Stream Processing is an important aspect of Big data technology. It’s used to quickly query a continuous Data Stream and discover situations within a short time frame after getting the data. The detection time spans from a few milliseconds to several minutes. For example, by querying data streams from a temperature sensor, you can receive an alarm when the temperature has hit the freezing point using stream processing.
Here’s all you need to know about Stream Processing, as well as some key pointers to keep in mind before you start the process.
Table of Contents
What is Stream Processing?
Stream Processing is the act of taking action on a set of data as it is being created. Historically, data professionals used the term “real-time processing” to refer to data that was processed as frequently as was required for a certain use case. However, with the introduction and adoption of stream processing technologies and frameworks, as well as lower RAM prices, “Stream Processing” has become a more particular term.
Multiple jobs on an incoming sequence of data (the “data stream“) are frequently conducted in Stream Processing, which can be done serially, in parallel, or both. This workflow is known as a Stream Processing Pipeline, and it includes the generation of stream data, data processing, and data delivery to a final destination.
Aggregations (e.g., sum, mean, standard deviation), Analytics (e.g., predicting a future event based on patterns in the data), Transformations (e.g., changing a number into a date format), Enrichment (e.g., combining the data point with other data sources to create more context and meaning), and ingestion are all actions that Stream Processing performs on data (e.g., inserting the data into a database).
How Does Stream Processing Function/Work?
Data from IoT sensors, Payment Processing Systems and Server and Application Logs are all examples of data that can benefit from Stream Processing. Publisher/subscriber (also known as pub/sub) and source/sink are two prevalent paradigms. A publisher or source generates data and events, which are provided to a Stream Processing Application, where they are enhanced, tested against fraud detection algorithms, or otherwise altered before being sent to a subscriber or sink. Apache Kafka, Hadoop, TCP connections, and in-memory Data Grids are some of the most frequent sources and sink on the technical side.
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Why do you need a Stream Processing Architecture?
The usefulness of insights obtained from Data Processing was demonstrated by Big Data. Not all of these insights are made equal. Some insights are more beneficial just after they occur, but their value fades rapidly with time. Such scenarios are possible thanks to Stream Processing, which provides insights faster, frequently within milliseconds to seconds of the trigger.
Some of the secondary reasons for employing Stream Processing are listed below.
Some data comes in the form of an unending stream of occurrences. To accomplish batch processing, you must first store the data, then pause data collecting for a period of time before processing it. Then you have to worry about doing the following batch and aggregating across numerous batches. Streaming, on the other hand, easily and naturally accommodates never-ending data streams. Patterns can be detected, results may be inspected, several degrees of focus can be examined, and data from multiple streams can be viewed simultaneously.
Time series data and spotting trends over time are obvious fits for stream processing. If you’re trying to determine the length of a web session in a never-ending stream, for example ( this is an example of trying to detect a sequence). It’s difficult to accomplish it using batches because certain sessions will be split into two. Stream processing can readily handle this.
When you take a step back and think about it, time-series data are the most continuous data series: traffic sensors, health sensors, transaction logs, activity logs, and so on. Almost all IoT data is in the form of a time series. As a result, using a programming model that fits organically makes it logical.
Processing Time and Storage
Batch processing allows data to accumulate and attempt to process it all at once, whereas stream processing processes data as it arrives, spreading the processing out over time. As a result, stream processing requires far less hardware than batch processing. Stream processing also allows for approximate query processing through systematic load reduction. As a result, stream processing is well suited to applications where only approximate results are required.
Sometimes data is so large that it is impossible to store it. Stream processing allows you to handle massive amounts of data in a fire-horse-like fashion while retaining only the most important information.
Finally, there is a lot of Streaming Data accessible (for example, customer transactions, activities, and website visits), and it will continue to expand as IoT use cases become more prevalent ( all kinds of sensors). Streaming is a far more natural way to consider and program those scenarios.
What are the Key Use Cases of Stream Processing?
In most use scenarios, event data is generated as a result of some activity, and some action should be taken right away. The following are some examples of real-time Stream Processing applications:
- Fraud and Anomaly Detection in real-time. Thanks to Fraud and Anomaly Detection powered by Stream Processing, one of the world’s leading credit card companies were able to cut fraud write-downs by $800 million per year. Delays in credit card processing are inconvenient for both the end customer and the store attempting to process the card (and any other customers inline). Credit card organizations used to do their time-consuming fraud detection operations in Batches after each transaction. With Stream Processing, businesses can run extensive algorithms to spot and stop fraudulent payments as soon as you swipe your card, as well as trigger alarms for odd charges that require further investigation, without letting their (non-fraudulent) clients wait.
- Edge analytics for the Internet of Things (IoT). Stream Processing is used by organizations in manufacturing, oil and gas, and transportation, as well as those designing smart cities and smart buildings, to keep up with data from billions of “things.” Detecting abnormalities in manufacturing that signal problem that needs to be corrected in order to enhance operations and increase yields is one example of IoT Data Analysis. With real-time Stream Processing, a manufacturer may see if a manufacturing line is producing too many anomalies as they happen, rather than waiting until the end of the shift to discover a whole defective batch. They may save a lot of money and avoid a lot of waste by pausing the line for rapid repairs.
