Introduction
Learning efficient streaming data architectures is challenging, and leveraging streaming data for analytics is indeed arduous? ? When deciding between stream processing and an OLAP (Online Analytical Processing) database for your big data analytics needs, consider the following factors: Does the data require real-time analysis, such as monitoring website traffic or tracking stock prices? Open supply or totally managed?
This blog series aims to demystify streaming data and provide engineering leaders with a guide for integrating streaming data into their analytics pipelines.
The upcoming sequence will cover.
- This submission will cover the fundamentals: streaming data formats, platforms, and use cases?
- Stream processing enables organisations to react to changing circumstances in near real-time, whereas traditional analytics is often based on batch processing, providing insights hours or even days later. Key differences include the speed of data ingestion, the focus on event-driven versus snapshot-based analysis, and the need for stream processing systems to handle high volumes of constantly arriving data.
- Will provide suggestions for operationalizing streaming information alongside several patterning architectures.
To navigate through this submission efficiently, take advantage of our table of contents located on the left side of the text.
What Is Streaming Information?
What’s streaming information refers to data about internet traffic and online activities. Real-time data is a continuous and boundless flow of information produced at an extraordinary pace and transmitted to a system or application. A notable illustration is clickstream data, detailing a user’s online interactions on a website. In a manufacturing environment, another application of IoT technology can involve collecting sensor data. One consistent theme runs through these instances: the rapid creation of substantial data in real-time.
Items of information streaming from data sources can be viewed as instances or events, akin to entries in a database, with distinct characteristics that set them apart. Occasion data is often unorganized or partially organized, stored in a hierarchical structure similar to JSON or AVRO. Occurrences typically comprise a key, a value potentially featuring embedded sub-components, and a timestamp indicating the relevant date and time. Secondly, occasions are frequently immutable – a crucial aspect in the unfolding narrative. Rarely do isolated events provide sufficient insight into the current state of a system. While occasion streams can be effective in sharing specific updates, such as “A cheeseburger was bought”, they tend to fall short when attempting to provide broader insights like “what number of cheeseburgers have been bought so far”? Ultimately, the primary consequence of this rapid-fire data surge is exclusivity, driven by the sheer velocity and volume of information, which demands its availability within a database shortly following the event’s occurrence.
Streaming services have been around for many years. By the early 1990s, IP had started to gain significant momentum as telecommunications companies leveraged it to manage the flow of both voice and data traffic across their networks. Instantly, a plethora of data is omnipresent. The technology has diversified across various sectors and applications, incorporating data from IoT sensors, financial metrics, internet analytics, gaming behavior patterns, and numerous other utilization cases. Real-time data has become a crucial component of analytics operations, enabling swift reactions to events that can significantly impact a company’s revenue. Real-time analytics on streaming data enable organisations to pinpoint patterns and outliers, develop revenue streams and respond to shifting dynamics with unparalleled agility. Despite these challenges, streaming data presents a unique conundrum for analytics, necessitating the adoption of specialized technologies and methodologies to effectively process and gain insights from this complex and dynamic information. Here is the rewritten text:
To get started, let’s lay the groundwork by exploring the basics of operationalizing streaming data, including formats, platforms, and practical applications.
Streaming Information Codecs
Several widespread general-purpose streaming data encoders exist. Each format’s unique characteristics necessitate evaluation and consideration, as its strengths and weaknesses can impact specific usage scenarios. We will briefly highlight these and then transition them onto streaming platforms.
JSON (JavaScript Object Notation)
A lightweight, text-based format that’s often easy to learn, this makes it a popular alternative for information sharing. JSON’s core characteristics include?
- JSON being human-readable and easily comprehensible simplifies debugging and troubleshooting processes.
- JSON is widely supported by numerous programming languages and frameworks, making it an excellent choice for ensuring interoperability between diverse technologies.
- JSON allows for flexible schema design, making it an ideal choice for handling dynamic data that may evolve over time.
JSON provides a convenient alternative for APIs or diverse interfaces handling vast amounts of data in various formats. An e-commerce website may utilize JSON to facilitate communication between its frontend and backend servers, as well as with third-party providers offering logistics or payment services.
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Avro
Avro is a lightweight, compact binary data format optimized for efficient serialization and deserialization, ideal for environmentally conscious information exchange. You can also format Avro messages in JSON. Here is the revised text in a different style:
Avro’s key characteristics include:
- Avro’s compact binary format significantly enhances efficiency by minimizing the consumption of community bandwidth.
