Data streaming is the backbone of so many technologies we rely on daily. Endless data sources that generate continuous data streams. Dashboards, logs and even streaming music to power our days. Data streaming has become critical for organizations to get important business insights — when you can get more data from more data sources, you might have better information to run your business.
This article explains data streaming, including:
Let’s get started!
Data streaming is the technology that constantly generates, processes and analyzes data from various sources in real-time. Streaming data is processed as it is generated.
(This is in direct contrast to batch data processing, which process in batches, not immediately as generated. More on that later.)
Streaming data from various sources can be aggregated to form a single source of truth. Then, you can analyze that single truth to gain important insights. Organizations can then use these insights to:
Today, various applications and systems generate such streaming data in various formats and volumes. Here are common examples of such data sources and how they are being used:
Traditionally, businesses performed data processing in batches, collecting them over time and saving computing resources and processing power. However, with the introduction of IoT sensors and the growth of social media and other streaming data sources, streaming processing has become critical for modern businesses.
These sources constantly generate a large amount of data every second, making it difficult to process with traditional batch processing techniques. Plus, the amount of data we generate far outpaces any previous data volumes. That makes storing all data in a data warehouse when it is generated even more difficult.
Data stream processing is critical for avoiding massive storage needs and it enables faster data-driven decisions.
Batch and stream processing are two ways of processing data. The following table compares the important characteristics of both processing types, including data volume, processing and latency.
Characteristic |
Batch Processing |
Stream Processing |
Data volume |
Processes large batches or volumes of data. |
Processes a small number of records, micro batches or individual records. |
How data is processed |
Processes a large batch of data at once. |
Process data as and when it is generated, either over a sliding window or the most recent data in real-time. |
Time latency |
High latency as it must wait until the entire batch is processed. Thus, the latency can range from minutes to hours. |
Low latency as it processes in real-time or near-real-time. Latency can range from seconds to milliseconds. |
Implementation complexity |
Simpler to implement |
Requires more advanced data processing and storage technologies. |
Analytics complexity |
It is complex to do analytics since large volumes of data need to be processed at once. |
Simple functions make analytics simpler than batch processing. |
Cost |
More cost-effective because there is less demand for more efficient data processing capabilities. However, data storage costs can be higher. |
More expensive as the processing engine requires real-time, faster processing capabilities. Less expensive when it comes to data storage. |
Use cases |
Suited for applications like payroll, billing, data warehousing, report generation, etc., that need to be processed on a regular schedule. |
Suited for applications like customer behavior analysis, fraud detection, log monitoring, and alerting. |
There are several benefits that data streaming technologies bring to any business. Following are some examples:
Making quick, accurate and informed decisions brings many competitive advantages for businesses in the current fast-paced environment. Data streaming helps realize that by:
This capability allows businesses to respond, adapt to changes and make better-informed decisions. It is particularly helpful for fast-moving e-commerce, finance and healthcare industries.
Data streaming helps organizations identify possible issues and provide solutions before they affect customers. For example, streaming logs can be analyzed in real-time to find errors and alert responsible parties. This capability allows businesses to provide uninterrupted service and avoid delays, improving customer satisfaction and trust.
Data streaming does not require expensive hardware or infrastructure, as it processes and analyzes large volumes of data in real-time without storing them in expensive data warehouses.
Additionally, data is processed in small batches or records at a time. Thus, businesses have the flexibility and time to scale their data processing capabilities according to their needs.
(Know the difference between data lakes & data warehouses.)
Data streaming helps businesses analyze customer behavior in real-time and provide personalized recommendations for customers. It can be useful in applications like e-commerce, online advertising and content streaming.
While data streaming brings many advantages to the business, there are also some challenges and limitations, such as:
Data streaming applications perform real-time processing by running the required computations over the data. There’s two big risks here:
The streaming data should meet quality standards and be consistent enough to process data accurately without errors. It can be challenging to manage in real-time. Thus, low-quality data or data inconsistencies can result in inaccurate data analytics.
Data streaming systems must be protected against cyberattacks, unauthorized access and data breaches. It can be challenging as data comes in real-time and, most of the time, has to be discarded after processing. The data streams require extra care, especially when the data is sensitive — PII or financial transactions — since they are common targets of cyber attackers.
While data streaming reduces storage costs, it can be expensive if you need to scale up to handle large volumes of data. Then, certain computations are more expensive to perform over streaming data. That makes streaming data a challenge for smaller organizations with limited budgets and resources.
Implementing and maintaining data streaming systems can be complex and may require specialized skills and expertise. Finding such resources can be challenging for some companies. Furthermore, it may take a significant amount of time to master those skills.
Data streaming requires more system resources, such as processing power and memory. Systems must be scalable to handle large volumes of data. It can be a limitation for startups or smaller companies.
Many companies offer data stream processors to gather large volumes of streaming data in real-time, process it, and deliver it to multiple destinations. Some cloud providers also provide managed platforms and frameworks for handling and processing streaming data. Some popular data stream processors and platforms help organizations collect, process, and analyze data from multiple streaming sources.
Data streaming is the technology that processes continuously generated data in real time. Today, numerous sources generate streaming data. Thus, it is critical to have an efficient streaming data processor in place for processing, analyzing, and delivering that analyzed data to multiple places. Data streaming differs from batch processing in terms of data volume, the way it is processed, latency, complexity, costs, and many other ways.
Data streaming offers several benefits, including improved customer satisfaction. However, there are also limitations, like the need to invest in processing power and security, and requirements to meet data quality and consistency. It can be challenging for smaller organizations with a limited budget. Today, several data streaming technologies are available.
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This posting does not necessarily represent Splunk's position, strategies or opinion.
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