“The amount of data dealt with in the worlwide society will expand explosively by a factor of 200.”

Big Data is flooding everywhere, and companies require systems to analyze and understand this data as soon as they acquire it.

Achieving a high rate of Data Ingestion, Data Processing, and the display of data for analysis requires the use of Real-time Processing Systems. 

In this quick-start guide, we cover all the aspects of Real-time Data Processing. We will discuss the benefits, architecture, and use cases of real time data processing, Stream Processing (also known as Real-time Streaming Analytics), and lastly discuss Batch vs Real-time Processing.

What Is Data Processing?

Real-Time Processing: Real-time Processing | Hevo Data
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When you process data, you convert the acquired data into a usable form so that it’s easier to analyze and draw conclusions. In most companies, data scientists and engineers are often in charge of gathering and translating this data.

Companies all around the globe employ Digital or Electronic Data Processing Methods, often known as EDP, to process data using machines, computers, workstations, servers, modems, and processing software programs.

These tools generate outputs in the form of graphs, charts, images, tables, audio, video extensions, vector files, and other desired formats such that it becomes absorbable for everyone.

On the whole, Data Processing is a six-step process that involves raw data collection, preparation, sorting, processing analysis, output, and storage.

If you would like to have a deep dive into these six stages of Data Processing, we have covered them separately in this guide – What is Data Processing? – A Comprehensive Guide.

What Is Real-Time Processing?

A machine or a system that works in real-time takes your input, processes it, and yields a meaningful output in no time (order of milliseconds).

Real-time Processing is a continual input constant Data Processing of incoming data from various Data Sources with very short latency.

As companies acquire more and more data, the need to analyze this information, more so quickly to be able to create a competitive advantage is becoming imperative.

Numerous industries from Network Monitoring, Cybersecurity, and Banks to E-commerce require Real-time Data Processing to detect frauds, monitor daily transactions, and spot potential growth opportunities. 

A real-life example of Real-Time Processing is when you travel from destination A to destination B using Google Maps.

Google Maps automatically updates traffic congestion levels using information acquired from various mobile devices and road sensors in that area to suggest to you the optimal & shortest path to reach your destination. 

Banks also process millions of customer transactions daily. Incorporating Real-time Data Processing mechanisms in their transactional logging framework helps them ensure that legitimate transactions get approved, and fraudulent transactions are detected. 

Some of the most popular and well-established Real-time Data Processing tools are Apache Spark, Apache Kafka, Amazon Kinesis, Apache Samza, Apache Flume, Azure Stream Analytics, Azure Stream Analytics, IBM Streaming Analytics, Google Cloud Dataflow, Apache NIFI, and Apache Storm.

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Real-Time Data Processing Advantages

Real-time Processing: Real-time Processing Advantages | Hevo Data
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Here are the advantages offered by Real-time Data Processing Systems:

  • Immediate Updating of Databases & Immediate Responses to User Inquiries: Since there are no delays in processing, Real-time Processing Systems ensure timely action.
  • Up-to-Date Applications: For scenarios, where you require a high frequency of changes to your applications, Real-time Processing ensures that new records are synchronized in a short time.
  • Breakneck Speed: With Real-time Systems, you receive rapid data loading and storage capabilities, allowing you to churn your data and draw insights quickly.
  • Accurate Information: Your data is never out of date. Everything is current and correct when it is done in real-time.
  • Business Agility: Enabling faster and smarter responses to a changing business environment is easy when you have up-to-date data to match the market’s urgency and customers’ shifting preferences.
  • Quick Detection of Operational Issues: Real-time reports keep you posted on your current business operations for quick detection of bottlenecks and pertaining issues.

Real-Time Processing Architecture

A Real-time Data Processing Architecture comprises the following four components:

  • Real-time Message Ingestion: Message ingestion systems ingest incoming streams of data or messages from a variety of sources to be consumed by a Stream Processing Consumer. This service might be built as a simple data store that stores new messages in a folder. However, in many cases, a Message Broker is required to function as a buffer for the messages, which can support scale-out processing and reliable message delivery.

For more information on some of the most popular Message Broker Platforms, visit our helpful guide – Popular Message Broker Platforms for 2022.

  • Stream Processing: Stream processors process the ingested messages by performing Data Processing operations such as filtering, aggregation, or preparing them for Data Analysis.
  • Analytical Data Store: Analytical Data Store specializes in big-data preparation and management. They prepare data for analysis before serving it in a structured manner so that it can be queried using analytical software. Analytical Data Stores are also optimized for quick query response times and advanced analytics. 
  • Analysis and Reporting: The final step in Real-time Data Processing is to prepare charts, reports, or graphs and provide actionable insights that are readily available and comprehensible to all.
Real-time Data Processing Architecture: Real-time Processing | Hevo Data
Image Source: Microsoft

What Is Batch Processing?

