Project Name
Data Mapping from MySQL to MongoDB Database with Apache NiFi
Industry
Service
Technology
Apache NiFi, MongoDB, MySQL, Apache Kafka
Overview
Our client was a leading service provider who was facing challenges in transitioning the data mapping from MySQL to MongoDB while maintaining data integrity and minimizing operational disruptions.
Challenges
Our client was facing several challenges, including:
- Complexities in transitioning data mapping from a traditional MySQL database to a NoSQL MongoDB environment.
- Differences in potential data structures between MySQL and MongoDB lead to difficulty adapting the existing mapping.
- Another challenge was minimizing the disruptions to ongoing operations while implementing.
- It ensures data consistency and maintains the correct ordering of messages in Kafka during the data mapping transition.
Our Solution
The Ksolves team has implemented the Apache NiFi technology that is mentioned in the below steps:
- First, our team leverages the use of NiFi technology to address the challenges in data mapping from MySQL to MongoDB databases.
- Then, we designed the Apache NiFi flow to fetch the required data mapping from the MongoDB collection and save it to another designated MongoDB collection.
- We then integrated Kafka with Apache NiFi flow for efficient consumption and data storage to ensure a robust and reliable data transmission process.
- This helped our client treat the entire data mapping process as a happy flow that guarantees the integrity of the data and prevents data loss during transmission.
- Ksolves has implemented error-handling mechanisms to address the various conditions during the data mapping process, such as missing data or the presence of special characters.
- To maintain the consistency of data formats, we utilized clean JSON formats to maintain data consistency and eliminate issues arising from irregularities in the data.
- Ksolves then incorporated the latest expressions and technologies with NiFi to create an efficient workflow. Also, accommodate the data retrieval from both Kafka and MongoDB for the correct data mapping with the appropriate database.
- The next step that we have done is to integrate 19 subflows to perform various mappings and ensure instant merging of subflow data without data loss or errors.
- We implemented data type conversion instantly within the Apache NiFi flow using the JoltTransform processor. This conversion mechanism provides flexibility and avoids sole reliance on Jolt Transform.
- Then, we implemented a wait-and-notify processor mechanism to efficiently manage the data flow at different stages, ensuring a well-orchestrated workflow.
- For optimized data access, we employed the FetchDistributedMapCache and PutDistributedMapCache processors to leverage the DistributedMapCacheClientService.
- This solution helped to achieve seamless storing and retrieval of data from the distributed map cache, which optimized data access and retrieval.
- For content data storage mechanisms, we developed a mechanism to store content data into attributes within the NiFi flow and enable efficient retrieval of data from attributes for streamlined data processing.
Data Flow Diagram
Conclusion
With the successful implementation of Apache NiFi Technology, the Ksolves team not only addressed the challenges but also helped our client provide advanced error-handling mechanisms and instant integration of various sub flows. Moreover, with the right solution, clients get the assurance of an optimized and efficient process that gives a happy flow without any loss of data or errors.
