What Is Real-Time Streaming ETL? Examples and Architecture
Businesses generate vast amounts of data every second and traditional ETL processes often struggle to keep up. Streaming ETL changes the game by continuously processing data in real time, allowing organizations to act on insights the moment they become available.
Whether it’s fraud detection in finance, real-time patient monitoring in healthcare, or optimizing supply chains in retail, streaming ETL is transforming industries. This article explores what streaming ETL is, how it works, and how businesses can take advantage of its benefits.
What is streaming ETL?
Streaming ETL (extract, transform, load) is the continuous process of extracting, transforming, and loading data in real time. Unlike traditional ETL, which processes data in scheduled batches, streaming ETL works with data as it is generated, allowing businesses to react instantly to new insights.
Traditional ETL follows a batch processing model, where data is extracted at scheduled intervals, transformed, and then loaded into a destination system. This method is effective for historical reporting but lacks the ability to provide real-time insights.
In contrast, streaming ETL continuously ingests and processes data, making it possible to act on changes as they happen. This approach is particularly useful in scenarios where real-time decision making is essential, such as financial fraud detection, predictive maintenance in manufacturing, or personalized customer experiences in retail.
As a result, businesses benefit from reduced latency, improved operational efficiency, and a more agile approach to data management.
Benefits of streaming ETL
Streaming ETL provides several advantages that make it an essential component of modern data strategies. By processing data as soon as it arrives, organizations can leverage real-time analytics and drive faster decision-making. Some key benefits include:
Real-time analytics
Streaming ETL ensures that insights are always current. This is particularly useful in industries like supply chain logistics, where real-time adjustments can prevent bottlenecks and optimize operations.
Consistent data integrity
Continuous monitoring and processing of data help identify and correct inconsistencies as they occur. This reduces errors and ensures that organizations always have clean, reliable data for decision making.
Adaptability to data volume
Streaming ETL platforms can scale horizontally, distributing workloads efficiently to handle large volumes of data. Some systems also use in-memory processing to manage data surges without overwhelming storage infrastructure.
Cost savings
Because streaming ETL processes each data event as it happens, organizations can avoid the high costs associated with running frequent batch operations on large-scale infrastructure.
Seamless integration across platforms
Streaming ETL can ingest and process data from multiple sources, including cloud platforms, IoT devices, and traditional databases. This makes it easier to consolidate data from different systems into a unified view.
In-depth insights
Streaming ETL enables real-time data enrichment by integrating incoming data with historical records or external sources. This provides deeper insights for predictive analytics, anomaly detection, and trend forecasting.
By adopting streaming ETL, businesses can enhance agility, improve operational efficiency, and ensure that decision-makers have access to the most up-to-date information available.
Batch ETL vs. streaming ETL
While batch ETL remains useful for historical data analysis, streaming ETL is becoming the go-to solution for organizations needing real-time insights. Batch ETL operates on a scheduled basis, meaning data is processed in intervals ranging from minutes to hours. This can create delays in decision-making, as insights are only available after the batch process completes.
Streaming ETL, on the other hand, processes data continuously, significantly reducing latency. This allows businesses to act on the latest data without waiting for scheduled updates. For example, a retail company using batch ETL might only update inventory levels once a day, whereas a company leveraging streaming ETL can adjust stock levels in real time based on live sales data.
While batch ETL is simpler and often requires fewer resources, streaming ETL provides the immediacy needed for modern data-driven decision-making.
| Feature | Batch ETL | Streaming ETL |
| Processing Method | Batch-based | Continuous |
| Latency | Minutes to hours | Milliseconds to seconds |
| Data Sources | Static data sets | Real-time streams |
| Use Cases | BI dashboards, reports | Fraud detection, IoT analytics |
The streaming ETL process
The streaming ETL process follows a structured sequence of steps, ensuring that data is continuously extracted, transformed, and loaded with minimal delay. The following steps outline the stages involved in streaming ETL:
- Data extraction: The process begins with capturing data from real-time sources such as IoT devices, APIs, application logs, and event streams. Unlike batch processing, where data is extracted at scheduled intervals, streaming ETL continuously pulls data as it is generated.
- Data ingestion: Once extracted, the data flows into a message broker like Apache Kafka or AWS Kinesis. These tools ensure smooth data transfer and provide fault tolerance by managing high-velocity data streams.
- Data transformation: The raw data undergoes real-time transformations, which may include:
- Data validation to ensure accuracy
- Filtering to remove irrelevant or redundant information
- Aggregation to consolidate multiple data points
- Enrichment by integrating external data sources for enhanced insights
- Data loading: The transformed data is then delivered to its destination system, which could be a cloud data warehouse, a business intelligence tool, or an alerting system that triggers automated responses based on real-time analytics.
- Continuous monitoring and optimization: The final step involves monitoring the ETL pipeline for errors, bottlenecks, or data inconsistencies. Performance tuning and scaling adjustments ensure that the pipeline remains efficient and responsive to changes in data volume.
