In today’s data-driven world, the ability to process and analyze data in real time has become a critical competitive advantage for modern businesses. Event Stream Processing (ESP) is at the forefront of this transformative shift, enabling organizations to make instantaneous, data-informed decisions.
Gartner released its latest Market Guide for Event Stream Processing (ESP) back in May 2023, but it’s time to take another look even just six months later. This comprehensive report provides valuable insights into event stream processing, a critical aspect of real-time data analytics and integration. The key findings and recommendations from the report are excellent guideposts to help data and analytics leaders navigate the evolving landscape of ESP platforms.
Why ESP platforms matter for modern business
The market for ESP platforms comprises software subsystems designed explicitly for real-time computation on streaming event data. These platforms continuously perform calculations on unbounded input data as it arrives, allowing organizations to respond immediately to current situations. Input data sources span a broad spectrum, including clickstreams, business transactions, social media posts, market data feeds, sensor data from physical assets, and more.
The relevance of ESP extends across diverse industries, encompassing customer engagement, Internet of Things (IoT), financial services, supply chain, transportation operations, IT infrastructure, and beyond. ESP platforms simplify the implementation of high-volume, low-latency, or complex stream analytics systems, processing data in motion seamlessly.
Understanding Event Stream Processing is pivotal for modern businesses for several compelling reasons:
- Real-Time Decision-Making: ESP platforms enable organizations to make decisions in real time. In today’s fast-paced business environment, the ability to react to changing conditions promptly can lead to a competitive edge.
- Data-Driven Insights: ESP empowers businesses to derive valuable insights from streaming data sources, enabling them to uncover hidden trends, respond to customer behaviors, and optimize operations on the fly.
- Enhanced Customer Engagement: For industries like e-commerce, social media, and customer support, ESP facilitates real-time customer engagement, personalization, and feedback analysis.
- IoT and Supply Chain Optimization: In IoT and supply chain management, ESP allows organizations to monitor, manage, and optimize operations as they happen, leading to improved efficiency and reduced costs.
- Financial Services: In financial markets, ESP platforms can analyze real-time market data feeds, detect anomalies, and trigger automated trading decisions, which is crucial for staying competitive.
- Operational Efficiency: ESP platforms contribute to operational efficiency by automating responses to events, reducing manual interventions, and enhancing overall situational awareness.
- Competitive Advantage: Businesses that harness ESP gain a competitive edge by reacting swiftly to market changes, ensuring better customer experiences, and staying ahead of their competitors.
- Adaptability: As the volume of streaming data continues to grow, the ability to process this data in real time is essential for organizations looking to adapt to evolving customer preferences and market conditions.
Event Stream Processing is not just a technological trend but a strategic imperative for modern businesses. Understanding the market, its components, and the capabilities it offers is crucial for organizations aiming to harness the power of real-time data analytics, make informed decisions, and maintain a competitive edge in today’s dynamic business landscape.
Gartner’s main recommendations
Based on the findings of the Market Guide, Gartner offers several recommendations for data and analytics leaders:
- Use ESP Platforms for High-Volume, Low-Latency Applications: ESP platforms excel in high-volume, low-latency, event-driven applications that require complex temporal, order-sensitive, or geospatial logic.
- Develop Custom Streaming Applications on ESP Platforms: If off-the-shelf applications, analytical tools, or SaaS offerings do not meet specific real-time business requirements, consider building custom streaming applications directly on an ESP platform.
- Specify Business Requirements Collaboratively: Work closely with business decision-makers to define latency, processing logic, and the volume and type of input data. Stream analytics can significantly enhance situation awareness, decision accuracy, and response times in operational business systems.
Several key forces are driving changes in the adoption of ESP platforms:
- Proliferation of Real-Time Streaming Data: Organizations are witnessing a continuous increase in streaming data from internal and external sources. ESP platforms are crucial in leveraging this data for real-time business decisions.
- Success of Open-Source and Open-Core ESP: Open-source ESP technology has reduced the cost of stream processing for user organizations and software vendors. Open-core ESP products, bundled with open-source ESP platforms, have gained traction in the market.
- Expansion of Cloud Computing: Cloud computing has made ESP more accessible to businesses, offering benefits like scalability, self-provisioning, continuous patching, and offloading high availability and disaster recovery efforts. Cloud ESP services are now available from various providers.
- Integration of AI: Organizations are increasingly incorporating machine learning, business rule processing, and other analytics within ESP applications to enhance decision intelligence.
Two major trade-offs shape the ESP market:
Data in Motion vs. Data at Rest: ESP platforms process data in motion, enabling the elimination of irrelevant data without external storage, reducing data volume and latency. This approach is ideal for scenarios involving interaction data, business data, telemetry data, and more.
Buy vs. Build: Organizations can build custom streaming applications from scratch or opt for ESP platforms. While the build approach offers flexibility, it requires specialized expertise and time. Many vendors also face the buy-vs-build decision, with some embedding ESP platforms to reduce development costs and time to market.
Data in Motion or at Rest
ESP Platforms: These platforms excel at processing data in motion, allowing the discarding of irrelevant data without storing it, minimizing data volume, and reducing latency. They are well-suited for scenarios involving interaction data, business data, and telemetry data, among others.
ABI Platforms: Most ABI products implement data-at-rest streaming by reading data from an event broker or loading data into a database before querying it. However, some ABI products offer true data-in-motion streaming, continuously ingesting and updating queries and user displays with minimal delays.
DBMSs: Various high-performance DBMSs support high-volume, low-latency stream processing applications with data-at-rest architecture. They continuously ingest streaming data, making it immediately available for applications or analytics.
Unified Real-Time Platforms
Unified platforms combine ESP capabilities with a DBMS or in-memory data grid and a programmable application engine. They support both streaming data in motion and historical data at rest, making them suitable for various low-latency applications.
Unified platforms are used in real-time customer engagement, network monitoring, transportation operations, supply chain management, financial fraud detection, and more. Vendors employ buy-and-build strategies for their stream processing capabilities, offering diverse architectures and performance characteristics.
Gartner’s Market Guide for Event Stream Processing is certainly worth another look. With the proliferation of real-time streaming data and the success of open-source ESP, organizations have more options than ever to harness the power of streaming data for real-time decision-making.
Elizabeth Wallace is a Nashville-based freelance writer with a soft spot for data science and AI and a background in linguistics. She spent 13 years teaching language in higher ed and now helps startups and other organizations explain – clearly – what it is they do.