Real-Time Data Processing: Architecture, Tools & Examples

Data processing is a procedure for transforming raw data into a usable format for real-life applications. It involves a series of steps, such as data collection, refinement, and storage. There are various ways of processing data, such as batch and real-time processing. While batch processing results in latency, real-time processing facilitates fast data operations, insight generation, and decision-making. 

This article provides an in-depth explanation of real-time data processing. It guides you through architecture, challenges, applications, and tools for real-time data processing. You can leverage this article to improve the operational efficiency of your enterprise and gain a competitive advantage in your industrial domain. 

What is Real-Time Data Processing?

Real-time data processing is a technique of instantaneous ingestion, transformation, storage, and analysis of data as soon as it is generated. It is a preferred method for performing faster data operations with latency in the range of milliseconds. You can leverage real-time data processing for accelerated data analysis and business insights generation.

A common real-time data processing example is the product recommendation algorithm of e-commerce websites. Here, you can collect data on customer behavior, such as their browsing history, search queries, clicks, purchases, or add-to-cart actions, immediately when they are navigating your e-commerce portal. You can then analyze these data to recommend more personalized product options to your customers in real-time. 

How Real-Time Processing Works?

Real-time data processing consists of the following series of steps:

Data Collection

The first step in real-time data processing is instant data ingestion. This is done by collecting data from various sources, such as server logs, IoT devices, or social media feeds. You can ingest this data with the help of data streaming tools like Apache Kafka or Amazon Kinesis. 

Data Processing

Here, the ingested data is aggregated, cleaned, transformed, and enriched. This converts it into a format suitable for other data systems and applications.

Data Storage

After processing, you can store your data in destination systems such as relational databases, streaming platforms, or in-memory databases. 

Data Distribution

You can distribute the processed data across various systems to make it accessible for downstream operations. 

Benefits of Real-Time Data Processing

Some of the advantages of real-time data processing are as follows:

Faster Decision Making

Real-time processing provides you with data insights quickly after analysis. These insights help you identify the parameters on which you are performing well and the factors that are negatively affecting your growth. You can use this information to speedily change your product features in accordance with the preferences of your target customers. This gives you an edge over your competitors, contributing to business growth. 

Enhanced Data Quality

During real-time data movement, you can quickly discover discrepancies in your datasets. Early detection helps you remove errors immediately and improve your data quality. Moreover, the errors are caught closer to their source in real-time data flow. This helps identify the root cause and resolve inaccuracies in data at an early stage. 

Elevated Customer Experience

In an enterprise, real-time data processing can help you exponentially improve the customer service experience. You can evaluate your customer data promptly and identify the loopholes discouraging people from buying your products or services. Such evaluations help you improve your product development and marketing strategies to increase customer engagement and revenue. 

Increased Data Security

You can quickly detect fraud or security breaches by monitoring and processing your data in real-time. This is especially useful in the finance sector or stock markets. You can also use real-time processing to identify early signs of negative trends that could impact market or stock prices. This allows you to take preventive actions beforehand to minimize potential losses. 

Real-Time Vs. Batch Vs. Near Real-Time Data Processing

Here is a tabular comparison of the real-time, batch, and near-real-time data processing:

Real-Time Processing Architecture

The real-time processing architecture can be classified as follows:

Lambda

Lambda architecture has a layered framework consisting of batch, speed, and serving layers. The batch layer enables you to store data in raw form, which is then processed in batches. You can store data using distributed file systems like Hadoop Distributed File Systems (HDFS). For batch processing your data, you can use tools like Apache Spark or Apache Flink. 

The speed layer enables the distributed processing of real-time data using stream processing tools like Apache Kafka or Apache Storm. The serving layer allows you to unify the outputs of both batch and speed layers. It acts as an intermediary between the end-user and the processed data. The serving layer uses a database such as Apache Cassandra or MongoDB to store processed data and query engines like Apache Hive to enable you to query the data. 

Kappa

Kappa architecture is simpler than Lambda architecture and can handle real-time processing in a more streamlined way. It consists of only one streaming layer. The Kappa architecture layer uses tools like Apache Kafka stream or Apache Flink to ingest and process data and store it in a database like Apache Cassandra. 

