Hadoop Big Data Analytics


The client is a large, American Fortune 200 Fintech company that operates an online payments system in most countries that support online money transfers. The client engaged Quantilus to tackle the challenges with their existing analytics platform based on Oracle.


The client was facing several challenges with their existing analytics platform that was based on Oracle. The main issues were: 

  • Difficulty in processing large volumes of data in a timely manner due to Oracle’s limited scalability. 
  • High costs of maintaining and licensing the Oracle database, which was proving to be a bottleneck for the company’s growth. 
  • Lack of flexibility and agility in managing data and processing analytics due to the rigidity of the Oracle architecture. 
  • The need to handle unstructured and semi-structured data, which was not well-supported by Oracle. 


The company wanted to migrate to a Hadoop-based Big Data platform to address these issues and gain new capabilities for their analytics needs. 


The company opted for a solution that involved migrating their analytics platform from Oracle to Hadoop using an automated data and metadata migration tool. The key features of the solution  were: 

  • Automatic discovery and migration of Oracle database schemas to Hadoop. 
  • Large-scale data migration with parallel processing to minimize downtime and ensure minimal disruption to business operations. 
  • Automatic conversion of SQL queries to Hive queries for seamless migration of existing analytics applications. 
  • Built-in data quality checks to ensure data accuracy and completeness. 
  • Automated metadata migration to ensure continuity of data lineage and governance. 


The migration was done in a phased manner, with a series of tests to validate the data and ensure that the new Hadoop-based platform was able to meet the company’s analytics needs. 

The migration to a Hadoop-based analytics platform had several benefits for the Fintech company, including: 

  • Improved scalability and performance, enabling the company to handle larger volumes of data and process analytics in a timely manner. 
  • Significant cost savings due to the open-source nature of the Hadoop platform and the ability to run on commodity hardware. 
  • Greater agility and flexibility in managing data and processing analytics, with the ability to handle unstructured and semi-structured data. 
  • Better support for data governance and lineage due to the automation of metadata migration. 
  • The ability to leverage the Hadoop ecosystem and its rich set of tools and technologies for advanced analytics and data processing. 


The migration to a Hadoop-based analytics platform proved to be a game-changer for the Fintech company, enabling them to take their analytics capabilities to the next level and stay competitive in their industry. 



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