A New Era of Data Engineering Infrastructure: Stream Data Management for Real-Time Operations
Trinity Data Integration Lab
According to research by Google, data engineers spend approximately 45% of their time on data preprocessing. However, based on industry experience, this figure can rise to as much as 70% of the total effort (in both manpower and time) during enterprise data engineering projects. In practice, vendors often implement case-by-case solutions to accommodate specific project needs, typically collaborating with in-house IT teams under tight constraints. From the enterprise perspective, however, given the diversity, scale, and complexity of data, as well as the growing demands of business analysis and operational continuity, a dedicated data analytics department must rely on an integrated data platform to execute efficiently.
ETL (Extract, Transform, Load) platforms emerged in the 1990s in response to the need for structured data integration. Over time, ETL became widely adopted for enterprise-level data analytics. As a batch processing approach, ETL is well-suited to traditional database systems and remains effective for periodic data workflows.
ETL (Extract, Transform, Load) platforms emerged in the 1990s in response to the need for structured data integration. Over time, ETL became widely adopted for enterprise-level data analytics. As a batch processing approach, ETL is well-suited to traditional database systems and remains effective for periodic data workflows.
Adapting to the New Technological Landscape – Stream Data Management
In today’s data- and AI-driven business landscape, the ability to perform efficient and timely analysis is a key competitive differentiator. Traditional ETL models are increasingly inadequate when it comes to meeting modern demands for real-time responsiveness and flexible data handling. This has led to the emergence of a new generation of platforms—Data Pipeline solutions.
A Data Pipeline platform supports two types of data processing: batch processing and stream processing. Batch processing is suited for traditional database data and non-real-time analytical needs, such as scheduled reports. In contrast, stream processing is designed for handling streaming data and real-time analytics, such as monitoring and alert systems.
Unlike ETL’s batch operations, stream processing allows for real-time (or near real-time) data ingestion, transformation, and analysis—without persisting intermediate data to storage. Stream Processing is particularly well-suited to native streaming sources such as message queue systems (Kafka, ActiveMQ, RabbitMQ), web services, and even real-time social media feeds or forum posts. Previously, applying traditional ETL to such sources resulted in poor performance and limited responsiveness. Today, these challenges are directly addressed by stream-native architectures.
A Data Pipeline platform supports two types of data processing: batch processing and stream processing. Batch processing is suited for traditional database data and non-real-time analytical needs, such as scheduled reports. In contrast, stream processing is designed for handling streaming data and real-time analytics, such as monitoring and alert systems.
Unlike ETL’s batch operations, stream processing allows for real-time (or near real-time) data ingestion, transformation, and analysis—without persisting intermediate data to storage. Stream Processing is particularly well-suited to native streaming sources such as message queue systems (Kafka, ActiveMQ, RabbitMQ), web services, and even real-time social media feeds or forum posts. Previously, applying traditional ETL to such sources resulted in poor performance and limited responsiveness. Today, these challenges are directly addressed by stream-native architectures.
Introducing Trinity 5 – The Next-Generation Data Pipeline Platform
Trinity has served the ETL market for over 15 years, continuously evolving its product suite in response to key trends such as big data analytics, centralized IT job scheduling, compliance with personal data protection laws, social sentiment analysis, large-scale address correction following municipal mergers, and enterprise data governance.
Now recognized as a leading domestic brand, Trinity is trusted across industries—including telecommunications, finance, manufacturing, and the public sector—and has increasingly replaced foreign competitors at the enterprise level.
In recent years, we closely monitored global trends in Stream Processing and observed a growing need among our local client base. As a result, two years ago we initiated development of Trinity SDM (Stream Data Management)—a platform purpose-built for real-time data streaming.
Unlike many foreign Data Pipeline platforms that treat ETL and Stream Processing as separate modules (often with distinct core architectures), Trinity SDM was engineered from the ground up to seamlessly integrate with Trinity ETL and the broader Trinity platform. This results in a true unified Data Pipeline architecture—not two siloed systems under one label.
Launched earlier this year, this major leap forward is branded as Trinity 5, complete with a new logo and product identity. Trinity 5 and Trinity SDM have already seen adoption by clients, reinforcing our position in the market.
