One of the main characteristics of time-series data is its stream-like nature: typically, time-series data is generated at fixed intervals by devices and sensors and then flows at a relatively constant rate into a database for storage. Analyzing these data flows can provide many insights that are impossible to achieve using only historical data, but the ability to do so in real time as data reaches the database is a necessity. This makes stream processing a natural fit for performing real-time analytics in a time-series database.
What Is Stream Processing?
Stream processing is a method of continuously ingesting, processing, and analyzing data in real time as it flows through a system. Unlike batch processing, which collects and processes data in large chunks at scheduled intervals, stream processing operates on individual events as they arrive. This eliminates delays and allows systems to respond dynamically to changes, making it ideal for applications that require low-latency processing.
A key feature of stream processing is its ability to maintain state and track data over time rather than treating each event in isolation. This enables advanced computations such as trend detection, anomaly identification, and aggregation over defined time windows. To ensure accuracy and reliability, stream processing systems often incorporate mechanisms for fault tolerance, such as event replay and checkpointing, allowing them to recover from failures without data loss or duplication.
As data volumes and real-time demands grow, stream processing is becoming a foundational technology for modern data architectures. By enabling continuous processing, it shifts data analysis from a passive, retrospective approach to an active, real-time one, improving efficiency and automation across various domains.
Stream Processing Platforms
There are many stream processing platforms that can be used with time-series data. Four of these are described as follows:
-
Apache Flink operates on a dataflow model where data streams continuously pass through a directed acyclic graph (DAG) of operators, allowing for low-latency, event-driven computations. Flink supports stateful processing, event-time handling, and windowing; ensures fault tolerance through checkpointing; and offers exactly-once or at-least-once processing guarantees.
-
Apache Storm processes data in a distributed manner using a topology-based architecture. It consists of spouts, which ingest data, and bolts, which perform transformations and computations, allowing for flexible stream processing pipelines. Storm operates with at-least-once processing semantics by default and supports fault tolerance through automatic task reassignment upon failure.
-
Apache Kafka is a publish-subscribe messaging system, where producers send data to topics, and consumers process the data in real time. Kafka is built for scalability and durability, using a log-based storage system where messages are retained for a configurable period, allowing for replayability and fault recovery.
-
Amazon Kinesis is a managed cloud service for real-time data streaming, offering scalable ingestion, processing, and storage of high-velocity data. It includes multiple components: Kinesis Data Streams for real-time data flow, Kinesis Data Firehose for automated delivery to storage and analytics services, and Kinesis Data Analytics for real-time stream processing using SQL and Apache Flink.
Applications of Stream Processing
Telecommunications
-
Network Monitoring: Stream processing enables real-time network monitoring by continuously analyzing traffic patterns, detecting anomalies, and identifying potential service disruptions. By processing data from network devices, logs, and sensors as it is generated, operators can quickly detect latency issues, congestion, or security threats like DDoS attacks. Stream processing allows for immediate alerts and automated responses, ensuring high network performance, reduced downtime, and improved user experience without waiting for batch analysis.
-
Fraud Detection: Stream processing plays a critical role in fraud detection by analyzing call records, transaction data, and user behavior in real time. It can identify suspicious activities such as SIM card cloning, unauthorized account access, or irregular call patterns, triggering instant alerts or automated actions to prevent financial loss. By continuously monitoring billions of events per second, stream processing enhances the accuracy and speed of fraud detection, reducing false positives and enabling swift intervention before significant damage occurs.
Energy & Utilities
-
Smart Grid Monitoring: Stream processing enhances smart grid monitoring by continuously analyzing real-time data from sensors, meters, and substations to ensure grid stability and efficiency. It enables instant detection of voltage fluctuations, equipment failures, or power outages, allowing operators to respond proactively before issues escalate. By integrating data from various sources, stream processing supports predictive maintenance, load balancing, and automated fault recovery, improving overall grid reliability and reducing downtime.
-
Renewable Energy Forecasting: Stream processing plays a crucial role in optimizing renewable energy generation by analyzing weather data, sensor readings, and historical patterns in real time. By continuously processing inputs such as solar irradiance, wind speed, and grid demand, it enables accurate short-term forecasting and efficient energy distribution. This helps grid operators balance supply and demand, reduce reliance on backup energy sources, and improve the integration of renewable energy into the power grid.
Manufacturing & IIoT
-
Predictive Maintenance: Stream processing enhances predictive maintenance in manufacturing and Industrial IoT (IIoT) by continuously analyzing real-time sensor data from machinery and equipment. By detecting patterns in temperature, vibration, pressure, and other operational metrics, it can identify early signs of wear, misalignment, or potential failures before they cause unplanned downtime. This enables proactive maintenance scheduling, reduces costly disruptions, and extends equipment lifespan, ultimately improving efficiency and lowering operational costs.
Automotive & Transportation
-
Fleet Management: Stream processing enables real-time fleet management by continuously analyzing GPS data, vehicle diagnostics, and driver behavior. It allows fleet operators to optimize routing, monitor fuel efficiency, detect maintenance needs, and ensure compliance with safety regulations. By processing telematics data instantly, companies can improve vehicle utilization, reduce operational costs, and enhance overall fleet efficiency.
-
Autonomous Vehicles: Stream processing is essential for autonomous vehicles, enabling them to analyze sensor data from cameras, LiDAR, radar, and GPS in real time. By continuously processing this data, vehicles can detect obstacles, make navigation decisions, and respond to changing road conditions instantly. This low-latency processing is critical for ensuring safety, improving driving accuracy, and enabling real-time coordination between autonomous systems and smart infrastructure.
Stream Processing in Time-Series Databases
Many time-series databases provide a similar functionality known as continuous query, a predefined query that runs automatically and continuously on a time-series database as new data arrives. This typically is used to perform aggregation, filtering, or transformation on incoming data. However, there are many situations in which continuous query is intrinsically limited: preprocessing and transformation in scalar functions, session windows, and low-latency use cases like fault detection are all examples of scenarios where continuous query cannot provide equivalent performance to real stream processing.
For this reason, some architects deploy a stream processing platform, like those mentioned above, in their data architecture. Incoming data is first sent to the stream processing platform, and results are delivered to the time-series database. Many time-series databases include connectors for common stream processing products that can help ingest data from them more easily. Finally, some time-series databases like TDengine include stream processing as a built-in component, offering the power of third-party solutions without the overhead.
Learn More
If you’re interested in stream processing and how it can be used with time-series databases to provide real-time insights into your data, join us on Wednesday, February 26 at 10 a.m. Pacific Time for an educational webinar.
