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The Guide to Data Streaming for Data Professionals

Data streaming is emerging as the secret sauce that drives modern apps and businesses. It is the continuous flow of data generated from various sources for real-time processing and analysis for immediate insight and action. Whether it is streaming apps, stock market trading solutions or home security systems, data is created continuously and constantly. Modern data professionals, marketers and product owners work with a stream of data generated in real-time from multiple sources and stored in data lakes or warehouses.

This article is created as a deep dive into all aspects of data streaming including its definition, benefits, use cases, challenges and the future.

What Is Data Streaming?

Data streaming is both a technology and a process that allows continuous ingestion, processing and analysis of data as it is generated by various sources such as sensors, social media feeds, market data or user-generated data. This modern approach replaces the traditional methods of processing the same data in batches. Data streaming allows on-demand processing and analysis for quicker decision-making.

Types of Data Streaming

Data streaming is classified into different types based on the source and the nature of processing required for each source. These include:

Event streams
Event streams are characteristically continuous streams of data created in real-time by different sources such as sensors of IoT devices, transactional and financial systems or user interactions within an app or website.

Log streams
Log streams are based on the log file entries made by IoT devices, internal IT systems or applications. The data generated may include system errors, security logs, customer logs and troubleshooting systems. Log streams require additional processing tools to extract meaningful insights on performance, fault detection and overall system efficiency.

Sensor streams
Sensor streams are based on sensor-related data on location, temperature and inventory provided to various businesses. They provide data for efficient processing in business.

Social media streams
Data in these streams is generated by social media feeds on user preferences and likes as well as brand mentions and sentiments. It helps gauge user sentiment, brand awareness and detect market trends.

Click streams
Click streams are generated by user interactions on a company website or app. They measure the behavior, interest and preferences of visitors to the website.

Why Is It Important?

Data streaming essentially encapsulates the nature of data in the real world for most modern businesses and apps. Whether it is predictive analytics, media streaming, fraud detection or data security, all processes closely resemble a series of events.

Data streaming helps to validate, structure and throughput data visualizations with zero latency for all of them. It solves the problem of storage as data is accessed in the cloud only on a need basis. This has resulted in greater flexibility in the scale and usage of computing resources for businesses.
Organizations can leverage data streaming to get snapshots of their operations, consumer behavior or market data instantly. For example, it has enabled e-commerce platforms to monitor user activity and recommend products as they shop.

What Are the Characteristics of Data Streaming?

As streaming data is created by a multitude of sensors and sources in real-time, from apps to sensors to log files of monitoring systems, they have distinct characteristics from traditional data.

a) Time Sensitive
Real-time data is generated continuously from various sensors and sources, making it time-sensitive and significant for a particular period. For example, in the context of security systems detecting suspicious movement, the data is relevant only for a small timeframe and should be analyzed and addressed during the same period.

b) Heterogenous
As the data is generated from multiple disparate sources across geographies and systems, it could be a mixture of multiple formats such as AVRO, JSON and CSV (Comma-Separated Value files), with data types that include numbers, dates, strings and binary types.

c) Continuous
Data streams are generated in real-time and are continuous by nature. They may or may not be acted upon instantly.

Benefits Of Data Streaming

Data streaming brings with it an inherent improvement over traditional batch processing. According to Gartner’s survey in 2022, nearly 80% of businesses have seen revenues jump after implementing real-time analytics. Its implementation has been growing at a CAGR of nearly 26% YOY. There are many more quantifiable benefits for data streaming such as:

1) High ROI for business processes
Gartner’s report also states that data streaming and its implementation in real-time analytics have helped organizations squeeze nearly $2.3 trillion in revenue. It has allowed organizations to identify process inefficiencies, improve customer satisfaction and reduce non-workforce costs such as machine failures. They can react and respond quickly to time-sensitive data. For example, 73% of American manufacturing enterprises have seen a streamlined deployment process after the implementation of real-time analytics backed by data streaming.

2) Reduced infrastructure costs
Unlike traditional batch processing, there is no need to store data in large warehouses for data streaming. This significantly reduces the cost associated with storage systems and hardware.

