Run ETL using Background Jobs Solution: A Hybrid Model
If you are a developer dealing with the complex process of data management, particularly with ETL (Extract, Transform, Load), this article is for you. We will guide you through the cases where running ETL jobs using background job solutions in a hybrid (or on-premise) model can be useful. Additionally, you will learn how IronWorker can help you handle data security, privacy, and performance issues effectively. Let's dive in and make your job simpler!
Table of Contents
- What is ETL?
- What is a Hybrid Model?
- Why run ETL in your own web infrastructure?
- Challenges with running ETL in your own web infrastructure environment
- The Role of IronWorker in Data Processing
- Running ETL jobs in your own AWS environment using IronWorker
- Case Study: Leveraging IronWorker for HIPAA-Compliant Medical Imaging
- Conclusion
What is ETL?
Extract, transform, and load (ETL) is a common data processing technique that involves moving data from one or more sources to a destination system where it can be analyzed, manipulated, or stored. ETL is often used for data warehousing, business intelligence, data migration, and data integration.
What is a Hybrid Model?
A hybrid model combines the advantages of on-premises infrastructure with a public cloud to achieve the best results for specific use cases. This approach retains control over sensitive data while harnessing the resources of public cloud providers.
With Hybrid Iron.io, the API and all the complexity of our job processing system lives in the cloud, while the actual execution of the workloads is on-premise, behind your firewall, on your hardware (or in your own VPC). The only thing you need to run on your systems is our runner container; no databases to install and maintain, no API servers, or anything else. The runner container talks to the Iron.io API, asking for jobs, executing them, and dealing with all the things that can happen while running.
Why run ETL in your own web infrastructure?
ETL can pose some challenges, especially when it comes to security and privacy. If you are using a third-party hosted solution for ETL, you may have to deal with:
- Data breaches and cyberattacks that can compromise your sensitive data or expose it to unauthorized parties.
- Strict security requirements that don’t allow accessing databases from the public cloud or any third-party service
- Compliance issues with regulations such as SOC 2, HIPAA, and GDPR that require you to protect the confidentiality, integrity, and availability of your data.
However, running ETL in your own web infrastructure environment also comes with some drawbacks.
Challenges with running ETL in your own web infrastructure environment
You’ll encounter the following challenges:
- Hardware maintenance and upgrade is completely your responsibility. You need skilled IT personnel to manage and maintain the infrastructure effectively. Finding and retaining qualified staff can be a challenge, especially as technologies and demands evolve.
- You need to invest in servers, networking equipment, cooling systems, and other hardware, which can be expensive.
- Scaling your infrastructure to accommodate fluctuating workloads or sudden spikes in demand can be challenging. Organizations need to predict their future requirements accurately to avoid either underutilization or over-provisioning of resources.
That’s where IronWorker comes in.
The Role of IronWorker in Data Processing
IronWorker is a SaaS platform that allows you to run any code or software in any language on any cloud. With IronWorker, you can:
- Run ETL or any other jobs in any cloud (AWS is preferable).
- Use IronWorker webhooks to trigger a new task to run, “scheduled task” feature, automatic retries on failures, etc. to automate your workflows and handle failures gracefully.
- Use IronWorker’s dashboard where you can find logs, metrics, alerting integrations, etc. to monitor and manage your jobs and performance.
Running ETL jobs in your own AWS environment using IronWorker
Assume you’re using AWS RDS (or any other service that stores private data) in a private VPC, i.e. your database is not accessible from the public cloud. ETL jobs need access to the database. This is a good fit for configuring IronWorker to run the ETL jobs in your own AWS environment, in the same VPC where your database is hosted. It gives you the power and flexibility of containers without the hassle and overhead of managing them. It also gives you the convenience and simplicity of a hosted solution without the risk and cost of losing control over your data.
Case Study: Leveraging IronWorker for HIPAA-Compliant Medical Imaging
To illustrate how IronWorker can help you run ETL in your own AWS environment with a hybrid model, let’s look at a case study where Iron is working with a world-leading healthcare provider to handle medical imaging under HIPAA compliance constraints.
The healthcare provider has millions of medical images stored in S3 buckets across different regions. They need to perform various ETL tasks on these images such as:
- Extracting metadata from the images such as patient ID, diagnosis code, date of service, etc.
- Transforming the images into different formats such as JPEG or PNG for faster processing and display.
The healthcare provider also needs to ensure that their ETL processes comply with HIPAA regulations that require them to protect the privacy and security of their patients’ protected health information (PHI).
The healthcare provider uses IronWorker to run ETL in their own AWS environment. They employ IronWorker’s encryption features to encrypt their data in transit and at rest using their own keys.
The healthcare provider also uses IronWorker’s user-friendly web interface to see their ETL jobs with statuses (complete, failed, queued, etc.) and statistics.
By using IronWorker to run ETL in their own AWS environment, the healthcare provider is able to:
- Achieve HIPAA compliance by keeping their PHI data in their own AWS environment and encrypting it using their own keys.
- Reduce data breaches and cyberattacks by minimizing the exposure of their PHI data to third-party hosted solutions.
- Improve performance and scalability by using IronWorker’s Autoscale feature and leveraging low network latency.
- Enhance user experience and satisfaction by leveraging a user-friendly and intuitive web interface for their ETL processes and results.
Conclusion
IronWorker is the ultimate platform for running ETL or any other job in any cloud. It allows you to enjoy the benefits of both data sovereignty and control, and user interface and experience without compromising on security, privacy, or cost. If you want to learn more about IronWorker, sign up here and/or schedule a demo.
This article is an overview and does not include specific implementation codes. For more detailed, step-by-step instructions on how to implement ETL processes using IronWorker, you can refer to the IronWorker Documentation and iron-worker-examples repository in Github.
