What is Streaming ETL? : An Easy Guide

Are you looking to perform streaming ETL? If that is the case you are in just the right place. In this blog, we will tell you everything you need to know about streaming ETL.

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.

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.

How ETL Works?

• Extract: As the name suggests, Extract means the collection of data from different sources which could be databases, data warehouses, data streams & event streams as well. Formats can also be different including JSON, CSV or TXT etc.
• Transform: Different Operations are performed on data in this stage for the purpose of cleaning it, and preparing it for performing analysis & reportings. 
• Load: In this final step, the data is loaded or stored into a data warehouse or any sort of database which could be relational like MySQL or non-relational like MongoBD.

Batch ETL vs Streaming ETL

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:

Feature	Batch ETL	Stream ETL
Data Processing	Processes data in chunks at scheduled intervals.	Processes data continuously as it is generated.
Latency	High latency; data is available after processing is complete.	Low latency; data is available almost instantly.
Use Cases	Suitable for periodic reports.	Ideal for real-time analytics, fraud detection, and IoT.
Scalability	Can handle large volumes but may struggle with high-velocity data.	Handles high-velocity data streams effectively.
Complexity	Generally simpler to implement.	Requires more sophisticated infrastructure and tools.
Resource Efficiency	It can be more resource-intensive, especially during processing windows.	Optimizes resource usage by processing data as it arrives.

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.

Disadvantages of ETL Tools

I have listed some of the limitations/disadvantages of ETL Tools for your reference.

• High Initial Costs: ETL tools can be expensive to license and implement, especially for larger organisations or complex systems.
• Complexity and Learning Curve: Many ETL tools have a steep learning curve and require specialised skills to configure and manage effectively.
• Performance Issues: ETL processes can be resource-intensive and might lead to performance bottlenecks if not optimised properly.
• Maintenance Overhead: Ongoing maintenance and updates can be time-consuming and may require continuous monitoring and troubleshooting.

Few Examples of Streaming ETL

Credit Card Fraud Detection: When you swipe your credit card, the transaction data is sent to, or extracted by, the fraud detection application. The application then joins the transaction data in a transform step with additional data about you and then applies fraud detection algorithms. All of your history of transactions, your schedule of spending, amount of spending & many other data points need to be used in order to classify a genuine activity from a fraudulent one.

Internet of Things: Devices produces thousands of data points in real-time to be used for further processes to run. How it could be possible to gather all these data points in real-time, clean these, pre-process them & then transfer those to another stage to drive some value.

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.

AWS Glue

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load your data for analytics. AWS Glue has implemented Streaming ETL based on Apache Spark to run continuously & consume data from continuous stream platforms like Amazon Kinesis Data Streams & Apache Kafka. AWS has provided its implementation detail along with a working example on the following official documentation. 

Image source:- https://d2908q01vomqb2.cloudfront.net/da4b9237bacccdf19c0760cab7aec4a8359010b0/2020/04/16/serverless-etl-diagram.png

Microsoft Azure Databricks

Microsoft Azure also provides the functionality of setting up streaming ETL using Azure Databricks which is a fully managed service providing powerful ETL & analytics along with other many functionalities. There are useful resources available to see the more details about implementing the streaming ETLs. Please have a look at the official documentation & Azure Medium blog. 

GCP BigQuery

GCP also provides the functionality of setting up a streaming ETL by using the concept of Pub/Sub, DataFlow, BigQuery & Apache Beam. Further details can be viewed at the following official documentation link. 

Image source:- https://miro.medium.com/max/1000/1*9zCX81ho6hRa4NE5qk1MVg.png

Streaming ETL Use Cases

Streaming ETL is the backbone of real-time data processing and is used across various industries. Here are some key use cases:

1. Real-Time Analytics: Suppose you’re running an online retail store. With Streaming ETL, you can monitor user activity in real time. This allows you to analyze customer behavior, optimize pricing strategies instantly, or personalize user experiences as they browse.
2. Fraud Detection: In banking and finance, catching fraudulent activities quickly is essential. Streaming ETL enables continuous monitoring of transactions, flagging suspicious behavior in real-time, which can prevent significant losses.
3. Internet of Things (IoT): IoT devices continuously generate massive amounts of data. For example, sensors in a smart factory can stream data on machinery performance. Streaming ETL processes this data in real time, allowing for predictive maintenance, where you can address potential equipment failures before they occur.
4. Supply Chain Management: Keeping a supply chain running smoothly requires real-time visibility into inventory levels, shipments, and production status. Streaming ETL helps businesses react instantly to any disruptions, ensuring that products are delivered on time and at optimal costs.
5. Customer Experience: Businesses like telecom companies or online service providers can use Streaming ETL to monitor real-time service quality. If a customer experiences a drop in service quality, immediate action can be taken to rectify the issue, thereby enhancing customer satisfaction.

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.

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Have any further queries? Get in touch with us in the comments section below.

FAQs about ETL Tools