A stream data source is characterized by continuous time-stamped logs that document events in real time. Examples include a sensor reporting the current temperature, or a user clicking a link on a web page. Stream data sources include:
Server and security logs
Click stream data from websites and apps
IoT sensors
Real-time advertising platforms
Therefore, streaming data architecture is a dedicated network of software components capable of ingesting and processing large amounts of stream data from many sources. Unlike conventional data architecture solutions, which focus on batch reading and writing, streaming data architecture ingests data as it is generated in its raw form, stores it, and may incorporate different components for real-time data processing and manipulation.
An effective streaming architecture must account for the distinctive characteristics of data streams which tend to generate large amounts of structured and semi-structured data that requires ETL and pre-processing to be useful. Due to its complexity, stream processing cannot be solved with one ETL tool or database. That’s why organizations need to adopt solutions consisting of multiple building blocks that can be combined with data pipelines within the organization’s data architecture.
Batch processing vs. Real-time stream processing
In batch data processing, data is downloaded in batches before being processed, stored, and analyzed. On the other hand, stream data ingest data continuously, allowing it to be processed simultaneously and in real-time. The complexity of the current business requirements has rendered legacy data processing methods obsolete because they do not collect and analyze data in real-time. This doesn’t work for modern organizations as they need to act on data in real-time before it becomes stale.
The main benefit of stream processing is real-time insight. We live in an information age where new data is constantly being created. Organizations that leverage streaming data analytics can take advantage of real-time information from internal and external assets to inform their decisions, drive innovation and improve their overall strategy. Here are a few other benefits of data stream processing:
Handle the never-ending stream of events natively: Batch processing tools need to gather batches of data and integrate the batches to gain a meaningful conclusion. By reducing the overhead delays associated with batching events, organizations can gain instant insights from huge amounts of stream data.
Real-time data analytics and insights: Stream processing processes and analyzes data in real-time to provide up-to-the-minute data analytics and insights. This is very beneficial to companies that need real-time tracking and streaming data analytics on their processes. It also comes in handy in other scenarios such as detection of fraud and data breaches and machine performance analysis.
Simplified data scalability: Batch processing systems may be overwhelmed by growing volumes of data, necessitating the addition of other resources, or a complete redesign of the architecture. On the other hand, modern streaming data architectures are hyper-scalable, with a single stream processing architecture capable of processing gigabytes of data per second.
Detecting patterns in time-series data: Detection of patterns in time-series data, such as analyzing trends in website traffic statistics, requires data to be continuously collected, processed, and analyzed. This process is considerably more complex in batch processing as it divides data into batches, which may result in certain occurrences being split across different batches.
Increased ROI: The ability to collect, analyze and act on real-time data gives organizations a competitive edge in their respective marketplaces. Real-time analytics makes organizations more responsive to customer needs, market trends, and business opportunities.
Improved customer satisfaction: Organizations rely on customer feedback to gauge what they are doing right and what they can improve on. Organizations that respond to customer complaints and act on them promptly generally have a good reputation. Fast responsiveness to customer complaints, for example, pays dividends when it comes to online reviews and word-of-mouth advertising, which can be deciding factor for attracting prospective customers and converting them into actual customers.
Losses reduction: In addition to supporting customer retention, stream processing can prevent losses as well by providing warnings of impending issues such as financial downturns, data breaches, system outages, and other issues that negatively affect business outcomes. With real-time information, a business can mitigate, or even prevent the impact of these events.
