Are you on a quest to optimise your ETL workflows? Or perhaps, you’re looking to level up your data management strategies? The solution may lie in Matillion’s powerful feature – Shared Jobs. Unlike normal jobs, which are confined to a single project and require manual updates, shared jobs are global in scope, reusable across multiple projects, and automatically reflect any changes made. Additionally, shared jobs are efficient at handling parallel processing, making them ideal for high-performance environments, whereas normal jobs can face execution issues and performance bottlenecks when running multiple instances in parallel.
The key benefits of using Matillion’s Shared jobs are:
- Reduce development and support effort by re-using centrally defined ETL jobs
- Achieve uniform solutions across the ETL estate
- Enable running processes in high availability mode without driving the development costs up
- Facilitate collaboration between development teams
- Simplify testing and debugging of ETL processes
This all contributes to a better quality of ETL jobs and a lower total cost of ownership for your ETL processes. In this blog post Padmashree Pratap explains the key benefits of using shared jobs. In the next blog post, she has shared some real-life examples of how shared jobs are implemented for specific use cases.
The Magic of Matillion’s Shared Jobs
In Matillion, shared jobs are the superheroes of ELT processes. Shared jobs are reusable and can be seamlessly incorporated into multiple projects. They can encapsulate common processes that can be managed and updated across different workflows, ensuring consistency, and reducing maintenance effort.
Shared jobs are essentially ELT jobs that have been abstracted into reusable templates. They contain a set of transformations or processes that can be called by other jobs, making them an excellent tool for standardizing, and simplifying complex ETL workflows.
The Matillion Advantage: Key Benefits of Using Shared Jobs
Reusability and Maintainability
A retail giant wants to streamline its sales data transformation process across different regions. By creating a shared job for data transformation, the company can use this job in multiple projects. Any updates to the shared job automatically apply everywhere it’s used, reducing the effort needed for maintenance. This approach ensures consistent and high-quality data processing with minimal maintenance.
Uniformity Across Projects
An e-commerce company integrates data from various sources and ensures that all customer data follows the same validation and transformation rules. Shared jobs enforce these rules uniformly, maintaining data integrity and consistency, which are essential for accurate reporting and analysis. In a multinational corporation, different departments might process customer data differently. Using shared jobs, the company can apply a standard set of validation and transformation rules across all departments, ensuring the final dataset is consistent and reliable.
Parallel Processing and High Availability
Shared jobs can handle large volumes of data by running multiple instances of a transformation job simultaneously. They can operate independently in their own threads, allowing for parallel execution without conflicts. This capability is crucial for high-availability clusters where multiple job instances might need to run concurrently. Shared jobs can process data across multiple nodes in a cluster, managing thousands of transactions in parallel. This scalability improves performance and ensures the system remains responsive even under heavy loads.
Enhanced Collaboration
Shared jobs foster better collaboration among team members. Because they are standardized and reusable, different team members can work on various parts of the ETL process without worrying about inconsistencies. This leads to more efficient teamwork and faster project completion times. A data engineering team can develop a shared job for data extraction, which the analytics team can then use for transformation and loading. This clear division of labour and standardization ensures that each team member knows exactly what to expect from the shared components, reducing the risk of errors and misunderstandings.
Streamlined Testing and Debugging
Shared jobs simplify testing and debugging ETL processes. Once a shared job is tested and verified, it can be used confidently across multiple workflows, reducing the overall testing burden, and maintaining high-quality standards. A shared job that handles data validation, for instance, can be thoroughly tested to ensure it catches all errors and inconsistencies. Once validated, this job can be reused across multiple ETL workflows, providing confidence that data validation will be consistent and reliable in all instances.
Cost Efficiency
Using shared jobs can lead to significant cost savings. By reducing the need for redundant job creation and maintenance, shared jobs help organizations allocate their resources more efficiently. The time saved on developing, testing, and maintaining individual jobs can be redirected towards more strategic initiatives. A consulting firm that builds custom ETL solutions for various clients can develop a library of reusable components that can be quickly adapted for different clients, reducing project costs and turnaround times.
Real-life examples
This article illustrates that shared jobs in Matillion, when used effectively, reduce the total costs of ownership of your data platform. If you are interested in some real-life examples please read the next blog post, Real-life examples of Matillion Shared Jobs.
