Project Name
How Ksolves Optimized Large-Scale Data by Migrating from RDBMS to Apache Cassandra (NoSQL)?
Our client operates in the network industry, collecting data from millions of IoT devices stored in MySQL. As their data volume grew exponentially it reached gigabytes. To address this, they needed a scalable and efficient system to manage and optimize their data storage.
Our client was facing multiple challenges and needed a reliable solution to address them. The key issues were:-
- The production system had millions of modems requiring frequent data collection and storage in a database multiple times a day. We also needed to retain historical data for 6–12 months, but managing it with an RDBMS was challenging and inefficient.
- Cassandra is designed for horizontal scalability, allowing easy expansion across multiple nodes. It efficiently distributes large volumes of data across a cluster, unlike RDBMS, which struggles with scaling.
- Cassandra’s distributed design and optimized storage ensure high read and write throughput. It excels in write-heavy workloads and efficiently handles large-scale concurrent transactions.
- Cassandra’s decentralized architecture ensures high reliability by eliminating single points of failure, unlike RDBMS, which relies on single servers and is more prone to failures.
- Cassandra runs efficiently on commodity hardware, making it a cost-effective choice compared to the high infrastructure costs of RDBMS for large-scale deployments.
- Cassandra excels at managing large-scale, distributed, and unstructured datasets, making it ideal for big data applications. It outperforms RDBMS in scalability and performance for such use cases.
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.
Finally, our comprehensive approach facilitated a well-structured migration for our client. By implementing custom Bash scripts, optimized Cassandra data modeling, performance tuning techniques, and continuous support, we successfully transitioned from RDBMS to Apache Cassandra. This migration delivered a highly scalable and efficient solution for managing large-scale, distributed data.
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