What is Streaming ETL? : An Easy Guide

Imagine running an e-commerce business today, where up-to-date information makes the difference between making the right decisions quickly or not – if you had an online store and wanted to know the inventory level in real-time. Still, your data is only updated once a day; you might oversell your products, and customers would get unhappy due to the actual unavailability of products. 

That’s where Streaming ETL comes in. It synchronizes data the moment it is created, ensuring that all of your systems are always up to date. It helps organizations be responsive: it can be on the transactional level to make operations smoother or on the customer contact level to make better decisions.

In this blog, we will talk about what streaming ETL is, its components and features, and how it differs from traditional ETL. Stay tuned to learn more!

What is Streaming ETL?

Streaming ETL is a process where data is extracted from sources, transformed, and loaded into a target system in real time. Unlike traditional ETL, which processes data in batches, Streaming ETL operates continuously, capturing data as it is generated and making it available for immediate analysis or action.

Imagine you’re running a retail business. You want to know how many sales have been made today, not tomorrow or even an hour later. Streaming ETL lets you process that data as it’s created, giving you real-time insights.

Key Components of Streaming ETL

1. Ingestion: This is where data streams from multiple sources (e.g., IoT devices, transaction logs) are continuously collected.
2. Transformation: Data is processed and enriched in real-time, applying complex transformations like aggregation, filtering, or joining with historical data.
3. Loading: Transformed data is loaded into target destinations, often cloud data warehouses or real-time dashboards.

In Streaming ETL, latency and scalability are critical. Organizations need systems capable of processing high-velocity data streams while maintaining low latency to ensure real-time decision-making. Technologies such as Hevo, Apache Kafka and Apache Flink are pivotal in building reliable streaming data pipelines.

Importance of Real-Time Data Processing

In today’s fast-paced world, having real-time insights is crucial. Whether you’re monitoring customer behavior, tracking financial transactions, or managing supply chains, real-time data allows you to make timely, informed decisions. With Streaming ETL, you can detect patterns, anomalies, or trends as they happen, enabling you to react immediately. For example, if you detect fraudulent activity on a credit card in real time, you can stop it before it causes damage.

The importance of real-time data processing boils down to one thing: staying competitive. When you can respond to events as they unfold, you’re always a step ahead, whether it’s catching a problem before it escalates or seizing an opportunity the moment it arises.

What is Traditional ETL?

Traditional ETL or simply ETL, is a process of transferring 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. 

This form of ETL is not generally preferred because it takes longer to analyze data because it is processed in batches.

Batch ETL vs Streaming ETL vs ELT

In traditional data environments, ETL software extracted batches of data from a source system usually based on a schedule, transformed that data, then loaded it to a repository such as a data warehouse or database. This is the “batch ETL” model shown in the following diagram as well. 

To give you a clearer picture, let’s compare Batch ETL and Stream ETL and ELT:

Feature	Batch ETL	Stream ETL	ELT
Data Processing	Processes data in chunks at scheduled intervals.	Processes data continuously as it is generated.	Data is first extracted and loaded into the destination and then transformed as needed.
Latency	High latency; data is available after processing is complete.	Low latency; data is available almost instantly.	Medium: data availability depends on various factors.
Use Cases	Suitable for periodic reports.	Ideal for real-time analytics, fraud detection, and IoT.	Works well for large datasets, particularly in cloud-based environments.
Scalability	Can handle large volumes but may struggle with high-velocity data.	Handles high-velocity data streams effectively.	Can handle large datasets easily.
Complexity	Generally simpler to implement.	Requires more sophisticated infrastructure and tools.	Easy to Implement with automated tools like Hevo.
Resource Efficiency	It can be more resource-intensive, especially during processing windows.	Optimizes resource usage by processing data as it arrives.	Not ideal for real-time use cases, as transformations happen after the data is loaded.

Real-time Streaming ETL Architecture

Real-time streaming architecture and traditional ETL architecture are fundamentally the same. The ETL process consists mainly of a data source, an ETL engine, and a destination. In the Real-time Data Streaming architecture, the data comes from the data sources, and then it acts as an input for ETL tools to process and transform data. The transformed data is then forwarded to the Data Warehouses that center your data universe. All the pieces of data are fed to applications and requests from the Data Warehouse. 