Batch Processing: Real-time Processing | Hevo Data
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Batch Processing, as the name suggests, is the processing of data in bulk. Batch Processing, instead of working in real-time, collects transactions over some time and schedules their processing at some later point.

Post-processing, it displays the results to a software application or a system from which data scientists, analysts, or engineers can analyze data to make sound decisions. 

Unlike Real-time Processing, Batch Processing is characterized by more operational flexibility and a slower reaction to changing market conditions.

A simple example to help you understand how Batch Processing works is when you use washing machines.

To wash clothes in a washing machine, you would wait for a cloth pile to build up and then wash your clothes. This mode of operation saves you operational costs and is an efficient way to process your business data.


  • Involve simple systems that don’t require special hardware or system support.
  • Efficient processing of large volumes of data.
  • Works offline.
  • Capabilities like automation and minimal user involvement.
  • Low maintenance.


  • Limited in scope and capability.
  • No real-time updates.
  • Debugging Batch Processing systems is complex.
  • May require dedicated staff to handle issues.

Real-Time Processing vs Batch Processing

Aligning all of our understanding of real-time processing and batch processing in one table, we get this contrast:

ParameterReal-time ProcessingBatch Processing
Time FrameReal-time systems have predictable response times. They ingest and process transactions in order of milliseconds.Batch Processing record transactions and process them at a specified interval.
Processor AvailabilityReal-time Data Processing demands no time delays, and therefore the processor has to be responsive at all times.Batch Processing schedules the processing of transactions, so the processor needs to be only available when there is a requirement.
OrientationReal-time Processing is action or event-oriented.Batch Processing is measurement-oriented.
Data Handling CapacityReal-time Data Processing is useful for modest volumes of data. Processing large data volumes requires a significant amount of computation time and processing capability.Batch Processing is used for large volumes of data. 
Resources InvolvedReal-time Processing needs high computer architecture and high hardware specifications.Batch Processing can work with normal computer specifications.
Costs of ImplementationReal-time Stream Processing is complex and costly. It requires a unique combination of hardware and software to deliver speedy outputs. Batch Processing is the simplest processing method for business applications. It is economical as well.
MaintenanceReal-time Stream Processing is an intricate setup that requires daily updates and backup solutions to regularly receive and supply data. Batch Processing is less intricate and easy to manage compared to Real-time Stream Processing.
ExamplesATM TransactionsData StreamingIoT Sensor or Radar SystemsCustomer Service SystemsProcessing of PayrollsCreation of Billing CyclesCustomer OrdersCredit Card Transactions

Use Cases of Real-Time Data Processing & Analytics

Business Operations

Real-time Systems provide your business with strategic insights in the areas of Finance, Sales, Marketing, and Customer Service, allowing you to run your back-office activities as efficiently as possible.

Real-time Analytics aids in the detection of faults and the reduction of operational risks that might jeopardize your business operations. 

Financial Trading

Inherent to traders is the ability to forecast stock price rises and falls. If you don’t, you’ll lose money.

Your business’ Financial Analysts and Traders who have access to Real-time Analytics can benefit from the most up-to-date information on financial market circumstances.

To acquire a wide perspective on the market, they can sync and receive data from news, weather reports, databases, and social media. This viewpoint will help them make the greatest and most informed trading decisions possible.

Customer Support

Real-time Systems synchronize your customer interactions and their experiences. Because your Support Team is in sync with your customer engagements, they can start up where the discussion left off.

This kind of attention to customer queries produces a good impression of the business and lays the groundwork for a stronger and more loyal customer base.

Marketing Campaigns

Using Real-time Data Processing, your Marketing Teams get direct access to the frequency of clients’ purchases in their Marketing Automation Solution.

They can create more powerful campaigns and practical roadmaps, using the information on users’ app usage, location, age, job title, and a variety of other parameters.

This approach targets your audience correctly and helps create engaging customer experiences. 

Sales Initiatives

Using Real-time Data Processing, your Sales Reps can extract data about interested prospects, operationalize it in their Salesforce/HubSpot customer database, and contact them on time.

This increases your sales and boosts conversions, in turn helping drive more revenue to your business.

What Is Stream Processing?

Stream Processing: Real-time Processing | Hevo Data
Image Source: Atomiv

Stream Processing is a Big Data technique that involves ingesting a continuous data stream and analyzing, filtering, transforming, or improving the data in real time. 

