many businesses large and small. The volume of information generated and collected by enterprises in recent years continues to skyrocket in leaps and bounds. With this, big data initiatives are on the increase with the end goal of making business decisions easier and more profitable. The gap between the data and the business insights sits within analytics. With analytics, data can be shaped into insights and actionable output. Without it, data is merely data and its uses are likely limited from a business standpoint.

Table of Contents

Since analytics are so essential, a flurry of solutions has come forth in the last while. Everything from hardcore data science tools that require a Ph.D. to operate effectively through to simple tools that anyone can operate and generate profound findings. Regardless of which tool or path is chosen, this article will cover the path to implementing big data analytics, including the different advantages and limitations. At a higher level, we will also cover the basics of what big data analytics consists of and some of the technologies that make up this segment of analytics. Let’s dive in!

What is Real-Time Big Data Analytics?

Real-time big data analytics is the process of analyzing large volumes of data in real-time to identify patterns, trends, and insights that can be used to make better decisions. This type of analytics is used in a variety of industries, including finance, healthcare, retail, and transportation. Big data analytics use massive amounts of data that are housed within big data platforms such as Databricks or Google Big Query. Sometimes you’ll also see Apache Hadoop or Spark used as part of a big data stack.

Within big data analytics, there are a few different approaches as to how they can be applied. Generally, these are divided into two different classes: continuous analytics and on-demand analytics. Continuous analytics is a form of real-time analytics where data is analyzed as it is generated. This approach involves the use of streaming analytics technologies, which enable organizations to process and analyze data in real time as it flows into their systems. On the other hand, on-demand analytics involves querying data on an as-needed basis, often in response to specific business questions or requests. In the past, on-demand analytics were the most used since continuous analytics requires the ability to stream data continuously. Data streaming, at big data levels, has only really become possible at scale within the last few years, whereas on-demand analytics is supported even in many legacy analytics systems.

On top of differentiating between continuous and on-demand analytics use cases, there are also four different types of analytics. The four types of analytics are descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. The type of analytics you require will depend on the problem you plan to solve or the business goals you wish to achieve. Let’s look at all four of these types in greater detail.

Descriptive Analytics

This type of analytics involves analyzing past data to gain insights into what happened and why. Descriptive analytics is used to identify patterns, trends, and relationships in historical data. Examples of descriptive analytics include sales reports, customer behavior analysis, and social media monitoring.

Diagnostic Analytics

This type of analytics involves analyzing data to determine why something happened. Diagnostic analytics is used to identify the root cause of a problem or to understand the factors that contributed to a particular outcome. Examples of diagnostic analytics include root cause analysis, customer churn analysis, and A/B testing.

Predictive Analytics

This type of analytics involves using historical data to make predictions about future events or trends. Predictive analytics is used to forecast future demand, identify potential risks, and optimize business processes. Examples of predictive analytics include demand forecasting, risk analysis, and customer lifetime value prediction.

Prescriptive Analytics

This type of analytics involves using data to determine the best course of action to take in a particular situation. Prescriptive analytics is used to optimize business processes, improve decision-making, and reduce costs. Examples of prescriptive analytics include resource optimization, dynamic pricing, and supply chain optimization.

With the above knowledge, you now have an idea of what big data analytics are. The next step is for us to explore how real-time big data analytics actually work.

How Do Real-Time Big Data Analytics Work?

How real-time big data analytics work is a crucial part of understanding how to implement and apply the benefits associated with them. Real-time big data analytics works by ingesting and processing large volumes of data in real-time using specialized software tools and technologies. More than just the output of insights or actions, this type of analytics requires multiple layers or steps to achieve. The process typically involves data ingestion, processing, analysis, and finally visualizations. Of course, certain use cases may omit certain steps but this would be the general pattern. Let's look at each component in more detail.

Data Ingestion

Real-time data is ingested from various sources, such as sensors, IoT devices, social media feeds, and transactional systems. This data may be integrated using native tools or potentially with the help of a third-party tool, such as Arcion. This is the step in which data actually gets moved into the system where the analytics will be performed.

Data Processing

The ingested data is processed using streaming analytics tools, which apply real-time analytics algorithms to extract insights and detect patterns as the data is flowing. This step may include transformations and other processes that get the data into the correct format. Depending on the use case, some insights may even be derived at this stage.