The data sources feed data to a stream processing platform, which acts as a backbone to streaming ETL applications. The ETL application can extract a stream of data from the source, or the data source can push or publish the data to an ETL tool for transformation. Then, after processing the data, it is transferred to the destination.

Benefits of Stream Processing

• You will always have fresh data available because you are processing one event at a time in real-time. The latency of data will be good.
• It helps in saving the cost because you don’t need to run the operations on small servers. You will have a small amount of processing for every piece of data or stream in real-time.

Setting Up Streaming ETL

In setting up streaming ETL you need:- 

• A Data Source feeding data to the system. 
• ETL Streaming Engine to process all the ETL functionalities. 
• Sink in the end to use the data. 

Stream Process platform serves as the backbone to streaming ETL applications, and also for many other types of streaming applications and processes. The streaming ETL application may extract data from the source, or the source may publish data directly to the ETL application. When a streaming ETL process completes, it may pass data to the right to a destination (potentially a data warehouse). Or it may send a result back to the original source on the left. In addition, it can concurrently deliver data to other applications and repositories.

Best Tools for Streaming ETL in 2024

Now that you know why it is crucial to have streaming ETL pipelines for your data migration, let’s have a look at a few tools that can be used to automate the process of creating streaming etl pipelines.

• Google Cloud Dataflow
• Hevo Data
• Kafka
• AWS Glue
• Amazon Kinesis
• Fivetran
• IBM Infosphere

Industry-Specific Use Cases of Streaming ETL

1. Finance

Real-time data is crucial to operations in the financial sector, fraud detection, algorithmic trading, and more. A Streaming ETL system enables real-time monitoring of transactions, which detects suspicious activities immediately instead of waiting for batch processes, possibly missing critical fraud signals.

2. Healthcare

Healthcare is a core industry for real-time data processing, where patient monitoring and even telemedicine can be made possible. It’s the ingestion and processing of streams of continuous patient data from wearable devices with ETL pipelines – which indeed alerts healthcare providers to any critical changes in vital signs or health conditions instantly.

3. E-Commerce

For e-commerce platforms, Streaming ETL can provide a competitive edge by enabling real-time personalized recommendations, pricing adjustments, and inventory management.

4. Internet of Things(IoT)

Streaming ETL is indispensable in the IoT space, where vast amounts of sensor data need to be processed in real time. Whether it’s smart cities, connected vehicles, or industrial automation, real-time analytics powered by Streaming ETL ensures faster decision-making and operational efficiency.

Challenges in Streaming ETL

There are various challenges that you might face while implementing streaming etl, such as:

1. High Data Volume: Real-time data processing means systems must handle large volumes of data arriving at high velocity. The pipeline would fail if the data load exceeds too much. 
2. Latency: Users expect immediate insights from real-time data, meaning streaming ETL systems must operate at a low latency.
3. Data Integrity and Accuracy: Streaming data means dealing with potential out-of-order events, duplicate data, or incomplete records. You must ensure that the final data in the destination is the same as the source. 
4. Scalability: Streaming ETL pipelines need to be fault-tolerant, ensuring that data is neither lost nor duplicated during failures.

How to Build a Streaming ETL Pipeline with Hevo?

Hevo is a reliable, cost-effective, and easy-to-use automated platform that syncs your data in real-time. With Hevo, you can connect up to 150+ sources with your desired destination and stream your data without any hassle of coding. 

To create a streaming etl pipeline with Hevo, follow two simple steps:

Step 1: Connect your source.

Step 2: Connect your destination.

And that’s it! That’s how simple it is. 

Conclusion

In summary, streaming ETL represents a significant advancement over traditional ETL by facilitating real-time data processing and integration. Unlike batch ETL, which handles data in discrete intervals, streaming ETL continuously processes data as it arrives, ensuring timely insights and rapid responsiveness. By understanding the distinctions and benefits of streaming ETL, you can better leverage these technologies to meet your evolving data needs and drive more informed decision-making.

If you’re looking for a more straightforward solution, you can use Hevo Data – a No Code Data pipeline to build perform ETL in an instant.

FAQs about ETL Tools