This article is an excerpt from our comprehensive, 40-page eBook: The Architect’s Guide to Streaming Data and Data Lakes. Read on to review the 6 most common use cases, or get the full eBook now (FREE) for in-depth tool comparisons, case studies, and a ton of additional information.
What is Streaming data?
Streaming data is a flow of data collected from many sources in various formats and amounts and by streaming it means that it is continuous and has no end. It can be collected from applications, sensors, bank transactions, website activities, server log files, and more.
Streaming data can have many examples in the real world, they can be used to provide real-time stock trades, and provide real-time analysis in different applications such as real-time location tracking and determining the traffic data in real-time.
What is Stream processing?
After collecting this huge amount of data we need to process this data to get meaningful information and use it in our daily life.
Stream processing is taking an action on a series of data close to the time they are created. So, It analyzes and processes the data as they generate immediately, rather than collecting a group of data and doing processing on them for a variety of use cases. Data streams are processed, stored, analyzed, and edited to produce high-quality real-time data such as text, logs, images, videos, audio, or video.
Stream processing technologies are an effective way of managing continuously generated events, website visits, IoT sensors, and customer transactions. We will talk about some of the uses in a moment.
Stream processing can be preferable to batch processing, which needs the data to build up and process them at once while stream processing processes the data as they occur.
Stream processing and streaming architectures use cases
Log analysis is one of the most common use cases of IT operations, and it can be applied to a wide range of real-time log analyses to gain insights. This tool is designed to allow for analysis of depth logs with visualization and includes out-of-the-box filters to help you save time.
The technical approach is based on distributed processing of log streams in real-time. Stream processing is used to query continuous data streams and process received data. It can generate analyses of streams and transactions, then take data from existing streams and create new streams for additional use cases.
With log analysis, we can analyze the network logs to detect anomalies and incidents before it reaches the user. Also, log analysis of routers can give you a good picture of the activity on your network such as monitoring the internet activity of your applications and router attack detection. So when something goes wrong it will appear on logs and alerts you as they occur.
Log analysis tools are crucial for effective monitoring because they allow us to extract meaningful data from log files. Popular log analytics tools such as Splunk and Elasticsearch can be used with Upsolver to provide the streaming and enrichment capabilities for real-time log analytics.
A stream processor must help developers write applications that respond to incoming data. The combination of these powerful technologies ensures that fraud can be detected and prevented.
Streaming transaction data can detect anomalies that signal fraud in real-time, and stop fraudulent transactions, or identify and stop them before they are even completed. Fraudulent transactions can also be stopped as they occur by inspecting, correlating, and analyzing the data which can occur in many industries.
Using machine-learning algorithms and by analyzing transactions in real-time we can Identify patterns to detect fraudulent transactions. It uses complex algorithms that iterate over large data sets and analyze the patterns in data. One of the algorithms is a binary classification which can detect if a transaction is fraud or not.
You can also identify fraud and send notifications by email, SMS, social media, and other forms of communication.
Cyber attacks will always be a concern in all computing technologies, but there will always be ways to combat them. Strong cybersecurity systems rely not only on cyber-defense technology but also on people making smart cyber-defense decisions.
At the same time, the use of streaming data to detect anomalies in a data stream allows you to identify security issues in real-time to isolate threats. The advantages of stream processing include the ability to filter data from storage and the integration of data into a more robust analytics platform.
Using stream processing in real-time for traffic comes in, We can Identify a DDoS attack by analyzing traffic to see if a suspicious amount of traffic comes from a single IP address or from huge traffic that comes from a user who has a single profile.
Another use case Is real-time processing data coming from sensors and devices. For example, it can be useful for aircrafts in gaining insights on faults as they occur before coming to a major problem which also results in fewer maintenance delays and improves flight safety.
Also, stream processing of information in the oil and gas industry can help in monitoring various processes in petroleum production and refining and different modalities including temperature, pressure, and pressure in order to guarantee the integrity of oil and gas production.
Data in modern IoT applications are usually real-time data in a stream, the calculations used in streams must be performed in real-time to reduce processing latency and provide accurate inventory and supply chain management. Near real-time analyses are possible with Upsolver and powerful databases and analytics engines.
Using stream processing on streaming data is very useful in the online advertising industry, It is used in social networks that tracks the user behaviour, user clicks and interests and based on this collected data for each user. It promotes ads which the users might be interested in. So, stream processing helps in advertising campaigns by processing the user clicks and interest in real time and showing sponsored contents.
A streaming architecture is recommended for the modern cloud data stacks. It is a long and difficult process for organizations to move away from the traditional on-prem databases architecture to the cloud. A streaming tool like Upsolver allows data teams to migrate data from an on-prem data solution to a cloud-native environment by providing schema-on-read, built-in transformation functions, automatic partition and compaction capabilities. It provides native CDC support to allow the latest view of the data in near real-time. If the users want to move from a traditional database to a cloud data lake architecture, it’s essential to minimize complexity of data processing and maintenance requirements.
If your business needs real-time responses from big data, you can stream the data so it can be filtered, sampled, aggregated and correlated. You get access to a variety of data sources, including posts on social media, emails, tweets, text messages, phone calls and more.
Real-time use of big data pays off and is evolving in most companies, but what do all these use cases have in common? A well-articulated business case offers all parties involved, from management to management, a high return on investment and offers tangible goodwill in the application case through improved revenues and a proven success story.
Want to learn more about streaming data analytics and architecture? Get our Ultimate Guide to Streaming Data:
- Get an overview of common options for building an infrastructure
- See how to turn event streams into analytics-ready data
- Cut through some of the noise of all the “shiny new objects”
- Come away with concrete ideas for wringing all you want from your data streams.