3) Reduced preventable systemic losses
As data streams work on live data provided by sensors and live feeds, the likelihood of identifying critical system failures is significantly higher. Organizations are better informed about security breaches, market fluctuations, negative customer sentiments and manufacturing defects as they happen, allowing them to act immediately and prevent losses.

4) Improved customer satisfaction and competitive advantage
Real-time data processing provides the organization with a competitive advantage in an open ecosystem where customers may decide in a split second whether to choose a competitor due to systemic failures or negative customer experiences.

What Is the Process of Streaming Data?

The process can be broken up into 3 key steps. They are:

1) Data ingestion: Where data is collected from various sources such as sensors, social media feeds, etc., through a stream processing software, data ingestion uses “Stream Producers”, which are software components in applications and IoT devices that collect data from the source. These stream producers transmit the records to the stream processor with parameters such as stream name, data value and sequence number. The processor groups the data temporarily using the stream name and sequence number to chronologically arrange it.

2) Data processing: Next, the stream processing software applies transformations, aggregations or analysis to the ingested data as it occurs. Stream processors transform and structure the output of the stream producer to be analyzed further using analytical tools. The results of these transformations are usually alerts, actions, dynamic visualizations on dashboards or a new data stream that can be consumed by other consumers.

3) Data analytics & visualization: Processed data is presented with real-time insights through dashboards or reports on an application. These tools analyze the data ready for consumption.

4) Data storage: An optional stage in some streaming systems is to store the processed data for later analysis. These essentially act as a hybrid of stream and batch processing systems. In most cases, these are flexible storage spaces such as data lakes. These may require relevant data partitioning, processing and backfilling with historical data for comparative or historical analysis.

When stream processing occurs, all the above steps work seamlessly and almost instantaneously, producing an output that could be a BI dashboard, an alert or a data stream that can be processed and consumed further.

What Are the Challenges in Data Streaming?

The real-time and complex nature of data streaming brings with it the following challenges;

1) Data diversity and complexity
Real-time data streams are often plagued by data loss and damaged packets of data. Sources like IoT sensors, social media feeds, navigation data of GPS, etc. are heterogenous. As a result, data streaming also must handle disparate and diverse datasets. The system should be able to tag and present the same according to the consumer needs.

2) Time sensitive
Data streams depend on the timeliness of data as they often work on critical systems that have time-bound relevance and importance. The systems should be fast enough to analyze and visualize data while it is relevant.

3) Elasticity of data
Data stream processing systems must have a high level of quality even when the data load increases dynamically. The systems must adapt accordingly and increase or reduce capacity based on the volume of data they receive to ensure optimal usage of resources.

4) Imperfect nature of data
The disparate nature of data sources and the mismatch in transmission mechanisms sometimes result in missed or damaged data elements. This may occur as the information elements appear in jumbled order.

5) Lack of replicability of data
Due to the real-time nature of data sources, the data streams can’t often be repeated if data transmission is lost. Even though provisions exist for the re-transmission of data, the new data may not be a replica of the previous data stream.

6) Data security
Big data processors such as those involved in stream processing applications will have a large list of producers and consumers that communicate with a data cluster continuously. Without proper access control, any client can be configured to read and write any data topic. In such cases, it is important to properly authenticate any client attempting to write or read a particular topic.

Use Cases for Streaming Data

Some typical use cases involve event data that is generated by some action and requires immediate decision. The following are two such examples in the real world.

1) Real-time fraud detection: Stream processing-powered anomaly detection has helped one of the world’s largest credit card providers reduce its fraud write-downs by $800Mn per year. As soon as a user swipes a credit card, stream processing enables systems to run algorithms that recognize and block fraudulent charges and send triggers for these anomalous charges to the bank and consumer without the consumer having to wait for approvals.

2) Real-time personalization and marketing: Nearly 89% of marketers for top e-commerce companies use stream processing to deliver personalized messages. E-commerce companies create contextual user experiences as the user is on their website or app based on the user’s browsing activity, previous purchases, and other in-app behavior. Nearly 14% of marketers have created an ROI of nearly $15 for every dollar spent through these personalized campaigns.

