Modern Data Transformation using Jolt
In today’s data-driven world, the ability to efficiently manipulate and transform data is a fundamental requirement for software developers and data engineers. Enter Jolt Transformation, a powerful framework that offers a streamlined way to transform JSON data without the need for traditional getter and setter methods or complex custom code.
What is Jolt Transformation?
Jolt Transformation is a lightweight and versatile JSON-to-JSON transformation library and framework. It is designed to simplify the process of transforming structured data, particularly JSON data, from one format to another. Jolt provides a declarative way to specify how the transformation should occur, making it a powerful tool for data manipulation and conversion tasks.
Imagine being a Workday Service Development Engineer, where various companies store their employee and HR data with different object properties. However, when you retrieve data from the Workday Employee microservices, you need to present it uniformly. This means that you can seamlessly accept data in various formats and transform it into a single standardized JSON structure, all achieved dynamically during runtime using the open-source Jolt library.
How Jolt Works?
Jolt works by applying a set of JSON-to-JSON transformation rules specified in a declarative manner to incoming JSON data. These transformation rules define how the input JSON should be transformed into the desired output format. Here’s how Jolt works in more detail:
Feel free to utilize this in your playground environment
https://jolt-demo.appspot.com/#inception
Input.json
{
"client": {
"address": {
"street": "11 street cop work",
"state": "New York"
},
"name": "Amazon",
"contact": "001–786543",
"employeeDetails": {
"name": "John Green",
"designation": "Software engineer",
"phone": "91–7865432300",
"home": "080–78645–87362",
"address": "11th streem Smt Amest"
}
}
}Jolt Spec:
[
{
"operation": "shift",
"spec": {
"client": {
"name": "companyName",
"address": {
"street": "clientStreetAddress",
"state": "clientAddressState"
},
"employeeDetails": {
"name": "employee.name",
"designation": "employee.designation",
"phone": "contact.phone",
"home": "contact.home",
"address": "contact.address"
}
}
}
},
{
"operation": "modify-overwrite-beta",
"spec": {
"adreess": "=concat(@(1,clientStreetAddress),' ',@(1,clientAddressState))"
}
},
{
"operation": "remove",
"spec": {
"clientStreetAddress": "",
"clientAddressState": ""
}
}
]Output.json
{
"companyName": "Amazon",
"adreess": "11 street cop work New York",
"employee": {
"name": "John Green",
"designation": "Software engineer"
},
"contact": {
"phone": "91-7865432300",
"home": "080-78645-87362",
"address": "11th streem Smt Amest"
}
}Use cases:
Here are some common use cases for Jolt Transformation
1. API Data Normalization: When dealing with data from various APIs, Jolt can normalize the data into a consistent format that your application expects. This ensures uniformity in data processing.
2. Data Integration: Jolt is valuable for integrating data from different sources, especially when the sources provide data in varying JSON structures. It can help unify and structure data for analysis or storage.
3. JSON Schema Evolution: In cases where JSON schemas evolve over time, Jolt can help map data from an older schema to a newer one, making it backward-compatible with existing code.
4. Filtering Data: Jolt can filter out unwanted data from a JSON document, allowing you to focus on specific information that’s relevant to your application.
5. Data Enrichment: Jolt can enrich data by adding or modifying fields based on certain conditions, calculations, or external data sources.
6. ETL Processes: Jolt can play a role in ETL (Extract, Transform, Load) processes, where data is extracted from various sources, transformed into a consistent format, and then loaded into a data warehouse or analytics platform.
Challenges and Considerations:
1. Learning Curve: Understanding how to write effective Jolt transformation specifications may have a learning curve, especially for newcomers to the framework. It’s essential to invest time in grasping the syntax and concepts.
2. Complex Transformations: While Jolt can handle complex transformations, very intricate or convoluted transformations may become challenging to express clearly in a Jolt Spec. Maintaining readability and manageability of complex transformations can be a consideration.
3. Performance: Depending on the complexity of the transformation and the size of the input data, performance considerations may arise. It’s important to profile and optimize transformations as needed for large datasets or resource-intensive operations.
4. Data Validation: Jolt primarily focuses on transformation rather than data validation. If data validation and error handling are crucial aspects of your application, you may need to combine Jolt with additional validation mechanisms.
5. Schema Changes: When source JSON schemas change frequently or unpredictably, maintaining Jolt transformation specifications to accommodate these changes can become cumbersome. Agile adaptation to schema changes is necessary.
Conclusion
Jolt Transformation stands as a valuable tool in the world of JSON data manipulation. Its declarative approach to transforming data simplifies the process of mapping, filtering, and reshaping JSON structures.
- Jolt excels at converting JSON data from one format to another without the need for custom code, ensuring consistency and efficiency in data processing.
- Its flexibility allows for a wide range of use cases, from API data normalization to data integration, data validation, and much more.
- Jolt’s dynamic runtime transformation capability enables it to adapt seamlessly to different input data structures, a vital feature in today’s diverse data landscape.
- While there are challenges, such as a learning curve and complex transformation scenarios, these can be overcome with practice, proper documentation, and adherence to best practices.
- Jolt Transformation empowers developers to streamline data manipulation tasks, improve code maintainability, and enhance the consistency of JSON data structures across applications.
As JSON data continues to play a central role in modern software development, Jolt’s ability to handle complex transformation scenarios and maintain data uniformity positions it as a valuable tool for data-centric projects. By understanding its capabilities and addressing its challenges, developers can harness the power of Jolt to elevate their data transformation workflows.
Additional Resources:
https://docs.digibee.com/documentation/components/tools/transformer-jolt/transformer-getting-to-know-jolt