With its innovative real-time capabilities, localized support, and exceptional cost-performance, Trinity SDM is poised to transform enterprise analytics—enabling rapid adaptation to market changes and securing a competitive edge in today’s data-driven business environment.
Now recognized as a leading domestic brand, Trinity is trusted across industries—including telecommunications, finance, manufacturing, and the public sector—and has increasingly replaced foreign competitors at the enterprise level.
In recent years, we closely monitored global trends in Stream Processing and observed a growing need among our local client base. As a result, two years ago we initiated development of Trinity SDM (Stream Data Management)—a platform purpose-built for real-time data streaming.
Unlike many foreign Data Pipeline platforms that treat ETL and Stream Processing as separate modules (often with distinct core architectures), Trinity SDM was engineered from the ground up to seamlessly integrate with Trinity ETL and the broader Trinity platform. This results in a true unified Data Pipeline architecture—not two siloed systems under one label.
Launched earlier this year, this major leap forward is branded as Trinity 5, complete with a new logo and product identity. Trinity 5 and Trinity SDM have already seen adoption by clients, reinforcing our position in the market.
With its innovative real-time capabilities, localized support, and exceptional cost-performance, Trinity SDM is poised to transform enterprise analytics—enabling rapid adaptation to market changes and securing a competitive edge in today’s data-driven business environment.
Trinity SDM: Capabilities and Applications
Trinity SDM is designed to enhance the performance and flexibility of real-time data stream processing, addressing the growing demands in modern data-driven business environments for instantaneous data capture, transformation, and analysis. The system focuses on the following core capabilities:
- Real-Time Stream Connectors: Supports integration with message queue systems such as Kafka, ActiveMQ, and RabbitMQ, as well as Web services, enabling seamless connectivity with various data producers to ingest data streams in real time.
- Stream Data Transformation and Processing: Offers high-performance transformers and processors that can rapidly convert and manipulate streaming data, facilitating the immediate transformation of raw data into valuable insights.
- Real-Time Stream Monitoring and Visualization: Includes the powerful StreamConsole interface for monitoring data flow and job status in real time. StreamConsole supports live log tracking, job monitoring, and alert notifications, enabling users to manage streaming processes efficiently. Additionally, with the Web JFDesigner frontend, the system provides an intuitive visual representation of data flow and operations to enhance user experience and ease of use.
- Automated Data Control and Maintenance: Built on advanced Spring Boot technology, the platform supports automation of stream job lifecycle management—including auto-start, live monitoring, and auto-shutdown—minimizing manual intervention and improving operational efficiency.
- Logging and Performance Monitoring: Integrates with the ELK (Elasticsearch, Logstash, and Kibana) stack to deliver real-time system log tracking and performance metrics, allowing prompt detection and resolution of potential issues.
- End-to-End Stream Data Monitoring: Enables complete traceability and monitoring of data from source to output, ensuring data stream accuracy and integrity while supporting real-time analysis and decision-making.
- Apache Flink Integration: Seamlessly integrates with Apache Flink to leverage its robust stream processing and transformation capabilities, making Trinity SDM ideal for handling large-scale real-time data streams. Future enhancements will explore integration with Apache Flink’s Complex Event Processing (CEP) features to enable advanced use cases such as real-time pattern recognition and fraud detection.
Trinity 5 offers enterprises a comprehensive data integration and processing solution, combining Trinity ETL for batch processing and Trinity SDM for stream processing into a single, fully integrated platform. As mentioned earlier, Trinity 5 is not merely a bundled solution but a true convergence of batch and stream technologies.
Several existing Trinity ETL clients have evaluated and transitioned their data processing workloads to stream mode using Trinity SDM, citing improved efficiency and operational performance.
Beyond addressing real-time stream processing needs, Trinity SDM offers broader application potential. In response to evolving client and market requirements, future developments will further integrate Apache Flink’s CEP capabilities to support advanced analytics solutions, including systems for fraud detection alerts and other dynamic real-time applications.
Several existing Trinity ETL clients have evaluated and transitioned their data processing workloads to stream mode using Trinity SDM, citing improved efficiency and operational performance.
Beyond addressing real-time stream processing needs, Trinity SDM offers broader application potential. In response to evolving client and market requirements, future developments will further integrate Apache Flink’s CEP capabilities to support advanced analytics solutions, including systems for fraud detection alerts and other dynamic real-time applications.