Data Streams: A Simplified Explanation 101

on Data Integration, Data Warehouse • February 6th, 2021 • Write for Hevo

FI-Data Streams

Do you wish to understand what Data Streams are and how they work? Do you wish to set up Data Streams for your business? If yes, then you’ve come to the right place. This article will provide you with a deep understanding of Streaming Data, its properties, and the challenges presented by it. Any tool that manages your Data Streams should be able to do handle the challenges stated in this article.

Table of Contents

Understanding Data Streams

We associate the word stream with something which is flowing or is continuously renewed or steadily supplied. A big river is formed when multiple streams coming from many directions, at varying speeds, merge to form a single larger entity.

Data Stream Example
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When it comes to data, Streaming Data is a regular supply of data that is continuously generated by one or more sources. These sources may generate data at varying speeds and time intervals. Moreover, data from a single source may come at different speeds at different times. 

As an example, the video that you see on YouTube is a Data Stream of the video being played by your mobile device. 

Furthermore, a concept drift may happen with data over a period of time. That is, a Data Stream from the same source may change its properties or nature with time. As more and more devices connect to the Internet, Streaming Data helps them in accessing content immediately, rather than waiting for the whole entity to be downloaded. Tools are being developed to process chunks of data as they arrive, without knowing how much data is left or when the Data Stream would end. 

Properties of Data Streams

Data Stream - Flow
Image Source

The various properties of Data Streams are as follows.

1) Homogenization

When you watch a video online, you see moving imagery and hear audios. In older days, a movie tape had 2 physically distinct tracks on them, the moving images were in the centre and the tracks on the sides used to hold the related audio. Nowadays, both audio and video are delivered in digital format as a stream of bytes. Physically they are very similar, and it’s up to the program to differentiate and process them. 

This is what we call homogenization of the Data Stream. The storage and transmission of data do not depend on its type; it’s always a stream of bytes flowing. 

The receiver chooses to interpret these bytes as per their function. For example, a C program may read the first two bytes and treat it as an alphabet, whereas a video player may read a couple of bytes together and treat them as an image frame. 

2) Modularity

A Data Stream can contain modules that may consist of logically different parts that can be separated and recombined.

3) Interoperability

Streaming Data can work with various systems and data sources. For example, your YouTube video can be embedded on your Facebook page. These are two separate data streams working in close coordination, as the video will now be played on the Facebook page.

4) Connectivity

A Data Stream can connect different applications and devices, as well as connect producers to consumers. 

For example, it can connect customers to film producers to advertisers. Another example is the UPS, which forms a real-time Data Stream with all its moving trucks to calculate the optimal delivery route and to track where a package is located at a point in time.

5) Bi-directional

While you’re watching a video, your actions on the video are recorded and sent back to its source. 

Consumer behaviour and some of the actions are recorded to generate a digital footprint or a digital trace. A digital trace can be used to upsell products and influence decision making. 

Another good use of this phenomenon can be seen in the healthcare industry. A patient’s medical history, combined with his current diagnosis and pricing data, can suggest optimal treatment paths based on his budget and environment. It can also trigger alerts for him and his associated health workers. 

6) Continuity

As events happen and changes occur, digital sensors record them and send them to programs and interested people. This can lead to better business decisions and timely intervention. 

For example, an application can continuously monitor stock market data and generate suggestions to buy or sell a stock, or a road sensor can detect an overspeeding vehicle and alert the law enforcement agencies. 

7) Real-Time and Concurrent 

A Data Stream is concurrent and must be processed in real-time.

For example, in the case of ride-sharing matching, location data is used to trace taxis and users concurrently. Once a ride is booked or canceled, new data must be propagated (sent back to interested parties) instantly. 

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Challenges Presented by Data Streams

There are various challenges that have to be considered while dealing with Data Streams:

1) High Bandwidth Requirements

Unless the Data Stream is delivered in real-time, most of its benefits may not be realized. With a variety of devices located at variable distances and generating different volumes of data, network bandwidth must be sufficient to deliver this data to its consumers. 

2) Memory and Processing Requirements

Since data from the Data Stream is arriving continuously, a computer system must have enough memory to store it and ensure that any part of the data is not lost before it’s processed. Also, computer programs that process this data need CPUs with more processing power as newer data may need to be interpreted in the context of older data and it must be processed quickly before the next set of data arrives. 

Generally, each data packet received includes information about its source and time of generation and must be processed sequentially. The processing should be powerful enough to show upsells and suggestions in real-time, based on users’ choices, browsing history, and current activity. 

3) Requires Intelligent and Versatile Programs

Handling data coming from various sources at varying speeds, having diverse semantic meaning and interpretation, coupled with multifarious processing needs is not an easy task. 

4) Scalability

Another challenge Streaming Data presents is scalability. Applications should scale to arbitrary and manifold increases in memory, bandwidth, and processing needs. 

Consider the case of a tourist spot and related footfalls and ticketing data. During peak hours and at random times during a given week, the footfalls would increase sharply for a few hours leading to a big increase in the volume of data being generated. When a server goes down, the log data being generated increases manifold to include problems+cascading effects+events+symptoms, etc. 

5) Contextual Ordering

This is another issue that Streaming Data presents which is the need to keep data packets in contextual order or logical sequences. 

For example, during an online conference, it’s important that messages are delivered in a sequence of occurrences, to keep the chat in context. If a conversation is not in sequence, it will not make any sense. 

6) Continuous Upgradation and Adaptability

As more and more processes are digitized and devices connect to the internet, the diversity and quantum of the Data Stream keep increasing. This means that the programs that handle it have to be updated frequently to handle different kinds of data.

Building applications that can handle & process Streaming Data in real-time is challenging, taking into account many factors like ones stated above. Hence, businesses can use tools like Hevo that help stream data to the desired destination in real-time.


This article gave you a simplified understanding of what a Data Stream is, how it works, what the properties of Streaming Data are, and what challenges are faced in developing a system to handle it. Most businesses today use Streaming Data for their day-to-day operations in some form. Developing and maintaining tools in-house to handle Streaming Data will be a challenging and expensive operation.

Businesses can instead choose to use existing data management platforms like Hevo. Hevo provides a No-Code Data Pipeline that allows accurate and real-time replication of data from 100+ sources of data.


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