What is Streaming ETL? 

Streaming ETL is a modern approach to extracting, transforming, and loading (ETL) that processes and moves data from source to destination in real-time. It relies on real-time data pipelines that process events as they occur. Events refer to various individual pieces of information within the data stream. Depending on the source and purpose of the data, an event could be a single user visit to a website, a new post on a social media platform, or a data point from a temperature sensor.

In other words, whenever the source systems generate data, the streaming ETL system, or platform, automatically extracts, transforms, and loads it into the target system. As data flows through the pipeline, the system performs various operations such as filtering, routing, and mapping, enabling immediate feedback and real-time analytics based on the latest data.

Streaming ETL Architecture 

Traditional and streaming ETL are similar concepts, but streaming ETL utilizes a real-time processing architecture. In conventional ETL, data comes from a source, is stored in a staging area for processing, and then moves to the destination (data warehouse). In streaming ETL, the source feeds real-time data directly into a stream processing platform.

This platform acts as a central engine, ingesting, transforming, and enriching the data as it moves. The processed data can then be delivered to data warehouses or data lakes for analysis. The data can also be routed back to the source to provide real-time feedback.  

The design of a streaming ETL architecture relies on five logical layers.  

1. Source

The first layer represents the origin of data. It includes social media platforms, Internet of Things (IoT) devices, and log files generated by web and mobile applications. It also includes mobile devices that create semi-structured or unstructured data as continuous streams at high velocity.  

2. Stream Storage

The stream storage layer provides scalable and cost-effective components to store streaming data, such as database systems, key-value sources, or object storage services. In the storage layer, streaming data can be stored in the order it was received for a set duration of time.  

3. Stream Ingestion

The ingestion layer consolidates data from various sources in real-time. This streaming data is ingested through efficient data transfer protocols and connectors.  

4. Stream Processing

Stream processing layers transform the incoming data into a usable state through data validation, cleaning, normalization, data quality checks, and transformations. In the processing layer, the streaming records are read as they are produced, allowing for real-time analytics. 

5. Destination

The destination is a purpose-built layer, depending on a specific use case. It can be an event-based application, a web lake, a database, or a data warehouse.  

Another difference between traditional and real-time streaming ETL architectures lies in the data flow. In the latter, processed data can be delivered to destinations and potentially fed back to the source in real-time. In other words, real-time ETL provides the opportunity to rethink the flow of various applications. 

Batch ETL vs. Streaming ETL  

In batch processing, ETL software extracts data in batches from a source on a scheduled workflow, transforms that data, and loads it into a repository or a data warehouse. On the other hand, streaming ETL is a constant flow and processing of data from the source to its destination. It allows the automatic extraction and transformation of data. Then, it loads it to any destination during event creation.  

Streaming ETL offers less latency as it processes data in real time and continuously uploads and updates results. On the other hand, the latency in batch ETL is higher because the data is processed in intervals. Typically, the latency ranges from a few minutes to hours for batch processing.  

Another difference between streaming and batch ETL is the volume of data handled. Normally, an ETL pipeline is well-suited for processing large volumes of data collected over time while streaming ETL is a great option for handling high-velocity data that require immediate processing.  

Streaming ETL involves a single, long-running job continuously updating the processed data. It handles failures better than batch ETL because results, partial data transformations that are continuously fed into the overall process, are generated incrementally. The system does not discard the already generated results if a failure occurs. Still, it reprocesses the data from where it left off. In contrast, batch processing writes results in chunks. If a failure happens, it can result in incomplete data, requiring the entire batch to be reprocessed, which is time-consuming and resource-intensive.  

The Benefits of Streaming ETL 

Streaming ETL helps businesses make decisions faster as the data is processed as soon as it arrives. Here are some additional benefits of streaming ETL for organizations that rely on real-time data. 

Real-time Analytics

The streaming ETL system’s continuous data processing ensures that insights are always current. It is useful when fast actions and decisions are required based on the latest data, such as making real-time adjustments in supply chain logistics.

Consistent Data Integrity

Streaming ETL maintains high data quality by continuously monitoring and correcting data inconsistencies as they occur. By identifying and fixing errors as they occur, streaming ETL minimizes inaccuracies in the data. This continuous improvement ensures that organizations have clean, reliable information to make informed decisions.   

Adaptability to Data Volume

Streaming ETL platforms combine techniques to tackle rising data volumes. They can scale horizontally and add more processing power to distribute the workload. Some platforms utilize in-memory processing to handle real-time data surges without overwhelming storage systems.

Integration Across Platforms

Streaming ETL can handle various data formats and sources, from traditional databases and cloud platforms to IoT devices. This smooth integration across different data platforms streamlines the data processing pipeline and creates a unified approach to data management.

