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Enterprise Data Management

Understanding Real Time Data Streaming Tools and Technologies

Chandan Gaur | 12 May 2022


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Overview of Data Streaming and Analytics

Real-time data streaming and analytics is the process used for analyzing the huge amount of data at the moment it is used or produced. In this, we extract valuable information for the organization as soon as it's created or stored. Let's take an example of analyzing the huge amount of data as it is produced within banks and branches, stock exchanges throughout the globe and further, this analytics data delivers to the users/administrator through an analytics dashboard. Real-time analytics can be used for the below purposes:-

IoT analytics is an application that helps to understand the huge volume of data generated connected IoT devices. Source: IoT Analytics Platform for Real-Time Data

  • To report historical data and current data concurrently.
  • For receiving alerts based on certain and 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 are the benefits of Real-Time Streaming and Analytics?

The benefits of Real-time streaming and analytics are below:

Data Visualization

A set of historical datasets can be placed into 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.

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.


Take an example of fraud detection, fraud can be detected immediately whenever it happens, and a proper safety precaution 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.

Stream Analytics delivers the most powerful insights from the data, a lot of Data Processing platforms available to process data from ingestion platforms. Source: Real-Time and Streaming Architecture

What is Real-Time Streaming?

Real-time streaming is the process by which huge sizes/volumes of data can be processed quickly. A firm extracting the information from that particular data can react to changing conditions in real-time. In other words, we can say that real-time streaming is based on the queries that work on time and buffer windows. When we compare this real-time streaming process with the traditional database model, we found a lot of differences between these two processes. Both processes are opposite to each other. Real-time streaming uses data while in motion through the server, but on the other hand, in the traditional database model,, data was first stored and indexed and processed. Real-time analytics are useful in many applications. Below are some of the major applications in which real-time streaming:-
  • 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 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 talked about detecting frauds, predicting errors, business decisions, and analyzing real-time logs, all are the scenarios/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 their 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 like log tracking, 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 employee’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.

Apache Spark is a fast, in-memory data processing engine with expressive development APIs to allow data workers to execute streaming conveniently. Source: Real-Time Streaming Application

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 sources from where real-time data origin. 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, Tumblr.


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

What are the best Tools for Real-Time Stream Ingestion?

Now there is a need to ingest the streams which are coming from the 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:-

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 the movement of data between disparate systems. It provides real-time control that makes it easy to manage data movement between any source and any destination. 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.

Apache StreamSets

StreamSets is also a data ingestion tool similar to NIFI. Basically, StreamSets is a data operations platform where we can efficiently develop batch and streaming dataflows, and 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 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 are best fo 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 that its design is specially for faster computation. Spark has design 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.

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.

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 in the form of a data stream, using which we can manipulate the streaming data. Apache Flink Architecture and Use Cases


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.