Real-time Stream Processing systems have time constraints associated with processing data as it flows through the system from source to destination. 

A Stream Processing System is made up of a number of modules that work in parallel and communicate through channels.

These modules can be Source Capturing (passing data from a source into the system), Filters (doing atomic actions on the data), or Sinks (that either consumes the data or pass it out of the system).

Stream Processing Tools and Technologies come in several forms, including Distributed Publish-Subscribe Messaging Systems like Kafka, Distributed Real-time Computing systems like Apache Storm, and Streaming Data Flow Engines like Apache Flink.

Tools For Real-Time Processing

Real-time Data Processing has set its foot in many industries, and it’s continuing to spread its roots in many more.

As the field of Real-time Stream Processing and Real-time Analytics is developing, and the need for Real-time Operationalization in businesses, more and more tools are coming into the market to make business operations easier. 

Real-time Data Processing Solutions help companies launch more successful Marketing Campaigns, better their Sales, and allow their products and services to reach a wider audience.

Handling modest volumes of data and being able to generate actionable insights quickly is one of the many advantages Real-time Data Processing Solutions come with.

These solutions make it easier to integrate Real-time Data (RTD) into your system so that your data can be processed and evaluated in Business Intelligence or Real-time Data Analysis Systems.

As a result, the solutions must be reliable in their operation and respond quickly and accurately. There are a variety of options on the list, including

  • Apache Spark
  • Apache Kafka
  • Apache Flink
  • Amazon Kinesis
  • Azure Stream Analytics
  • Google Cloud Dataflow
  • RabbitMQ, and many more.

To gain in-depth knowledge about these tools and their comparison, you can visit Best Data Stream Processing Systems for 2022.

Challenges to Real-Time Processing

Real-time Processing faces two big challenges:

  • High-Volume Rapid Ingestion: The ability to process data in real-time at a rapid pace is possible only for modest volumes of data. Ingesting, processing, and storing large volumes of data in real-time is a difficult undertaking, which must be implemented in such a way that there is no congestion in Data Ingestion Pipeline. Moreover, the Data Store must also support high-volume writes to be able to process data quickly.
  • Speedy Analytics (also called Operational Analytics): The capacity to create Business Intelligence in real-time is the second barrier when deploying Real-time Processing Systems. Real-time Systems must create alerts or reports in real-time for business employees as soon as data from the source becomes accessible so that they can take action quickly.

Learn more about: MongoDB to BigQuery


You might think of Real-time Data Processing as a lucrative option for your business. But choosing the right method of Data Processing, for the most part, depends on your business requirements.

In some cases, you might prefer a cost-effective, batching approach for effective Data Processing where your operational systems don’t need real-time sync. In other cases, or when you are working in banking, networking, or cybersecurity, Real-time Data Processing is the option you should go for. 

For most companies, establishing a Single Source of Truth (SSOT) and Real-time Data Migration capabilities between applications is also crucial.

While building an in-house Data Pipeline Solution can get demanding, Hevo Data comes to simplify all your Data Transfer and Data Transformation needs. 

Hevo Data is a No-Code and Zero Data Loss Solution that supports Data Ingestion from multiple sources be it your frequently used databases and SaaS applications like MySQL, PostgreSQL, Salesforce, Mailchimp, Asana, Trello, Zendesk, and other 150+ Data Sources. Hevo migrates your data to a secure central repository like a Data Warehouse in minutes with just a few simple clicks.

Using Hevo is simple, and you can set up a Data Pipeline in minutes without worrying about any errors or maintenance aspects. Hevo also supports advanced data transformation and workflow features to mold your data into any form before loading it to the target database.

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Hevo lets you migrate your data from your favorite applications to any Data Warehouse of your choice like Amazon Redshift, Snowflake, Google BigQuery, or Firebolt, within minutes to be analyzed in a BI Platform.

Why not try Hevo and see the action for yourself? Sign Up here for a 14-day free trial and experience the feature-rich Hevo suite firsthand. You can also check out our unbeatable pricing plans to choose the best-matched plan for your business needs. 

Post your questions or comments in the comment box below. We would be delighted to assist.

Divyansh Sharma
Marketing Research Analyst, Hevo Data

Divyansh is a Marketing Research Analyst at Hevo who specializes in data analysis. He is a BITS Pilani Alumnus and has collaborated with thought leaders in the data industry to write articles on diverse data-related topics, such as data integration and infrastructure. The contributions he makes through his content are instrumental in advancing the data industry.

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