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Project Name

Data Migration From RDBMS to Apache Cassandra (NoSQL) For Performance Optimization Techniques

Industry
Telecommunication
Technology
Apache Cassandra, MySQL, PostgreSQL, Bash Scripts

Overview

Our client belongs to the network industry. Their work was to collect the data from millions of IOT devices and the entire data was stored in MySQL. Their data started growing and size started to increase in GB exponentially. Because of this, they came with a requirement to get a system where they can manage their data properly.

rdbms-overview

Challenges

rdbms-challenges
  • There were millions of modems on the production system for which the Client needed data to be collected multiple times a day and saved to the database. Also, we wanted to maintain historical data of 6-12 months. It was hard and inefficient to handle it through RDBMS.
  • Cassandra is designed for horizontal scalability, allowing seamless expansion across multiple nodes. It can handle massive volumes of data by distributing it across a cluster, unlike RDBMS, which often faces scaling challenges.
  • Performance: With its distributed nature and optimized storage format, Cassandra delivers high read and write throughput. It's particularly efficient for write-heavy workloads and can handle large-scale concurrent transactions.
  • Cassandra's decentralized architecture eliminates single points of failure, enhancing reliability. RDBMS, in contrast, often relies on single servers, making them vulnerable to failures.
  • In terms of hardware costs, Cassandra's ability to run on commodity hardware can make it more cost-effective compared to the often expensive infrastructure requirements of RDBMS for large-scale deployments.
  • Cassandra is well-suited for handling large-scale, distributed, and unstructured datasets commonly associated with big data applications, offering better performance and scalability compared to RDBMS in such scenarios.

Our Solution

Migrating data from a Relational Database Management System (MySQL) to Cassandra, a NoSQL database, involves several key steps. Initially, it's crucial to comprehend the structural disparities between RDBMS (which follows a tabular structure) and Cassandra (with its columnar, distributed setup). Here's a summary of the process:

  • We started by comprehending the existing RDBMS schema and understanding how it maps to Cassandra's data modeling. Cassandra is optimized for different access patterns compared to traditional RDBMS.
  • Our team redesigned the schema to fit Cassandra's requirements. This might involve denormalizing tables, considering wide columns, and designing for query patterns as Cassandra's schema design is query-driven.
  • After that, we extracted the data from the RDBMS. Various tools and methods can aid in this process, such as Apache Spark, Talend, or custom scripts tailored to the specific databases involved. We have extracted the data in CSV using custom scripts.
  • We converted the data into a format compatible with Cassandra's structure. This might involve restructuring, aggregating, or transforming data to suit the new schema we did in our bash script.
  • Then, we loaded the transformed data into Cassandra and utilized Cassandra's data loading utilities or custom scripts to efficiently ingest the data into the new database.
  • Thoroughly tested the migrated data to ensure accuracy and integrity. Verify that the data in Cassandra aligns with expectations and accurately represents the original RDBMS content.
  • Our team fine-tunes the Cassandra configuration and data model for optimal performance. This step involves tweaking settings, adjusting partitioning strategies, and optimizing queries for efficient data retrieval.
  • After that, our team planned for ongoing synchronization or incremental updates during the migration phase to ensure data consistency between the RDBMS and Cassandra until the complete switchover.
  • Finally, we established monitoring systems to track Cassandra's performance and maintain the database over time. Regular maintenance and monitoring are crucial for ensuring the system's stability and reliability.

Data Flow Diagram

rdbms-data-flow

Conclusion

At last, our comprehensive approach helped our client with a well-structured migration process. Moreover, the use of custom Bash scripts, Cassandra data modeling, performance optimization techniques, and ongoing support gives a successful migration from an RDMS to Apache Cassandra and provides a more scalable database and efficient solution for handling large-scale, distributed data.

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