What is Data Streaming? A Comprehensive Guide 101

Real-time data is the need of the hour for businesses to make timely decisions, especially in cases of fraud detection or customer behavior analysis. Relying on traditional batch processing is not effective now. Data streaming is a powerful technology that provides organizations with the ability to process and analyze large amounts of data in real time. 

In this article, we will delve into all aspects, including its components, benefits, challenges, and use cases. So, whether you’re a data analyst, developer, or decision-maker, this article will provide you with a comprehensive understanding of the world of data streaming.

What is Data Streaming?

Data Streaming is a technology that allows continuous transmission of data in real-time from a source to a destination. Rather than waiting on the complete data set to get collected, you can directly receive and process data when it is generated. A continuous flow of data i.e. a data stream, is made of a series of data elements ordered in time.

The data in this stream denotes an event or change in the business that is useful to know about and analyze in real time. As an example, the video that you see on YouTube is a Data Stream of the video being played by your mobile device. As more and more devices connect to the Internet, Streaming Data helps businesses access content immediately rather than waiting for the whole entity to be downloaded.

How Does Data Streaming Work?

Modern-day businesses today replicate data from multiple sources such as IoT sensors, servers, security logs, applications, or internal/external systems, allowing them to micro-manage many non-rigid variables in real time. Unlike the conventional method of extracting, storing, and then later on analyzing data to take action, streaming data architecture gives you the ability to do it all while your data is in motion.

Now, let’s check out the steps:

Data streaming works by continuously capturing, processing, and delivering data in real-time as it is generated. The following are the basic steps involved:

• Data Capture: Data is captured from various sources such as sensors, applications, or databases in real-time. 
• Data Processing: The captured data is processed using stream processing engines, which can perform operations such as filtering, aggregation, and enrichment.
• Data Delivery: The processed data is then delivered to various destinations, such as databases, analytics systems, or user applications.
• Data Storage: The data can be stored in various ways, such as in-memory storage, distributed file systems, or cloud-based storage solutions.

Batch vs Stream Data Processing

Batch processing is a data processing technique where a set of data is accumulated over time and processed in chunks, typically in periodic intervals. Batch processing is suitable for the offline processing of large volumes of data and can be resource-intensive. The data is processed in bulk, typically on a schedule, and the results are stored for later use.

Stream processing, on the other hand, is a technique for processing data in real time as it arrives. Stream processing is designed to handle continuous, high-volume data flows and is optimized for low resource usage. The data is processed as it arrives, allowing for real-time analysis and decision-making. Stream processing often uses in-memory storage to minimize latency and provide fast access to data.

In summary, batch processing is best suited for the offline processing of large volumes of data, while stream processing is designed for the real-time processing of high-volume data flows.

Let’s look at the differences between batch and stream processing in a more concise manner.

Batch Processing	Stream Processing
Processes data in chunks accumulated over time	Processes data in real-time as it arrives
High latency	Low latency
Can handle large volumes of data	Designed to handle high-volume data flows
Resource-intensive	Optimized for low resource usage
Suitable for offline processing	Suitable for real-time data analysis
It may require significant storage resources	Often uses in-memory storage
Typically processes data in periodic intervals	Continuously processes data as it arrives

Practically, mainframe-generated data is typically processed in batch form. Integrating this data into modern analytics systems can be time-consuming, making it difficult to transform it into streaming data. However, stream processing can be valuable for tasks such as fraud detection, as it can quickly identify anomalies in transaction data in real time, allowing fraudulent transactions to be stopped before they are completed.

What are the Benefits of Data Streaming?

Here are some of the benefits:

• Stream Processing: Stream processing is one of the key benefits of data streaming, as it allows for the real-time processing and analysis of data as it is generated. Stream processing systems can handle high volumes of data, and are able to process data quickly and with low latency, making them well-suited for big data applications.
• High Returns: By processing data in real-time, organizations are able to make timely and informed decisions, which can lead to increased efficiency, improved customer experiences, and even cost savings. For example, in the financial industry, data streaming can be used to detect fraudulent transactions in real-time, which can prevent losses and protect customer information. In retail, it can be used to track inventory in real-time, which can help businesses to optimize their supply chain and reduce costs.
• Lesser Infrastructure Cost: In traditional data processing, large amounts of data are typically collected and stored in data warehouses, which can be costly in terms of storage and hardware expenses. However, with stream processing, data is processed in real-time as it is generated, which eliminates the need to store large volumes of data. This can greatly reduce the cost of storage and hardware, as organizations don’t need to maintain large data warehouses.

What are the Challenges of Data Streaming?

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

• High Bandwidth Requirements
• Memory and Processing Requirements
• Requires Intelligent and Versatile Programs
• Scalability
• Contextual Ordering
• Continuous Upgradation and Adaptability

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 meanings and interpretations, 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.

What are the Use Cases of Data Streaming?

Here are a few use cases:

• Information about your location.
• Detection of fraud.
• Live stock market trading.
• Analytics for business, sales, and marketing.
• Customer or user behaviour.
• Reporting on and keeping track of internal IT systems.
• Troubleshooting systems, servers, gadgets, and more via log monitoring.
• SIEM (Security Information and Event Management): Monitoring, metrics, and threat detection using real-time event data and log analysis.
• Retail/warehouse inventory: A smooth user experience across all devices, inventory management across all channels and locations.
• Matching for ridesharing: Matching riders with the best drivers based on proximity, destination, pricing, and wait times by using location, user, and pricing data for predictive analytics.
• AI and machine learning: This opens up new opportunities for predictive analytics by fusing the past and present data into one brain.

Data Streaming Architecture 

A typical Data streaming architecture contains the following components:

• Message Broker: It transforms the data received from the source(producer) into a typical message format and streams it on an ongoing note to make it accessible to be used by the destination(consumer). It acts as a buffer, helping to ensure a smooth data flow even if the producers and consumers are operating at different speeds.
• Processing Tools: The output messages from the message broker needs to be further manipulated using processing tools such as Storm, Apache Spark Streaming, and Apache Flink.
• Analytical Tools: After the output message is transformed by the processing tools into a form to be consumed, analytical tools help you to analyze data to provide business value.  
• Data Streaming Storage: Business often stores their streaming data in data lakes such as Azure Data Lake Store (ADLS) and Google Cloud Storage. Setting and maintaining the storage can be a challenge. You would need to perform data partitioning, data processing, and backfilling with historical data.  

Conclusion

This article gave you a simplified understanding of what data streaming is, how it works, what the benefits of data streaming 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.

Discover the essentials of Spark real-time streaming and how to configure it for effective real-time data analysis.

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 150+ sources of data.

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