In-depth Insights

It integrates incoming data with external sources, cleanses it, or augments it with additional relevant information as the data streams in. For example, incoming data streams can be merged with historical data, offering a comprehensive view for predictive analysis, anomaly detection, or trend identification. 

Streaming ETL Use Cases  

Streaming ETL is beneficial across various fields and enhances the overall decision making and operational efficiency for businesses.  

Fraud Detection  

Streaming ETL enables financial institutions to analyze real-time transaction data instantly. It enables them to detect fraud by analyzing a customer’s deviation from usual spending patterns and responding to fraudulent activities as they happen. The rapid analysis boosts transaction security and lowers the risk of financial losses.

Healthcare Monitoring  

With the help of streaming ETL, health organizations can pull patient data in real time from different sources, such as wearable devices, hospital equipment, and electronic health records. This allows for the immediate analysis of vital signs and other important health metrics.

Monitoring data in real time enables healthcare providers to set up early warning systems that spot sudden changes or unusual patterns in a patient’s health to prompt timely intervention and improve patient outcomes. Streaming ETL also supports predictive models that use historical and current data to predict potential health risks or worsening conditions, helping in proactive healthcare management.

Building Streaming ETL Pipelines  

Real-time analysis heavily relies on a strong streaming ETL pipeline that supports the continuous delivery and transformation of data streams to the engine. Setting up a streaming architecture pipeline to handle different data formats is challenging.

There are a few key steps and strategies involved in structuring it to get the most out of a streaming ETL pipeline:

• Defining Data Sources

The first step is identifying the real-time data sources that will feed the pipeline. This step includes customer clickstream data, sensor readings from IoT devices, social media feeds, or real-time transaction logs. Understanding the format (e.g., JSON, CSV) and structure of this data is essential for designing the pipeline effectively.

• Choosing Streaming Platform

Select a platform capable of ingesting, processing, and transporting real-time data streams. Consider factors like scalability, fault tolerance, and integration capabilities when choosing.

• Designing the Data Transformation Logic

Streaming data often requires real-time transformations to prepare it for analysis. Filtering out irrelevant data, parsing complex data structures, applying aggregations, or performing calculations are typical of a streaming data pipeline.

• Data Cleaning Processes

Integrate data cleaning and validation checks to identify and correct any anomalies. This step involves defining data quality rules, handling missing values, or performing data normalization.

• Selecting the Destination

The transformed data stream’s destination is the data sink. It can be a data warehouse, a real-time analytics platform, or even another streaming application. The chosen sink should be compatible with the format and structure of the data pipeline. 

• Monitoring the Pipeline

Streaming ETL pipelines require ongoing monitoring and maintenance. Implement performance monitoring tools to track data throughput, identify bottlenecks, and ensure smooth pipeline operation.

Challenges of Streaming ETL  

Streaming ETL can process high-velocity data immediately, but managing streaming pipelines is challenging because of their inherent complexity and higher resource demand. Continuous data streams can overwhelm the processing infrastructure, causing bottlenecks and delays. Also, with high-speed data, errors, and inconsistencies must be identified and addressed in real time, which is more challenging than handling errors in a batch process.

However, not all use cases require or are suitable for this approach. Many data scenarios require extensive transformations and complex data integration or involve data generated only sometimes. For these situations, near real-time ETL provides a compelling alternative. Organizations seeking to balance the advantages of real-time insights with manageability will be better off with a near real-time approach to ETL.

Streaming ETL Tools  

Streaming ETL tools and platforms ingest, process, and transform continuous data streams. Beyond core functionality, streaming ETL tools offer additional benefits and built-in capabilities for data cleansing and validation. These tools can also integrate with a variety of data sources and destinations. Many streaming ETL tools offer monitoring and management features to track pipeline performance, identify issues, and ensure the smooth flow of real-time data.

Final Thoughts  

Many businesses rely on real-time data to make instant data-backed decisions. Streaming ETL works flawlessly in managing and processing real-time data.  

Although streaming ETL offers significant benefits in terms of real-time data processing and immediate insights, there are several use cases where a traditional or near-real-time ETL approach can work better. Understanding each use case’s specific requirements and objectives is essential in determining the most appropriate approach to data integration and processing. 

Suppose your organization requires quick access to data but does not need it in real-time. In that case, an ETL tool with near-real-time data processing capabilities can be a viable solution.  

Astera offers an end-to-end ETL platform powered by AI and automation. It’s a 100% no-code solution with built-in transformations and native connectors that allow you to easily connect to and move data, whether on-premises or in the cloud. It also offers data quality management, empowering you to cleanse and validate data seamlessly. With its unified and intuitive UI, Astera ensures the platform is approachable even for non-technical users.  

Ready to ingest and move data in near real-time? Download a 14-day free trial or contact us to discuss your use case today.