- Personalization, Marketing, and Advertising in real-time. Companies may provide customized, contextual experiences for their customers via real-time Stream Processing. This could be a discount on something you put in your shopping basket but didn’t buy right away, a referral to connect with a newly registered friend on a social network, or an advertisement for a product.
Before Stream Processing: A Batched At-rest Data Framework
This paradigm is turned on its head with Stream Processing: application logic, analytics, and queries exist in real-time, and data flows through them in real-time. A Stream Processing program reacts to an event received from the stream by triggering an action, updating an aggregate or other statistic, or “remembering” the event for future reference.
Streaming Computations can process numerous data streams at the same time, and each computation over the event data stream can result in the creation of new event data streams.
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Batch Processing vs Stream Processing: What is the Difference?
Traditionally, data was processed in Batches according to a schedule or a predetermined threshold (e.g. every night at 1 am, every hundred rows, or every time the volume reaches two megabytes). However, as the pace of data has quickened and the volume of data has grown, Batch Processing is no longer an option for many use cases.
For current applications, Stream Processing has become a must-have. For a number of use cases and applications, businesses have resorted to technologies that respond to data as it is created.
Stream Processing enables programs to react to new data events in real-time. Stream Processing programs collect and process data as it is generated, rather than aggregating it and collecting it at a predetermined frequency as Batch Processing does.
Stream Processing Frameworks: Decoding What they are
A Stream Processing framework is a complete processing system that includes a Dataflow Pipeline that receives streaming inputs and generates actionable, real-time analytics. These frameworks are intended to make the creation of Data Streaming Software such as Stream Processing and event stream processing easier. A developer can quickly include functions from an existing library of tools into a Stream Processing framework, avoiding the need to create a full Stream Processing system from scratch.
Depending on the enterprise environment and use case, many frameworks might be employed. Several of these frameworks are now in use in the enterprise, both commercial and Open Source. While there have been a number of special-purpose Stream Processing frameworks developed, general-purpose Stream Processing frameworks are the most common.
The job of the Stream Processing framework, regardless of the Stream Processing engines utilised, is to accept a pipeline of data as an input, process it, and deliver the results to an output queue known as a sink. The Stream Processing framework will also include its own programming model, as well as a processing system that describes how to interface with and partition data, as well as the Data States, Error Management Controls, and other features.
History of Frameworks
Stream Processing has a long history, dating back to active databases that allowed users to perform conditional searches on data stored in them. TelegraphCQ, which is based on PostgreSQL, was one of the first Stream processing frameworks.
They split into two branches after that.
- Stream Processing is the first sub-branch. These frameworks allow users to design a query graph that connects the user’s code and runs it across multiple machines. Aurora, PIPES, STREAM, Borealis, and Yahoo S4 are other examples. The objective of these stream processing designs was Scalability.
- Complex Event Processing is the second branch. These frameworks support query languages (such as Streaming SQL) and were focused on doing efficient event matching against supplied queries, however, they typically ran on 1–2 nodes. ODE, SASE, Esper, Cayuga, and Siddhi are just a few examples. The focus of these architectures was on fast streaming algorithms.
Both of these areas’ Stream Processing frameworks were limited to academic research or specialized applications like the stock market. Yahoo S4 and Apache Storm brought Stream Processing back into the spotlight. It was described as “similar to Hadoop, but in real-time.” It is incorporated into the Big Data movement. These two branches have united in the last five years.
Stream Processing Architectures: How do these look like?
Stream processors are the systems that receive and send Data Streams as well as execute Application or Analytics Logic. A stream processor’s primary duties are to guarantee that data flows effectively and that computing scalable and is fault resistant.
Apache Flink is a robust, well-established Open-source stream processing framework that addresses these issues.
Many of the issues that developers of real-time Data Analytics and event-driven systems encounter today are naturally addressed by the Stream Processing paradigm:
- Instantly, applications and analytics react to events: There is no time lag between “event occurs” and “understanding is gained” and “action is taken.” Actions and analytics are current, reflecting data while it is still relevant, useful, and valuable.
- Stream Processing can handle substantially greater data volumes than traditional Data Processing systems: Only a significant fraction of the data is saved from the event streams, which are processed directly.
- Stream Processing represents the continuous and temporal nature of most data naturally and easily: This is in contrast to static/resting data analytics and scheduled (batch) queries. The stream processing concept readily suits incrementally computing updates rather than periodic recomputation of all data.
- The infrastructure is decentralized and decoupled with Stream Processing: The streaming model eliminates the need for huge, costly shared databases. Instead, the stream processing framework simplifies the process by allowing each stream processing application to maintain its own data and state. A stream processing application fits neatly into a microservices architecture in this manner.
Stream Processing, sometimes known as Data Processing on its head, is concerned with the processing of a continuous stream of events. A typical stream application has a number of producers that generate new events and a set of consumers who process them. Financial transactions, user activity on a website, and application Metrics are all examples of events in the system. Consumers can aggregate incoming data, send real-time automated warnings, or create new streams of data for other consumers to process.