By structuring streaming ETL in a step-by-step manner, organizations can ensure that data flows seamlessly from generation to actionable insights, maximizing efficiency and agility
Streaming ETL architecture
A well-structured streaming ETL architecture consists of several key components working together to process and manage real-time data efficiently.
Data sources: These include IoT sensors, application logs, financial transactions, and cloud services that generate raw data needing to be processed in real time.
Message brokers: These include tools that capture and queue data, ensuring smooth and scalable ingestion into the pipeline.
Processing engines: Technologies transform data in motion, filtering, aggregating, and enriching it before delivery.
Storage solutions: These are cloud-based data warehouses such as Snowflake or Amazon Redshift that store transformed data, making it accessible for analytics and reporting.
Analytics and visualization: Data platforms like Domo provide dashboards and real-time alerts, enabling immediate action on streaming data insights.
Integration with existing systems: Streaming ETL works alongside batch ETL and traditional databases, creating a hybrid data infrastructure that balances real-time and historical analysis.
Examples and use cases of streaming ETL
There are many different ways streaming ETL can have an impact on companies across different industries to boost how they use data to benefit their business.
Manufacturing
Traeger Grills, renowned for pioneering wood pellet grill technology, faced challenges with fragmented data across various systems, hindering real-time decision-making. By integrating Domo’s platform, Traeger consolidated data from sources like Salesforce and Google Analytics into a unified, real-time view. This transformation enabled executives to access up-to-date insights instantly, facilitating proactive management and rapid response to emerging trends.
For other companies in the manufacturing sector, streaming ETL can further enhance such transformations by continuously processing data from production lines, supply chains, and quality control systems. This real-time data flow allows manufacturers to promptly address equipment malfunctions, optimize inventory levels, and improve product quality, leading to increased operational efficiency.
Healthcare
GE Healthcare, a leader in medical technology and diagnostics, sought to improve data sharing and real-time analytics across its finance division. By adopting Domo, GE Healthcare established a single source of truth, enabling rapid data sharing and reporting on a global scale. This shift empowered business users across finance and operations to leverage real-time data for strategic decision-making.
Implementing streaming ETL in healthcare can further enhance such initiatives by enabling continuous processing of patient monitoring data, electronic health records, and medical device outputs. This real-time data integration facilitates immediate clinical decision-making, early detection of patient deterioration, and efficient management of healthcare resources, ultimately improving patient outcomes.
Professional services
CAE USA, a prominent aviation training company, sought to increase data awareness and operational efficiency. Utilizing Domo, CAE USA integrated data across departments, enhancing project management, training programs, and data security. The platform’s capabilities allowed for faster ETL processes, reducing data flow times from hours to minutes, thus enabling timely insights and decision making.
In the professional services industry, streaming ETL can further optimize operations by continuously integrating data from client interactions, project management tools, and financial systems. This real-time data processing supports immediate resource allocation, rapid response to client needs, and dynamic project adjustments, enhancing service delivery and client satisfaction.
Other industries that can benefit from streaming ETL include:
Finance:
Fraud detection systems analyze transactional data as it happens, identifying anomalies and blocking suspicious activities before they escalate.
E-commerce:
Customer interactions are analyzed instantly to provide personalized recommendations, optimize pricing, and adjust inventory in real time.
Internet of things (IoT):
Smart cities, connected vehicles, and industrial automation rely on streaming ETL to process sensor data and trigger automated actions.
High-frequency trading:
Stock market algorithms use streaming ETL to analyze real-time market fluctuations and execute trades within milliseconds.
Precision agriculture:
IoT sensors in farming continuously monitor soil moisture and weather conditions, automating irrigation for maximum efficiency.
By adopting streaming ETL, organizations across these industries can achieve real-time data integration, leading to improved operational efficiency, enhanced decision-making, and a competitive advantage in their respective markets.
How to get started with streaming ETL
Getting started with streaming ETL can be done even without a lot of data expertise or IT know-how. You really need to understand how you want to use your data and make sure you get the right tools in place. Your company can get started by:
- Assessing your real-time data needs and identifying key use cases.
- Choosing technologies that support streaming ETL, like Domo’s flexible data platform.
- Designing a robust, scalable ETL architecture that integrates with existing data systems (this can be custom via some data and IT expertise, or some tools have out-of-the-box features to help establish real-time data connections).
- Testing and deploying streaming ETL pipelines while continuously monitoring performance and optimizing workflows.
If you’re in the market for a streaming ETL solution, Domo is a solid option for teams that want an intuitive and scalable way to integrate real-time data processing into business operations. With powerful automation, flexible data pipelines, and seamless integration across platforms, Domo empowers organizations to connect data from multiple real-time sources, process and transform data instantly, and gain actionable insights through AI-driven analytics and visualization.
Ready to leverage streaming ETL for real-time insights and decision-making? Contact Domo today to learn how our platform can help you integrate, process, and analyze data in real time