Data Analysis 

The processed data is analyzed using machine learning models and statistical techniques to identify trends, anomalies, and patterns that can be used to make informed decisions. This is the step in the process where the deepest insights are usually extracted.

Data Visualization

In certain use cases, it may make sense to visualize the analyzed data to make it more digestible. The results of the analysis can be presented in dashboards and visualizations, which enable business users to understand the insights and take appropriate action easily. Although not a mandatory step for analytics, dashboards, and visualizations usually make insights more accessible.

To implement real-time big data analytics, organizations need to have a robust data architecture that can handle large volumes of data and support real-time processing. This architecture typically includes distributed data storage, such as Hadoop or cloud-based data warehouses, and real-time data processing frameworks, such as Apache Kafka, Spark, or Flink. In most cases, organizations need to also have skilled data scientists and analysts who can design and implement real-time analytics models and interpret the results. Of course, there are also platforms that make real-time big data analytics more accessible as well which makes the assistance of a data science team less crucial for garnering insights.

What Are The Technologies Used in Big Data Analytics?

After looking at some of the components of a real-time big data analytics process, it makes sense to dig into some of the specific technologies that can be used to implement one. Real-time big data analytics involves the use of various technologies that enable the processing and analysis of large volumes of data in real time, some of which you may already be familiar with. Below are some key technologies used in real-time big data analytics.

Apache Kafka

One of the most popular data streaming platforms in existence, Apache Kafka is a fundamental technology for many companies that implement real-time analytics. Kafka is a distributed streaming platform that allows for real-time data ingestion and processing. It can handle large volumes of data and provides low-latency processing capabilities. Some companies have also built on top of the Kafka platform to augment its capabilities. Some examples of companies that provide Kafka-based solutions include Confluent, AWS Kinesis, and Azure Event Hubs.

Apache Spark

Apache Spark is another common component in real-time analytics stacks. Spark is a distributed computing engine that provides real-time data processing capabilities. It supports both batch and stream processing and can be used for various use cases, including machine learning, graph processing, and SQL analytics. Many platforms and services leverage Spark under the hood. Some examples of Spark-based solutions include Databricks, Cloudera, and IBM.

Apache Flink

Apache Flink is a real-time processing framework that provides high-throughput, low-latency data processing capabilities. Like Apache Spark, Flink supports both batch and streaming processing and can be used for various use cases, including fraud detection, predictive maintenance, and real-time analytics. Some examples of companies that provide Flink-based solutions include Ververica, AWS Kinesis Data Analytics, and Google Cloud Dataflow.

NoSQL Databases

For many analytics solutions NoSQL databases are an integral part of the technology stack. NoSQL databases such as MongoDB, Cassandra, and HBase are often used in real-time big data analytics to store and query large volumes of data. These databases provide horizontal scalability, high availability, and low-latency querying capabilities, making them ideal for real-time analytics use cases.

Machine Learning Platforms

Due to the massive amount of data and possible trends within it, machine learning is heavily leveraged as part of a modern real-time analytics stack. When it comes to big data analytics, machine learning platforms such as TensorFlow, PyTorch, and scikit-learn are often used to build and deploy machine learning models. These platforms provide tools and libraries for data preprocessing, model training, and inference. As a common component in an analytics stack, machine learning platforms usually are integrated with real-time data processing frameworks mentioned earlier, such as Kafka or Spark. As interest grows in machine learning, industry giants like Google, AWS, and Microsoft and smaller players have created a vast array of tools and services that make machine learning accessible for analytics use cases.

As we explored above, real-time big data analytics involves the use of various technologies. These technologies include distributed streaming platforms, processing frameworks, NoSQL databases, and machine learning platforms. These technologies provide the necessary tools and infrastructure for building scalable and reliable real-time analytics solutions.

Advantages of Real-Time Big Data Analytics

Now that you know what real-time big data analytics is and what technologies are required to implement it, it’s time to dig into some of the selling points for moving forward with it. Real-time big data analytics has several advantages that make it an attractive solution for organizations looking to gain insights from their data quickly. Some of the key advantages of real-time big data analytics can lead to massive upsides for the companies that move forward with implementing such as solution.

Faster Decision-making

Real-time big data analytics enables organizations to make faster, more informed decisions based on real-time insights. For example, a financial institution may use real-time analytics to detect fraudulent transactions in real-time, enabling them to stop the fraud before it can do any damage.

