What Is Streaming ETL: Architecture, Examples & Tools

Scattered data across applications creates major trouble for data analysis. Integrating this data from different sources into a central analytics platform is crucial for extracting actionable insights.

However, manually integrating data can be a complex process that consumes valuable time and resources. A streaming ETL process can help you automate your journey.

This article discusses streaming ETL, a modern approach to integrating data in real-time. It highlights streaming ETL's core functionalities, explains the architecture, and provides practical use cases. You will also learn how it is different from traditional ETL processes.

What Is Streaming ETL?

Streaming ETL is a method of processing real-time data and transferring it between different sources for further analysis. It reduces the complexity of batch processing by continuously transforming and loading data, minimizing delays and ensuring real-time updates. This capability makes streaming data essential for any business domain requiring continuous data processing, including banking, social media, and more.

4 Important Streaming ETL Concepts

This section highlights the four most important ETL concepts that you must know before getting started with developing your custom streaming ETL pipelines:

Synchronous Reporting

Streaming ETL methods allow you to synchronize data reporting with newly generated data to enhance business decision-making. This robust feature makes it easy to enhance customer satisfaction and reduce fraudulent transactions.

Data Freshness

Data freshness is one of the most important aspects of streaming ETL since the data is processed and transferred to the destination in an instant. An increment in data transfer speed makes your dataset available for analysis at any time, which helps generate impactful insights. These insights can reform business planning and affect the critical decisions that you must make to enhance profitability.

Cost Effectiveness

In the streaming ETL method, data is processed in real-time as it arrives. It reduces the number of computational resources required to move and process large datasets. As the pressure on computational resources decreases, the costs associated with data processing go down significantly. Streaming ETL helps you save money while performing data integration tasks.

Real-Time Data Monitoring & Analysis

Streaming ETL pipelines enables you to analyze the latest recorded data more easily if the pipeline's destination is an analytical platform, such as Amazon Redshift, Google BigQuery, or any other. This allows you to uncover hidden data patterns and address potential issues quickly.

What Is Traditional ETL?

The traditional ETL method facilitates processing data from a source to a target platform in batches. It allows you to extract data from the source according to a schedule, transform it to be compatible with the destination, and load it into the destination.

Loading data this way takes longer because the data pipeline processes data in batches, increasing the time it takes to analyze the data. However, this method can be disadvantageous if you want real-time results. Here are the main stages involved in the ETL process:

• Extract: This is the first step of any ETL process in which you extract raw data from disparate sources. You can obtain data from many widely used sources, including databases, spreadsheets, IoT devices, or any other form.
• ‍Transform: The second stage involves transforming data to make it compatible with the destination platform. The transformation step involves cleaning, formatting, aggregating, standardizing, and other techniques as required.
• ‍Load: This final stage involves loading data into a destination for further processing and analysis. The destination can be a database, data warehouse, or any other storage platform.

Difference between Batch ETL & Stream ETL

The selection of an ETL pipeline depends on the specific use case associated with the task at hand. There are multiple factors that you should consider before choosing between batch ETL vs. streaming ETL, including:

• Core Purpose: In Batch ETL, you can process and load large volumes of historical data at specific, predetermined intervals. On the other hand, in the stream ETL approach, you can process and transfer data as it is generated.
• ‍Latency: Batch ETL delays data transfer as it processes data in large batches. Streaming ETL processes the data in real-time and offers low latency, making the results available immediately.
• ‍Applications: Streaming ETL is useful for fraud detection and prevention, real time stock price analysis, monitoring website traffic and user behavior, and more. On the flip side, batch ETL can be useful for generating monthly sales reports, analyzing customer behavior patterns over time, or data warehousing and historical data analysis.

Real-Time Streaming ETL Architecture

Streaming ETL architecture consists of three elements—a data source, a streaming ETL engine, and a destination. 

The source is the origin of the data stream. Some prominent data sources include sensors, IoT devices, and social media, among others. 

The streaming ETL engine continuously reads the data from the source in real-time. This extracted data might need some formatting, cleaning, or filtering before analysis. The ETL engine facilitates data transformation as the data arrives. 

Streaming ETL engine involves:

• Stream Ingestion: AWS IoT, Kinesis Agent, and AWS SDK are some of the services that can help you ingest data streams.‍
• Stream Storage: You can use different services, including Amazon MSK, Kinesis Data Streams, and Apache Kafka on Amazon EC2 for stream storage.‍
• Stream Processing: A service like AWS Lambda can be used for event-based and stateless data processing.

After processing the stream data, it is loaded into the destination for further processing and analysis. The destination can be a data storage location or an event-driven application.