- Avro’s well-defined schema ensures kind security and robust data validation capabilities.
- Avro’s schema can remain up to date without necessitating a modification to the consumer code.
Avro presents a compelling alternative for large-scale information platforms seeking to process and analyze vast quantities of log data efficiently. Avro proves to be a valuable tool in efficiently storing and transmitting data while offering robust schema support.
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x16cheeseburgerx02xdcx07x9ax99x19x41x12xcdxccx0cx40xcexfax8excax1f Protocol Buffers: Efficient Serialization for Modern Data Exchange
Protobuf is a lightweight, platform-agnostic binary format optimised for efficient serialization and deserialization of structured data, similar in design to Avro. Some traits of protobuf embody:
- Protobuf is engineered to be significantly more compact than other serialization formats, thereby further amplifying its efficiency by reducing network bandwidth usage.
- Protobuf’s well-defined schema enables robust typing and facilitates information validation.
- Protobuf enables side-by-side compatibility, ensuring that changes to the schema do not compromise existing code that leverages the information.
Protobuf could be particularly well-suited to a real-time messaging system that needs to handle enormous volumes of messages in real-time. The format proves well-suited for efficiently encoding and decoding message data, while also offering a significant advantage in terms of its condensed size and rugged typing capabilities.
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The need for a flexible and adaptable approach is evident; therefore, the solution must be versatile enough to accommodate various scenarios. When integrating new data streams, ensure you consider factors such as the volume of information, its processing requirements, and potential compatibility issues with various methodologies? JSON offers the broadest assistance and affords the most adaptability, making it a popular choice for data interchange and storage.
Streaming information platforms
Let’s explore ways to disseminate, process, and leverage this knowledge effectively. Where streaming platforms are accessible. Streaming platforms offer immense opportunities for in-depth exploration. While this weblog will not delve deeply into specific platforms, it will explore popular options, highlight top-level differences between them, and offer several key considerations for choosing the right platform for your unique situation?
Apache Kafka, an open-source distributed streaming platform, enables real-time processing of massive data volumes with unparalleled efficiency. This is widely considered the most popular and user-friendly streaming service, offering an extensive library of content from various genres. The technology provides a comprehensive suite of features, including data streaming, storage, and processing capabilities, making it a popular choice for building real-time data pipelines and messaging architectures that meet the demands of modern applications. This framework supports various information processing styles, including stream and batch processing (as described in part two of this series), as well as advanced event processing. Kafka’s immense popularity and widespread adoption create a vast community of users who share best practices and offer support, making it an exceptionally effective solution. Offering a wide range of deployment options allows for significant flexibility and adaptability in meeting diverse customer needs. A couple of noteworthy factors:
- Self-managed Kafka deployments can be executed both on-premises and within a cloud infrastructure. While the open-source nature of this supply makes it freely accessible, a warning is warranted regarding its inherent complexity, which demands significant in-house expertise to effectively utilize.
- Kafka can be deployed as a managed service via Confluent Cloud or Amazon Web Services’ Managed Streaming for Kafka (MSK). With each of these options, simplification of deployment and scaling is significantly achieved. You’ll likely be organized in just a few clicks.
- Kafka lacks inherent tools to execute analytics on occasion-related data.
Amazon provides a fully managed, real-time data streaming service through its cloud platform, AWS. Designed to collect, process, and analyze enormous volumes of real-time streaming data with the same immediacy as Kafka. Notwithstanding certain notable disparities between Kafka and Kinesis, the most significant difference lies in the fact that Kinesis is a proprietary and fully managed service offered exclusively by Amazon Web Services (AWS), underscoring its distinct value proposition. One significant benefit of Kinesis’ proprietary nature is the ability to seamlessly stream data for downstream processing and storage on platforms similar to Amazon’s suite, including S3, Redshift, and Elasticsearch. Seamlessly integrated with multiple AWS services like AWS Lambda, AWS Glue, and Amazon SageMaker, this solution enables effortless orchestration of end-to-end streaming data processing pipelines without requiring manual handling of underlying infrastructure. While there are some caveats to consider, they may prove crucial in certain usage scenarios.
- While Kafka is versatile in supporting multiple programming languages alongside Java, Python, and C++, Kinesis tends to excel in aiding Java-based applications as well as other JVM-compiled languages.
- While Kafka offers essentially infinite retention of data, Kinesis defaults to storing information for up to seven days.
- The kinesiology-based wearable device shouldn’t be tailored to accommodate a large number of users.