Delta

The Delta architecture enables you to combine and streamline both Lambda and Kappa's storage and processing capabilities through the micro-batching technique. This technique is an intermediary approach and forms the basis of data processing in many modern data lakes, like Delta Lake.  

Challenges with Processing Data in Real-Time

Some of the challenges of processing data in real-time are as follows:

Scalability

It can be challenging to process large volumes of data in real-time coming from sources such as social media feeds or financial transactions. This can lead to infrastructure overloading, reducing processing efficiency. Moreover, your infrastructure may not show flexibility according to an increase or decrease in data volume, resulting in over- or unnecessary usage of its resources. 

Data Quality

Real-time data comes from various sources in different formats, which may or may not be compatible with each other. Real-time processing also introduces technical glitches, sometimes resulting in missing data. All these irregularities contribute to the deterioration of data quality. 

Complexity

In real-time data processing, multiple tasks, such as ingestion, cleaning, transformation, and loading, are performed simultaneously. As a result, proper resource allocation for each of these tasks becomes a challenge. Historical analysis also becomes difficult in real-time processing as more emphasis is placed on current data. This affects the framing of long-term strategies in business. 

Security

While processing your data in real-time, there are risks of unauthorized access and data breaches. The reason for this is that in the rush to achieve faster processing, security protocols get compromised. To avoid this, you should secure the data pipelines through role-based access control, authentication, and encryption features. 

Costs

Real-time data processing requires specialized hardware and software infrastructure. This infrastructure is costly to buy and maintain, so processing data in real-time may cause financial strain. 

Use Cases for Real-Time Data Processing

Here are some real-life use cases of real-time data processing:

Finance

Real-time data processing is useful for detecting suspicious transactions and fraud in the finance sector. It also facilitates real-time analysis of stock market trends to enable you to take precautionary actions before potential monetary loss. 

E-Commerce

Real-time processing in e-commerce can help you comprehensively analyze customer behavior data. It helps you quickly relate purchasing behavior, search history, and preferences to give better product recommendations. You can also manage your inventory by continuously analyzing sales and inventory data. 

Healthcare

Processing health data records of a patient in real time helps in the early diagnosis and treatment of serious diseases. It also helps in monitoring and controlling public outbreaks of viruses. You can also use real-time data processing in inventory management of pharmaceuticals and medical equipment to ensure that there are no gaps in demand and supply. 

Communication

Real-time data processing has made swift communication with no latency possible. This is vital for customer service, where you can get solutions for your problems instantly through technologies like chatbots. Real-time data processing is integral to streaming services, delivering content without buffering. It is also an important part of shared workspaces, enabling real-time interaction and collaboration of teams for project completion. 

Real-Time Data Processing Tools

The real-time data processing tools are broadly categorized based on ingestion, storage, processing, and analysis functionality. Let us look at each of them in detail:

Real-Time Data Ingestion Tools

Real-time data ingestion tools enable you to collect data as soon as it is generated from various sources. Apache Kafka, Apache Nifi, Amazon Kinesis, and Wavefront are some examples of real-time data ingestion tools. 

Stream Processing Frameworks

Stream processing involves real-time processing of data streams. These streams collect and send data records into multiple systems simultaneously. Apache Spark, Apache Storm, Apache Samza, and Apache Flink are some examples of stream processing frameworks. 

Real-Time Data Storage

The real-time data storage process involves capturing and storing data immediately after its generation with low latency. There are some platforms that facilitate this, such as Apache Cassandra, Amazon DynamoDB, Firebase, and MongoDB. 

Real-Time Analytics

Real-time analytics involves instant data analysis to generate faster insights for quick decision-making. Many tools, such as Google Cloud Dataflow, Azure Stream Analytics, StreamSQL, and IBM Stream Analytics, facilitate real-time analytics. 

Cloud Or On-Premise for Real-Time Processing

You can remember the following things before opting for cloud or on-premise real-time data processing.

Cloud-based Real-time Data Processing

• Cloud-based services offer high scalability to handle massive amounts of datasets. 
• It provides flexibility to deploy and manage data-related operations anywhere and anytime. 
• Cloud-based real-time data processing is secured through encryption, multi-factor authentication, role-based access control, and regular audit mechanisms. 
• It is a cost-effective solution. 