Improved Operational Efficiency

For some organizations, real-time big data analytics can help improve their operational efficiency by providing real-time insights into their processes. For example, a manufacturing plant may use real-time analytics to monitor machine performance and detect any anomalies, enabling them to take corrective action before any damage occurs.

Better Customer Experience

Especially for customer-facing businesses, real-time big data analytics can help to deliver better customer experiences by providing real-time insights into customer behavior and preferences. For example, an e-commerce company may use real-time analytics to personalize the shopping experience for each customer, based on their browsing and purchase history.

Increased Revenue

Real-time big data analytics can help organizations increase revenue by enabling them to identify new revenue opportunities and optimize their pricing strategies. For example, a retailer may use real-time analytics to analyze customer behavior and adjust prices in real-time to maximize revenue.

Improved Risk Management

For companies looking to manage risk more effectively, real-time big data analytics can provide real-time insights into potential risks and threats. For example, a cybersecurity company may use real-time analytics to detect and respond to cyber threats in real time, enabling them to prevent or minimize the impact of an attack.

Overall, real-time big data analytics provides organizations with the ability to quickly and effectively gain insights from their data. The above advantages are by no means an exhaustive list. Most companies would have a tough time denying that even the short list of advantages above would not be worth the investment into this type of analytics.

Limitations in Real-Time Big Data Analytics

As always, there will always be a downside or limitations to a given set of technologies. In the case of real-time big data analytics, there are some limitations that organizations should be aware of. Some of the key limitations of real-time big data analytics are summarized below.

Complexity

Real-time big data analytics requires expertise in multiple areas, including data engineering, distributed systems, and machine learning. This can make it challenging for organizations to implement and maintain real-time analytics solutions. For example, a small business may lack the resources to hire data scientists and engineers to build and maintain a real-time analytics system. The organizational and technological complexity of implementing this type of analytics can be steep.

Cost

On top of complexity, or due to it, real-time big data analytics can also be expensive to implement and maintain. This is particularly true if organizations need to invest in specialized hardware or software to implement it. For example, a financial institution may need to invest in high-end servers and storage systems to handle the volume of data generated by real-time analytics applications. For some organizations, this budget requirement may make the solution out of reach.

Data Quality

Real-time big data analytics requires high-quality data that is accurate, complete, and up-to-date. If data quality is poor, it can result in inaccurate insights and decisions. These decisions could lead to revenue loss or even customer or user loss, depending on the use case. For example, a retailer may use real-time analytics to adjust prices based on customer behavior, but if the data is inaccurate or incomplete, it may lead to incorrect pricing decisions.

Security and Privacy

A requirement for many organizations is to handle data with security and privacy in mind. Especially at scale, it can be hard to handle large volumes of sensitive data in real time, posing security and privacy risks. For example, a healthcare provider may use real-time analytics to monitor patient data. Still, if the data is not properly secured, it can be vulnerable to data breaches and other security threats. In highly-regulated environments, this could lead to a loss of user confidence and even legal issues.

Scalability

If the analytics stack is not scalable, it can result in performance issues and delays. For example, a social media platform may use real-time analytics to monitor user behavior, but if the system cannot scale to handle the volume of data generated by millions of users, it may result in delays in processing and analyzing the data. At this point, only a small fraction of the insights from the analytics may be relevant or even useful.

Overall, organizations need to carefully consider the limitations of real-time big data analytics and weigh them against the potential benefits before investing in a real-time analytics solution. By tailoring the technology stack used to implement the solution, some or all of these limitations may be overcome.

Real-Time Big Data Analytics Processing Examples

Although some examples of how real-time big data analytics have been covered, exploring some of them a little further may help to understand where your organization may benefit from such an analytics solution. Here are some examples of different industries and use cases for real-time big data analytics, along with their benefits and challenges:

Finance Industry: Real-time Fraud Detection

In the finance industry, real-time big data analytics can be used to detect fraud in real-time. By analyzing large volumes of transaction data in real time, organizations can identify suspicious patterns and anomalies that may indicate fraud. Real-time fraud detection can help organizations prevent financial losses and protect their customers' financial information.