While each of these fully managed providers from Microsoft leverages streaming information built upon Microsoft Azure, distinct differences emerge in terms of design and performance. While there’s ample substance to create a compelling blog post, let’s touch on the key differences at a high level.
Azure Event Hubs is a highly scalable data streaming platform engineered to process, transform, and analyze immense volumes of real-time data efficiently.
This architecture excels at building information pipelines that seamlessly integrate data from diverse sources, such as IoT devices, clickstream logs, social media platforms, and more. Optimized for high-performance data streaming scenarios, Occasion Hubs can efficiently process hundreds of thousands of events per second, ensuring fast and reliable information dissemination?
Azure Service Bus is a reliable messaging service that enables dependable message queuing and publish-subscribe messaging patterns. This architectural style enables loose coupling and facilitates asynchronous interactions between software components, promoting scalability, maintainability, and fault tolerance. Service Bus facilitates a variety of messaging patterns and is specifically designed to ensure reliable message delivery. While the solution may handle high-volume scenarios, its primary emphasis lies in messaging, where real-time and stream processing are not typically essential.
Like Amazon Kinesis’ seamless integration with various AWS services, leveraging Azure Event Hubs or Azure Service Bus can be excellent choices for applications built on the Microsoft Azure platform.
Real-time analytics enable precise monitoring of streaming data, allowing for swift decision-making and targeted optimization.
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With a solid foundation in streaming data formats and distribution platforms established, our focus shifts to leveraging this data for real-time analytics. Now, let’s shed light on how leading organizations are actually applying streaming data in their day-to-day operations.
Organizations are leveraging streaming data to power real-time personalization engines across industries such as eCommerce, adtech, and media, among others. Here’s an inferred profile of someone interested in purchasing a product: they’re likely to be enthusiastic about literature and possibly a history buff with a special interest in Charles Darwin’s expedition to the Galapagos Islands? As a direct consequence of the proliferation of streaming information platforms, companies are increasingly leveraging this infrastructure to collect and disseminate vast amounts of data in real-time, thereby enabling them to infer customer intent and make informed predictions about their future viewing preferences. Rockset has garnered significant interest in this use case, with companies capitalizing on real-time data to deliver highly personalized user experiences.
Fraud and anomaly detection represent just a few examples of the many valuable uses cases for real-time analytics on streaming data. Organisations are harnessing the power of streaming data to capture individual behaviour, combining this with historical insights and leveraging online processing hubs to identify aberrant or fraudulent activity. As predicted, the adoption of this use case has become increasingly prevalent in fintech and funds firms seeking to gain a real-time competitive advantage through enhanced alerting and monitoring capabilities.
Online video games typically produce vast amounts of data that are increasingly being leveraged in real-time analytics. By capitalizing on real-time data streams, game developers can fine-tune their matchmaking algorithms to ensure players of comparable skill levels are paired together, fostering a more enjoyable gaming experience. Studios have the capability to boost participant engagement and retention by leveraging powerful tools like dwell metrics and leaderboards. Occasionally, event streams can be leveraged to help identify aberrant behavior linked to fraudulent activities.
The logistics industry is another massive consumer of streaming data. By leveraging a reliable real-time analytics platform, fleet operators can effectively monitor and manage their assets, receive timely alerts on equipment health, and proactively implement maintenance strategies to ensure optimal fleet performance and minimize downtime? Moreover, superior logistics leverage real-time data from GPS devices, order management systems, and supply chain schedules to optimize delivery routes.
Streaming information can be leveraged to underpin event-driven architectures, harmoniously aligning with the principles of domain-driven design. Streaming information provides a continuous flow of events that microservices can consume in real-time, replacing traditional polling-based updates. Occurrences can represent changes in a system’s status, individual actions, or domain-specific data. By modeling the system through events, you can achieve loose coupling, scalability, and flexibility.
Real-time data from various systems across an organization can be combined with log information using streaming technology. Logs can be streamed to a centralized platform, typically an OLAP database, where they are processed and analyzed for purposes such as alerting, troubleshooting, monitoring, and others.
Conclusion
Throughout our blog, we’ve explored a wide range of topics, including codecs, platforms, and use cases, but there’s still more to uncover.
While there may be intriguing differences among real-time analytics on streaming data, stream processing, and streaming databases, our focus in this instalment of the series will centre on the latter. While getting started with real-time analytics on streaming data, Rockset provides seamless integration with popular platforms through its built-in connectors for Kafka, Confluent Cloud, Managed Streaming for Apache Kafka (MSK), and more. Start earning money now with just $300 in credit – no bank card needed!