Alternatively, streaming ETL tools can help you achieve this in a much simpler way by enabling you to configure custom connectors.

Streaming ETL Use Cases

Most real-world applications utilize streaming ETL processes to streamline their day-to-day activities. Here are some of the widely discussed use cases of streaming ETL:

Fraud Detection

Streaming ETL plays a crucial role in real-time fraud detection, helping to reduce the risk of financial losses. By continuously analyzing transactional data as it occurs, the system can track and identify fraudulent activities, such as unauthorized credit card usage. 

Financial institutions can integrate anomaly detection algorithms into their applications. These algorithms will help to analyze anomalies during daily transactions. This allows them to take immediate actions, such as notifying users or blocking cards.

Internet of Things (IoT)

IoT devices use data points to measure specific values and restrict tasks when certain conditions arise. For example, precision farming in agriculture monitors soil moisture levels to optimize irrigation for better productivity.

Real-time streaming ETL can help in precision farming by continuously monitoring the level of moisture that the IoT device observes. When there is a spike in the moisture level, the device can trigger actions to restrict the irrigation process.

High-Frequency Trading

Streaming ETL pipelines play a significant role in high-frequency trading (HFT) to process massive amounts of real-time market data. These pipelines enable robust algorithms to identify profit opportunities. 

Customer Interactions

Customer interactions are one of the main criteria for judging how well an organization retains customers. Streaming ETL allows the transfer of customer data from an e-commerce platform to the analytical database in real-time. This continuous stream of data facilitates analyzing user behavior, suggesting similar products from the database, and offering coupons.

How to Build a Streaming ETL Pipeline with Airbyte?

Airbyte is a reliable data integration and replication platform that simplifies data movement from various sources to your desired destinations. It provides a rich library of 350+ pre-built connectors to streamline data pipeline development. 

What truly sets Airbyte apart is its open-source Python library, PyAirbyte. This library packages all Airbyte connectors and makes them readily available for building custom and flexible ETL pipelines within your Python environment. PyAirbyte also supports incremental data reading that allows you to capture only the changes made in the source data.

To build a custom ETL pipeline with PyAirbyte, all you need to do is: 

1. Install PyAirbyte: Install it using PyPi.‍
2. Extract Data: Utilize Airbyte connectors to extract data from multiple sources.‍
3. Perform Transformations: You can use Python libraries, such as Pandas or Spark, to perform complex transformations within your pipelines as needed.‍
4. Load Data: Use Python libraries to store data.

Alternative Approach: Build Streaming ETL Pipeline Using Airbyte and Pathway

You can integrate Airbyte with the Python framework to access the streaming ETL functionality. This process involves using Airbyte features with Pathway, a Python data streaming framework.

With Pathway, you can use the AirbyteServerless tool to seamlessly extract data streams from sources supported by the Airbyte platform. Pathway also allows you to transform the streams and load them into Postgres, Kafka topics, or other destinations.

Building streaming ETL pipelines using Pathway involves four steps:

• Installing and setting airbyte-serverless.
• Configuring Airbyte sources to stream data.
• Using Airbyte connectors from Pathway to extract data.
• Transforming the data into a format compatible with the destination of your preference. Some of the most common transformations include removing missing values, anomalies, and data standardization.
• Importing the transformed data into a destination platform using the Pathway output connectors.

Pathway offers stream processing abilities that enable replication of changes from the source to the destination dataset. This method reduces the time required to build data pipelines using different cloud services. Another advantage Pathway offers is that it reduces the cost associated with cloud services like AWS or Azure since it is an open-source tool.

Conclusion

The streaming ETL method enables data processing and movement in real-time as the data is generated. It reduces the latency observed in batch ETL and provides an efficient way to move data while saving additional costs. The popularity of the streaming ETL process has been growing since it was first introduced, and it can be observed in a wide range of applications used today.

Frequently Asked Questions (FAQs)

Q. What Is Streaming ETL?

Streaming ETL is a process that continuously extracts data from various sources, transforms it in real-time, and loads the transformed data into a destination.

Q. Why Do You Need ETL?

ETL is necessary for consolidating data from various sources into a central location for further analysis.

Q. How Is an ETL Pipeline Different from ELT?

ETL (Extract Transform Load) pipeline allows you to transform data before loading it into a destination. In contrast, the ELT (Extract Load Transform) pipeline facilitates moving raw data to the target destination, where you can transform it according to specific analytical requirements.

Q. Is Kafka Batch or Stream Processing?

Although Kafka is generally useful for stream processing, it supports both batch and stream processing.