On-Premise Real-time Data Processing

• On-premise data processing provides you with complete ownership and control over your data. 
• It facilitates highly efficient real-time processing because of the physical proximity of all the components of the infrastructure. 
• You can set up security protocols and role-based access control mechanisms to secure your data during on-premise real-time processing. 
• It is not a cost-effective solution. However, you can opt for an on-premise solution if you have the budget to invest in infrastructure or already have access to such infrastructure. 

How to Build a Real-Time Data Pipeline?

You can follow the below steps to build a simple real-time data pipeline:

Define the Objectives of the Data Pipeline

First, clearly define the objective behind building a data pipeline. The goal can be to perform data analytics, build machine learning models, or perform different data-related processes like data synchronization. 

Choose Data Sources

There are various real-time sources like server logs, IoT devices, social media feeds, trading data from the stock exchange, or GPS data. You should select the data sources from which you want to retrieve your desired data.

Real-time Data Ingestion

You can use different tools to ingest real-time data. Your data ingestion tool should be low-latent, scalable, and fault-tolerant to handle large volumes of data and failures in the ingestion process.

Design the Data Processing Plan

In this step, you process your data through cleaning, transformation, enrichment, and validation. Data processing helps you handle inconsistencies and convert, update, and verify data to ensure fulfillment of quality standards. 

Set Up Data Storage

You can store real-time ingested and processed data at a destination that can act as a repository to access data for further operations. Relational databases, data warehouses, data lakes, and NoSQL databases are some of the data storage options. When choosing a data storage solution, you should consider accessibility and scalability features. 

Deploy Your Pipeline and Set Up Monitoring Framework

You should deploy the data pipeline after running and testing it to achieve the desired objectives. You should also set up a monitoring framework to track and ensure the data pipeline is performing all its tasks correctly and in real time. 

Real-time Processing With Airbyte

Successful data integration plays an important role in the real-time data processing. It eliminates data silos by giving a unified view of your datasets. This improves data accessibility, which, in turn, saves the data processing time as you get all the information in one place. 

Airbyte is a data integration and replication tool that helps you integrate data scattered across various sources at a centralized location. To simplify this process, it offers a vast library of 350+ connectors. If the connector you want is not already present in the connector library, you can also create your own using its connector development kit (CDK). 

Some of the features of Airbyte that make it a robust data integration solution are as follows:

• Change Data Capture: Airbyte supports a change data capture(CDC) feature that enables you to sync your destination data in accordance with changes made at the source. You can leverage this feature to update your datasets quickly with reduced latency. ‍
• GenAI Workflow Management: You can use Airbyte to manage AI workflows by loading your unstructured data directly into vector stores like Pinecone. The platform also allows you to facilitate RAG transformations and build LLM apps using frameworks like LangChain or LlamaIndex. ‍
• Streamlines Data Orchestration: It helps orchestrate data workflows by integrating with data orchestration tools like Airflow or Dagster. This simplifies workflow management of complex data pipelines. 

What sets Airbyte apart is its open-source Python library, PyAirbyte, which allows you to create custom Python-based data pipelines within your Python environment. This library incorporates all the Airbyte connectors, providing flexibility to extract data efficiently from various data sources.

You can also apply complex transformations using familiar Python libraries like Pandas or Spark and seamlessly load the processed data into your desired destinations. PyAirbyte's support for incremental data reading ensures that you capture only the latest changes, optimizing pipeline efficiency and resource utilization. 

To get started, you can simply use PyPi to install PyAirbyte and leverage the interoperability this library provides with SQL and AI frameworks.

Summary

Real-time data processing is the backbone of faster operations in any data-driven organization. You should opt for it if you work in time-sensitive domains that demand fraud detection, robust security, or enhanced customer experiences. 

This blog provides complete information about real-time data processing. It explains the challenges and uses cases of real-time processing in detail. When selecting a processing tool, you should consider factors such as scalability, latency, architecture, security, and deployment complexity. This helps you build an efficient data processing system in the data-intensive ecosystem.