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Enterprise Data Management

Real Time Data Streaming - Overview

Chandan Gaur | 13 December 2023

Real Time Data Streaming

Overview of Data Streaming and Analytics

Real-time data streaming analytics is a powerful process that allows organizations to extract valuable insights from the vast amounts of data they generate or consume. Imagine being able to analyze data as it is being produced within banks, stock exchanges, and branches across the world. This real-time analytics data is then delivered to users and administrators through an interactive analytics dashboard, providing them with valuable information instantly. Real-time analytics serves various purposes, including reporting historical and current data concurrently, receiving alerts based on predefined parameters, making operational decisions in real-time, and applying pre-existing predictive or prescriptive models. It also enables the creation of real-time displays or dashboards that constantly update with changing datasets.

  • To report historical data and current data concurrently.
  • For receiving alerts based on certain predefined parameters.
  • To build operational decisions and apply them to business processes or on other production activities based on a real-time and ongoing basis.
  • To apply pre-existing prescriptive models or predictive models.
  • For the outlook of real-time displays or dashboards in real-time on constantly changing datasets.

What is Real Time Streaming?

Real-time streaming is a dynamic process that enables the fast processing of massive volumes of data. By extracting information from this data in real-time, businesses can respond swiftly to changing conditions. This type of streaming relies on queries that operate within specific time and buffer windows. When comparing real-time streaming to the traditional database model, there are significant differences between the two. Real-time streaming processes data while it is in motion through the server, whereas the traditional database model requires data to be stored, indexed, and processed. Real-time analytics have numerous applications, some of which include:

  • E-Commerce
  • Pricing and analytics
  • Network Monitoring
  • Risk Management
  • Fraud Detection

Real Time Streaming Data

These days, a large number of Data Processing platforms are available to process data from ingestion platforms. Some of them support the streaming of data, and some support real streaming of data which is Real-Time data. In the case of streaming, there is always some amount of delay in streaming data from the ingestion layer. But on the other hand, in the case of Real-Time data, have tight deadlines regarding time. Whenever we talk about detecting frauds, predicting errors, business decisions, and analyzing real-time logs, all are seniors/examples  of streaming. As soon as data arrives, it will receive instant, which we termed Real-Time data.

Why we need Real Time Streaming?

We need Real Time Streaming because we all already know that distributed file systems such as Hadoop, S3, and other distributed file systems support data processing in large volumes. On the other hand, we can also query them using their different frameworks, like Hive, which uses MapReduce as its execution engine. Many organizations are trying to collect as much data as they can regarding their products, services, or even their organizational activities, like tracking employees' activities through various methods used as log tracking and taking screenshots at regular intervals.

So with the help of this data, Data Engineering allows us to convert this data into some particular basic formats, and further data analysts turn this data into some useful results which help the organization in several ways, such as helping in improving their customer experiences and also boost their team member’s productivity. But on the other hand, whenever we are talking about real-time analytics, fraud detection, or log analytics, then this is not the way to process our data. The actual value data is in processing or acting upon it at the instant it receives.

Benefits of Real Time Streaming and Analytics

The benefits of Real Time streaming and analytics are below:

1. Data Visualization

A set of historical datasets can be placed on a single screen to represent an overall point. Still, on the other hand, streaming data can be visualized so that it updates in real-time to display what is occurring every second.

2. Business Insights

When it's about business, real-time analytics can be used for receiving alerts based on certain and predefined parameters. For example, if any store there is a drop in sales, then an alert can be triggered to tell management about the serious problem. Increase competitiveness: Real-time analytics helps companies surpass competitors who are still based on batch processing analysis.

3. Security

Take an example of fraud detection, fraud can be detected immediately whenever it happens, and proper safety precautions can be taken to limit the damage.

Limitations of Real Time Streaming and Analytics

  1. Compatibility: In the case of historical big data analytics, Hadoop is the most widely used tool, but in the case of streaming and real-time data, it is not. The better options are spark streaming, Apache Samza, Apache Flink, or Apache Storm.

  2. System Failure: In terms of business, real-time analytics or handling data at rapid rates is not an easy job. It could lead to faulty analysis or even sometimes system failure.

Real Time Data Streaming Architecture

It refers to the infrastructure and processes used to capture, process, and analyze data in real time. The architecture typically consists of four main components: data sources, data ingestion, data processing, and data delivery. Let's explore each of these components in more detail.

1. Data Sources  

Data sources refer to the various systems and devices that generate data. These include sensors, social media platforms, transactional databases, web applications, and more. These data sources can generate vast amounts of data, often in unstructured or semi-structured formats, making it challenging to process and analyze.

2. Data Ingestion

The data ingestion component collects, filters, and formats the data for processing. The ingestion process typically involves several steps, including data validation, data normalization, and data enrichment. Once the data is formatted correctly, it can be sent to the processing component for further analysis.

3. Data Processing

The data processing component analyzes the data and generates insights in real-time. This component can include various tools and technologies such as machine learning algorithms, statistical models, and data visualization tools. The data processing component aims to identify data patterns, trends, and anomalies to inform business decisions.

4. Data Delivery

The final component of the real time data streaming architecture is data delivery. This component is responsible for delivering the insights generated by the data processing component to end-users. This can include dashboards, alerts, reports, and APIs. 

What is the Real-Time Analytic Platform?