Benefits:

  • Faster detection of fraudulent activity
  • Real-time response to fraudulent transactions
  • Reduced financial losses

Challenges:

  • High volume of transaction data to process and analyze in real-time
  • Need for high accuracy to avoid false positives and false negatives
  • High cost of implementing and maintaining real-time fraud detection systems

Interested in learning more? Read our blog Leveraging Change Data Capture for Fraud Detection using Arcion Cloud and Databricks.

Manufacturing Industry: Real-time Equipment Monitoring

Real-time big data analytics can be used in the manufacturing industry to monitor equipment performance in real-time. By analyzing sensor data from machines in real time, organizations can detect any anomalies or issues that may indicate equipment failure or the need for preventative maintenance. Real-time equipment monitoring can help organizations avoid unplanned downtime, reduce maintenance costs, and improve overall equipment efficiency.

Benefits:

  • Reduced unplanned downtime
  • Lower maintenance costs
  • Improved equipment efficiency

Challenges:

  • High volume of sensor data to process and analyze in real-time
  • Need for high accuracy to avoid false positives and false negatives
  • Integration with existing manufacturing systems and processes

Retail Industry: Real-time Personalized Marketing

In the retail industry, real-time big data analytics can be used to deliver personalized marketing in real-time. By analyzing customer data in real-time, organizations can tailor their marketing messages and offers to each customer's individual preferences and behaviors. Real-time personalized marketing can help organizations improve customer engagement, increase sales, and build brand loyalty.

Benefits:

  • Improved customer engagement and loyalty
  • Increased sales and revenue
  • Real-time response to customer behavior

Challenges:

  • Need for high-quality and up-to-date customer data
  • Need for accurate and timely analysis to avoid irrelevant or incorrect marketing messages
  • Integration with existing marketing systems and processes

Interested in learning more? Read this featured piece on DZone, Change Data Capture to Accelerate Real-Time Analytics.

Healthcare Industry: Real-time Patient Monitoring

Real-time big data analytics can be used in the healthcare industry to monitor patients in real-time. By analyzing patient data in real-time, healthcare providers can detect any changes in patient condition or health status and take immediate action. By enabling automation, real-time patient monitoring can help healthcare providers improve patient outcomes, reduce hospital readmissions, and lower healthcare costs.

Benefits:

  • Improved patient outcomes and experience
  • Reduced hospital readmissions
  • Lower healthcare costs

Challenges:

  • Need for high-quality and up-to-date patient data
  • Integration with existing healthcare systems and processes
  • Privacy and security concerns related to patient data

Overall, real-time big data analytics can provide significant benefits to organizations across a variety of industries. However, implementing real-time analytics solutions can also present significant challenges, such as processing large volumes of data in real time, ensuring high data quality and accuracy, and integrating with existing systems and processes.

Conclusion

The introduction of real-time big data analytics has given organizations the ability to get actionable and thoughtful insights in real-time to take steps that will lead to profitable business outcomes. This article has covered a lot of ground about real-time big data analytics, including what it is, how to implement it, its advantages and disadvantages, and a wide array of use cases.

When it comes to real-time big data analytics, a third-party tool can be the best way to fast-track your implementation and simplify your technology stack. Arcion aims to utilize real-time big data analytics to help businesses grow and get relevant insights from their data. Arcion offers limitless scalability, zero downtime, and guaranteed consistency for your data, checking off many of the boxes in the tech stack for enabling real-time big data analytics. To get started today, contact our real-time data experts and implement robust real-time big data analytics with ease.

Matt is a developer at heart with a passion for data, software architecture, and writing technical content. In the past, Matt worked at some of the largest finance and insurance companies in Canada before pivoting to working for fast-growing startups.
Matt is a developer at heart with a passion for data, software architecture, and writing technical content. In the past, Matt worked at some of the largest finance and insurance companies in Canada before pivoting to working for fast-growing startups.
Luke has two decades of experience working with database technologies and has worked for companies like Oracle, AWS, and MariaDB. He is experienced in C++, Python, and JavaScript. He now works at Arcion as an Enterprise Solutions Architect to help companies simplify their data replication process.
Luke has two decades of experience working with database technologies and has worked for companies like Oracle, AWS, and MariaDB. He is experienced in C++, Python, and JavaScript. He now works at Arcion as an Enterprise Solutions Architect to help companies simplify their data replication process.
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