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.
Here’s what you will be looking at:
What is ETL?
ETL is a short form of Extract Transform and Load. ETL actually combines these three database functions into one tool to fetch data from one database and place it into another database. ETL is a very old concept which has been evolving since the 1970s and 80s, where in early days the process was sequential, data was not that fast & reportings & analytics were needed once in a while.
Hevo offers a faster way to move data from databases or SaaS applications into your data warehouse to be visualized in a BI tool. Hevo is fully automated and hence does not require you to code.
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- Completely Automated: The Hevo platform can be set up in just a few minutes and requires minimal maintenance.
- Real-time Data Transfer: Hevo provides real-time data migration, so you can have analysis-ready data always.
- 100% Complete & Accurate Data Transfer: Hevo’s robust infrastructure ensures reliable data transfer with zero data loss.
- Scalable Infrastructure: Hevo has in-built integrations for 150+ Data Sources that can help you scale your data infrastructure as required.
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- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.
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.
Image source:- https://storage.ning.com/topology/rest/1.0/file/get/2808307873?profile=original
What is Streaming ETL?
Streaming ETL is the processing and movement of real-time data from one place to another. This entire process occurs against streaming data in real-time in a stream processing platform. This type of ETL is very important given the velocity with which new technologies are generating data. Technologies like the Internet of Things, Online Retail, Banking Transactions are producing enormous amounts of data with unprecedented speed. Therefore, traditional ETLs needed to be more effective to handle these data streams in real-time.
Real-time Streaming ETL Architecture
Real-time Streaming architecture and traditional ETL architecture are fundamentally the same things. The ETL process consists of mainly a data source, ETL engine, and a destination. In the Real-time 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 are the center of 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, and these platforms act 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.
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.
Image Source:- https://www.alphalogicinc.com/wp-content/uploads/2020/05/ETL-Stream-Processing02.png
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 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 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 Tools
A number of companies have built product suites around ETL over the decades. Most of these tools and suites were built for the batch world. While technology providers are trying to catch their products up to the world of streaming data, most of the products simply lack the capabilities necessary for streaming ETL.
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.
Hevo has pre-built integrations with 150+ Data sources. You can connect your SaaS platforms, databases, etc. to any data warehouse of your choice, without writing any code or worrying about maintenance. If you are interested, you can try Hevo! Sign up here for a 14-Day Free Trial!
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