What To Consider While Choosing a Data Streaming Platform?

While choosing a data streaming platform, it is important to remember the most important KPIs. These are event rate, throughput (also called event rate times event size), latency, reliability and the number of topics (in the case of pub-sub architecture).

Another major factor is the scalability of the platform by adding nodes. Doing this in a clustered geometry can also improve the reliability of the platform.

One must also look at the number of programming languages supported by the platform as developers and client consultants will be the ones writing the applications for these platforms. For example, a good data streaming platform may support Java, Scala, C/C++, Go, .Net and Python to help the developers write applications. Some platforms support Java, JavaScript, PHP, Ruby, Swift or Node.js, WebSocket and C#. The choice of data streaming platform may be driven by the languages supported by it.

Another factor for consideration is the number of connectors it supports. Some platforms support as many as 120 connectors for all data sources, readily available and tested.

The ideal data streaming platform should be located where there is the least latency, ideally near the data sources and other components. If not, it is also good to choose platforms that provide a geographically distributed cluster that supports low latency for far-flung data sources and sinks.
Finally, it is also important to look at the overall manageability of the platform. Some platforms are notoriously hard to configure and maintain without dedicated expertise in running them. Commercially supported cloud servers may be easier to manage.

The choice of data streaming platform often is a balance of the above-mentioned factors. The right data streaming platform for one depends on one’s processing and analytical needs.

How Is Data Streaming Significant for A Business?

For consumer-centric businesses that deal with large variables in metrics and a multitude of data, data streaming isn’t just a technological upgrade, it is a strategic necessity. Data streaming enables real-time analytics, which can lead to more informed decision-making, operational efficiencies and enhanced customer experiences. For example,

1) Uber: Uber uses data streaming to power its rider-driver matchmaking through apps installed by the rider and driver to track the location details. When a rider requests a ride, Uber uses GPS data to match a driver who is nearby and leverages Google Maps traffic API to provide the ETA for the driver. On the driver’s end, Uber provides real-time traffic data and updates ride requests using the available requests sent out by nearby riders. The app also provides price information based on the demand and traffic conditions along with the distance to the destination.

2) Instagram: Nearly 95 million photos and videos are uploaded on Instagram every day and the system provides real-time view metrics, like counts, comments, and reactions to the content created. The live component of Instagram also requires critical instant data transfer for uninterrupted live calls and sessions.

Stream Processing Vs. Batch Processing

Batch processing is the traditional method that derives its terminology from traditional computing where computers periodically complete high-volume repetitive jobs in batches. In batch processing, data is aggregated from different sources and stored in a data warehouse to be used later by a business intelligence tool for analysis and visualization. It wasn’t ideal for real-time analytical purposes in machine learning or modern streaming and social media platforms. The need to process data immediately on an ongoing basis resulted in the genesis of stream processing. In this method, data isn’t normally stored but is accessed for processing from sources as and when required. Both have their merits and demerits, which include:

Batch Processing Stream Processing
Processes high volume of data in batches within a specific period Processes continuous stream of data in real-time
Data size is known and is finite in batch size Data size can be unknown and of indefinite quantity
Data is processed in multiple passes and takes longer Data is processed in relatively fewer passes and takes milliseconds
The input graph is static The input graph is dynamic
Data is analyzed based on a snapshot Data is analyzed continuously in real-time
The response is provided after job completion The response is provided instantly
Used in payroll, billing, food processing systems, etc. Used in payroll, billing, food processing systems, etc. Used in stock exchange, e-commerce transactions, social media, etc.

Many businesses have implemented a combination of stream processing and batch processing where data may optionally be stored and retrieved in batches for processing and analysis.

The Future of Data Streaming

Data streaming is already becoming a ubiquitous method to get real-time analytics in most modern businesses and applications. Going forward, the technologies will become more accessible and user-friendly so that businesses of all sizes can leverage their power without needing technical expertise.

The increasing popularity of data streaming will also require platforms to be built on robust security measures and adhere to strict data piracy regulations. The future looks very promising with new technologies, ingenious applications and increased adoption by more industries.

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