Streaming data architecture: Use cases
Traditional batch architectures may suffice in small-scale applications. However, when it comes to streaming sources like servers, sensors, clickstream data from apps, real-time advertising, and security logs, stream data becomes a vital necessity as some of these processes may generate up to a gigabyte of data per second. Stream processing is also becoming a vital component in many enterprise data infrastructures. For example, organizations can use clickstream analytics to track website visitor behaviors and tailor their content accordingly. Likewise, historical data analytics can help retailers show relevant suggestions and prevent shopping cart abandonment. Another common use case scenario is IoT data analysis, which typically involves analyzing large streams of data from connected devices and sensors. Streaming data architecture: Challenges Streaming data architectures require new technologies and process bottlenecks. The intricate complexity of these systems can lead to failure, especially when components and processes stall or become too slow. Here are some of the most common challenges in streaming data architecture, along with possible solutions. Business Integration hiccups: Most organizations have many lines of business and applications teams, each working concurrently on its own mission and challenges. For the most part, this works fairly seamlessly for a while until various teams need to integrate and manipulate real-time event data streams. Organizations can federate the events by multiple integration points so that the actions of one or more teams don’t inadvertently disrupt the entire system. Scalability bottlenecks: As an organization grows, so do its datasets. When the current system is unable to handle the growing datasets, operations become a major problem. For example, backups take much longer and consume a significant number of resources. Similarly, rebuilding indexes, reorganizing historical data, and defragmenting storage becomes more time-consuming and resource-intensive operations. To solve this, organizations can check the production environment loads. By test-running the expected load of the system using past data before implementing it, they can find and fix problems. Fault Tolerance and data guarantees: These are crucial considerations when working with stream processing or any other distributed system. Since data comes from different sources in varying volumes and formats, an organization’s systems must be able to stop disruptions from any point of failure and effectively store large streams of data. Components of streaming data architecture: Streaming data architectures are built on an assembly line of proprietary and open-source software solutions that address specific problems such as data integration, stream processing, storage and real-time analysis. Here are some of its components: Message broker (Stream Processor): This message broker collects data from a source, also known as a producer, converts it to a standard message format, and then streams it for consumption by other components such as data warehouses, and ETL tools, among others. Despite their high throughput, stream processors don’t do any data transformation or task scheduling. First-generation stream processors such as Apache ActiveMQ and RabbitMQ relied on the Message Oriented Middleware (MOM) paradigm. These systems were later replaced by hyper-format messaging platforms (stream processors), which are better suited for a streaming paradigm.
Unlike the legacy MOM brokers, message brokers hold up high-performance capabilities, have a huge capacity for message traffic, and are highly focused on streaming with minimal support requirements for task scheduling and data transformations. Stream processors can act as a proxy between two applications whereby communication is achieved through ques. In that case, we can refer to them as point-to-point brokers. Alternatively, if an application is broadcasting a single message or dataset to multiple applications, we can say that the broker is acting as a Publish/Subscribe model.
Batch and real-time ETL tools
Stream data processes are vital components of the big data architecture in data-intensive organizations. In most cases, data from multiple message brokers must be transformed and structured before the data sets can be analyzed, typically using SQL-based analytics tools
This can also be achieved using an ETL tool or other platform that receives queries from users, gathers events from message queues, then generates results by applying the query. Other processes such as performing additional joins, aggregations, and transformations can also run concurrently with the process. The result may be an action, a visualization, an API call, an alert, or in other cases, a new data stream.
Due to the sheer volume and multi-structured nature of event streams, organizations typically store their data in the cloud to serve as an operational data lake. Data lakes offer long-term and low-cost solutions for storing massive amounts of event data. They also offer a flexible integration point where tools outside your streaming data architecture can access data.
After the stream data is processed and stored, it should be analyzed to give actionable value. For this, you need data analytics tools such as query engines, text search engines, and streaming data analytics tools like Amazon Kinesis and Azure Stream Analytics.
Streaming architecture patterns
Even with robust streaming data architecture, you still need streaming architecture patterns to build reliable, secure, scalable applications in the cloud. They include:
Idempotent Producer: A typical event streaming platform cannot deal with duplicate events in an event stream. That’s where the idempotent producer pattern comes in. This pattern deals with duplicate events by assigning each producer a producer ID (PID). Every time it sends a message to the broker, it includes its PID along with a monotonically increasing sequence number.
Event Splitter: Data sources mostly produce messages with multiple elements. The event splitter works by splitting an event into multiple events. For instance, it can split an eCommerce order event into multiple events per order item, making it easy to perform streaming data analytics.
Event Grouper: In some cases, events only become significant after they happen several times. For instance, an eCommerce business will tempt parcel delivery at least three times before asking a customer to collect their order from the depot. The business achieves this by grouping logically similar events, then counting the number of occurrences over a given period.
Claim-check pattern: Message-based architectures often have to send, receive and manipulate large messages, such as in video processing and image recognition. Since it is not recommended to send such large messages directly to the message bus, organizations can send the claim check to the messaging platform instead and store the message on an external service.
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