What is stream processing?
Today, the transfer of information has not only increased in volume but also in speed, making it imperative to be able to process all the high velocity and volume data at low latency or in real-time to be able to extract maximum value.
A stream processing engine allows data collection, transformations such as cleaning and filtering, and analysis such as trend identification, summarization, aggregation as well as training data for machine learning models.
It's possible to have data without information, but it's impossible to have information without data. Data is critical in today’s fast-paced environment where trends and behaviors change instantly. Online businesses need to be on top of consumer behavior and be able to make decisions based on the data acquired from the digital footprints of the customers. Every time a customer clicks or searches for a product, the data is fed into an architecture that recognizes the intended items or content and starts recommending based on the customer’s interests.
An example is when you search for a particular shoe on Facebook, you’ll start coming across ads for sneaker stores and listings.
The internet, however, is a huge place now with billions of users adding up every year, and every user generates massive volumes of data. In addition, there are also IoT devices (Internet of Things) connected to the internet such as cars, refrigerators, security systems, medical appliances, etc. which continue to add to the data treasury.
Stream computing techniques allow us to manage high volumes of data at low-latency with limited computing sources.
Data in motion vs. data at rest
There are two types of data, stationary data, and data in motion/transit or data in rest. Data at rest or stationary data is usually saved in a database. The data has a fixed size and is not subjected to constant change like data in motion. For example, running analysis on performances of a particular sports team from a previous season would be based on data at rest. The data simply has to be extracted from where it was stored before you run different analytical techniques. Fixed datasets used for EDA or exploratory data analysis or training machine learning models for any task are examples of data at rest.
On the other hand, stream processing is based on data that is in motion, performing real-time analytics at low latency. An example of data in motion could be an online gaming platform, which transmits data continuously as the gamer makes progress. The data is important to update player status based on achievements, features to be enabled, and fraud detection such as fake accounts, bots e.t.c. Other sources of streaming data include GPS tracking systems in the truck industry used to track routes as well as sports data interfaces.
Industrial automation frameworks also make use of streaming data in the case of monitoring, for example, when the temperature of a certain chamber or a machine rises or falls below a certain threshold which might require emergency action from the factory management, quality assurance to identify and reject faulty products moving on a supply chain based on their weight, thickness, shape e.t.c.
The financial sector uses automation tools such as trading bots which are continuously being fed information from the market to compute financial formulas and perform actions such as buying and selling stocks/ cryptocurrency at certain thresholds.
Disaster management authorities can also use streaming data processing to monitor and evaluate conditions such as river flows, sea levels and rainfall to issue flood warnings and other predictions for timely actions to minimize loss of property and life.
Batch processing vs stream processing
Stream processing refers to the processing of data as it arrives in a presumably infinite form. Batch processing refers to storing data in fixed quantities before pushing it further down the pipeline.
Batch processing might require multiple CPUs, however, stream processing can achieve the same outcome with a limited amount of memory. Data in batches has to be stored but no storing buffer is required for streaming data as it is processed on the go. For data processed asynchronously, a message queue acts as a communication channel through a pipeline. Message queues are part of a pub/sub architecture that processes data using the publisher-subscriber model. Whether data is analyzed in real-time or stored until the messages can be received, pub/sub is a method of preventing data loss is a streaming engine. Message queues and stream processing are both used for ingesting and analyzing event data.
Batch data is processed in multiple rounds unlike streaming data which is processed in a single or only a few passes at best, hence it has much lower latency which is a key objective for functioning in today’s environment which has a fast-paced information transfer rate.
Macrometa stream processing engine
The Macrometa Global Data Network (GDN) allows seamless integration of streaming data. Typically, stream processing use cases involve collecting data generated during business activities by various sources discussed above such as mobile phones, IoT devices, applications, navigation systems, etc. This data can then be analyzed to identify patterns and extract valuable information. The data can then be acted upon using reactive programming which could execute a code snippet, call an external service, or trigger a complex integration.
Macrometa stream processing engine. Source: Macrometa docs
Benefits of the stream processing engine
- Real-time ETL: Data can be extracted, transformed, and loaded into the servers and integrated using sinks at high speeds for bringing rapid insights.
- Consume and publish events: Events can be consumed and published via a wide range of platforms such as Kafka, HTTP, TCP, MQTT, Amazon SQS, Google Pub/Sub, WebSocket, S3, and Google Cloud Storage. This decoupling using Pub/Sub results in faster development, agility, scalability, and increased reliability.
- Data filtering and cleansing: This helps remove duplicate, incomplete, unwanted, or corrupted data which might lead to problems for data analysis and data scientists.
- Summarization of data: Data summarization techniques can be used to display statistics and comprehensive visuals which can help derive significant insights for data-driven business decisions and identifying shortcomings.
The streaming data also allows smooth integration of Machine learning models such as facial recognition systems in security applications, fraud detection in online shopping, gaming, financial transactions, stock market trading , etc. For more details visit Macrometa’s stream processing documentation.
Stream processing is used for massive volumes of data that is unbounded in nature (i.e. infinite) and instead of arriving in a fixed storage size, arrives in a continuous form. It's then analyzed and acted upon at low latency and in real-time as well.
The rapid processing of data at high volume and velocity allows businesses to make educated and data-oriented decisions to enhance customer experience and increase efficiency.
Learn more in our eBook, The Guide to Stream Processing.