The following are some of the benefits of this architecture:
- Low Latency: A system’s ability to process and react to new events in real-time.
- Natural Fit: Stream processing systems are a natural fit for many applications that work with a never-ending stream of events.
- Uniform Processing: Stream processing systems conduct computation as soon as fresh data comes, rather than waiting for data to collect before processing the next batch.
Stateful Stream Processing vs Stateless Stream Processing: What is the Difference?
- Stateful Stream Processing is concerned with the overall state of the data, whereas Stateless Stream Processing is not.
- Information about prior events is employed as part of the analysis of current events in a Stateful Stream Processing context. Temperature readings from an industrial machine, for example, are more valuable when seen in aggregate and across time, allowing trends to be identified as they emerge.
- Data is evaluated just as it arrives in Stateless Stream Processing, with no consideration for state or previous knowledge. A Stateless Stream Processing system will suffice if all you need is a real-time feed of the ambient temperature without concern for how it changes. A Stateful Stream Processing system, on the other hand, will be necessary if you wish to forecast future temperatures based on how the temperature has changed over time.
- Coding, Operating, and Scaling Stateful Stream Processing is significantly more difficult. The Stateful Stream Processing system becomes more sophisticated and resource-intensive as the number of streams controlled increases and the data volumes produced by each stream grow. Stateful Stream Processing, on the other hand, is the most used type of Stream Processing in the workplace today since it yields significantly more meaningful insights than Stateless Stream Processing.
What are Stream Processing Software?
While a Stream Processing framework offers the foundation for your analytics, the actual analysis is carried out by Stream Processing Software (or a Stream Processing Application) developed on top of that framework. Using a Stream Processing framework to code multiple Streaming apps saves time and effort. Among the most common applications for Stream Processing Software are:
- Data Gathering, including multi-Cloud Integration and business data in the form of Streams and Messages.
- Data Dissemination, Monitoring, and Detection, as well as enterprise-wide delivery
- Anomaly Detection in real-time
- Aggregation in real-time
- Rule-based Detection and in-stream enrichment
- Data Adherence
- Financial Fraud Detection
- Real-time identification of Criminal Conduct
- System monitoring allows for real-time examination and management of server hardware, networks, applications, and industrial equipment.
- High-speed, algorithmic securities trading
- Monitoring and managing the supply chain
- Intrusion Detection in the network
- Analysis of marketing and advertising initiatives
- Real-time surveillance of customer behavior
- Monitoring and reduction of vehicle traffic
How do you get started with Stream Processing?
You can utilize a tool or build it yourself if you want to create an App that manages Streaming Data and makes real-time decisions. The answer is contingent on the level of complexity you want to handle, how much you want to scale, how much dependability and fault tolerance you require, and so on.
Place events in a message broker topic (e.g. ActiveMQ, RabbitMQ, or Kafka), write code to receive events from topics in the broker (they form your stream) and then publish results back to the broker if you wish to construct the App yourself. An actor is a name given to such a code.
Instead of writing the aforementioned scenario from scratch, you can save time by using a Stream Processing framework. You can build logic for each actor, wire them up, and connect the edges to the data source using an Event Stream Processor (s). You have the option of sending events directly to the stream processor or via a broker.
The hard work will be done by an Event Stream Processor, which will collect data, deliver it to each actor, ensure that they run in the correct order, collect results, scale if the load is high, and handle failures. Storm, Flink, and Samza are just a few examples. Check out the respective user guide if you want to build the app this way.
Since 2016, a new concept known as Streaming SQL has gained traction. A “Streaming SQL” language is one that allows users to write SQL-like queries to query Streaming Data. Many streaming SQL languages are gaining popularity.
Developers can quickly include Streaming queries into their Apps using Streaming SQL languages. By 2018, the majority of Stream Processors could process data using the Streaming SQL language.
Stream Processing & BigData: The Perfect Combination
While Stream Processing in a Big Data ecosystem works in much the same way it does in any other environment, there are a few advantages to Stream Processing in Big Data. Stream Processing’s capacity to do Data Analytics without requiring access to the complete Data Storage is one of them. Batch Processing of a vast Big Data repository is frequently tediously slow because Big Data by definition contains massive databases of Unstructured Data. Stream Processing is a simple technique that may generate real-time insights from large amounts of data in milliseconds or less.
Also, because this data is always changing, a batch procedure can never fully complete a massive Data Repository. The underlying data will continue to change once Batch Processing begins, thus any Batch Report will be out of date once it is completed. For this and other sophisticated Event Processing, Stream Processing is a good answer.
In a large data set, Batch Processing is still useful, especially when long-term, deep insights are required, which can only be achieved by analyzing the full Data Source. Stream Processing is the superior option when faster and more timely analyses are required.
As organizations expand their businesses, managing large volumes of data becomes crucial for achieving the desired efficiency. Streaming Process powers stakeholders and management to handle their data in the best possible way. In case you want to export data from a source of your choice into your desired Database/destination then Hevo Data is the right choice for you!
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