The Real-time analytics platform consists of the following steps:-

  • The Real Time Stream Sources

  • Real Time Stream Ingestion

  • The Real Time Stream Storage

  • Real Time Stream Processing

Real-Time Stream Sources

For real-time analytics, the first major need is sourced from where real-time data originate. There are many sources of streaming data:-

  • Sensor Data

The sensor is the device's output that measures a physical quantity and transforms it into a digital signal.

  • Social Media Stream

Social media streaming like a Twitter feed, Facebook, Instagram, YouTube, Pinterest, and Tumblr.

  • ClickStream

The stream contains the data about which pages the website visits and in what order.

Best Real-Time Stream Ingestion Tools?

Now there is a need to ingest the streams which are coming from real-time stream sources. So there are various open-source tools in the market through which we can ingest the stream, and some of them are below:-

Explore in detail about the Real Time Analytics Tools and Benefits

1. Apache NIFI

In simple words, we can say that Apache NiFi is a data ingestion tool. It is an integrated data logistics platform for automating data movement between disparate systems. It provides real-time control that makes managing data movement between any source and destination easy.

Apache NiFi supports disparate and distributed sources of differing formats, schemas, protocols, speeds, and sizes, such as machines, geolocation devices, clickstreams, files, social feeds, log files and videos, and more. It is configurable plumbing for moving data around, similar to how FedEx, UPS, or other courier delivery services move parcels around. Apache NiFi also allows us to trace our data in real-time, just like we could trace a delivery.

2. Apache StreamSets

StreamSets is also a data ingestion tool similar to NIFI. StreamSets is a data operations platform where we can efficiently develop batch and streaming dataflows, further operate them with full visibility and control, and easily evolve our architecture over time.

Apache Beam is an open-source unified programming model used to define and execute data processing pipelines. Source: Apache Beam Architecture

What are the Real Time Stream Storage Sources?

Further, we need storage in which we can ingest the stream. Many open-source stream storages are available in the market. Some of them are below:-

  • Apache Kafka

Kafka is beneficial for building real-time data pipelines and streaming apps. It is horizontally scalable, fault-tolerant, wicked fast, and runs in production in thousands of companies.

  • Apache Pulsar

Apache Pulsar is an open-source distributed pub-sub messaging system created at Yahoo and is now part of the Apache Software Foundation.


NATS Server is a simple, high-performance open-source messaging system for cloud-native applications, IoT messaging, and microservices architectures.

Real-Time Stream Processing

Some open-source data streaming platforms are available in the market which is best for processing the streaming data, and some of them are below:-

  • Apache Spark

Apache Spark is a unified analytics engine for large-scale data processing. Basically, Apache Spark is a computing technology design, especially for faster computation. Spark has been designed to cover batch applications, interactive queries, algorithms, and streaming. The main feature of spark is that it is in-memory cluster computing which means that this will increase the processing speed of an application.

Explore more about Apache Spark Architecture and Use Cases

  • Apache Apex

Apache Apex is also a unified stream and batch-processing engine. Basically, Apache Apex is based on separate functional and operational specifications rather than compounding them together.
  • Apache Flink

Apache Flink is an open-source stream processing framework for distributed, high-performance, and data-accurate data streaming applications. Flink also supports batch processing as a special case of stream processing.

  • Apache Storm

Apache Storm is also a free and open-source distributed real-time computation system similar to the above processing systems. The storm is very simple and also useful with any programming language. The storm is extremely fast, with the ability to process over a million records per second per node on a cluster of modest size. The main features of Storm are that it is fast, scalable, fault-tolerant, reliable, and easy to operate.

Deep dive into Apache Storm Security with KerberosApache Storm Security with Kerberos

  • Apache Beam

Apache Beam is a unified programming model used for implementing batch and streaming data processing jobs that run on any execution engine. The main features of Apache Beam are that it is unified, portable, and Extensible. It works with any processing engine like Apache Spark, Flink, Apache Apex, Google Cloud Dataflow, and Apache Gear pump.

Java vs Kotlin
Flink treats data streams as a data stream, using which we can manipulate the streaming data. Apache Flink Architecture and Use Cases

Use Cases of Real-Time Data Streaming

Now that we have explored the various components of the real-time data streaming architecture let's look at some of the use cases for this technology.

1. Fraud Detection

Real time data streaming architecture is widely used in fraud detection applications. By analyzing transaction data in real time, businesses can identify fraudulent activity and take immediate action to prevent losses.

2. Predictive Maintenance

In manufacturing, real time data streaming architecture can monitor equipment performance and predict when maintenance is needed. By detecting issues early, businesses can avoid costly downtime and prevent equipment failure.

Explore more about Predictive Maintenance Services and Solutions

3. Social Media Monitoring

Real time data streaming architecture is also used in social media monitoring applications. Analyzing social media data in real-time allows businesses to identify trends and sentiments and adjust their marketing strategies accordingly.  

4. Financial Services

Real time data streaming architecture is also widely used in financial services applications. Businesses can identify trading opportunities and make informed investment decisions by analyzing market data in real-time. 


Real Time data streaming and analytics is a process that mainly focuses on the data produced or consumed, or stored within a live environment. The scope of analytics can be from multiple sources. We can import or fetch the data, store it within a system, and execute data analysis algorithms.