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Data Protection in a Complex Hadoop Deployment

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Summary: One of the world’s largest financial services companies deploys Imanis Data to protect their Hadoop data lake and support business-critical analytics capabilities.

 

Industry: Financial Services. One of the largest financial services companies in the world that provides personal and commercial banking, corporate and investment banking, wealth management services, insurance, and transaction processing services globally. The company has embarked on a Big Data journey to enable next-generation analytics and smarter data management.

 

Hadoop Environment: The company has standardized on Hadoop as their Big Data platform and created a Data Lake. All customer transactions (~400M per day) are archived to the Data Lake for compliance and retention needs. Machine learning, cognitive computing, and predictive analytics are used against the archived data to drive next-generation analytics for their clients. The company has set up three distinct Hadoop clusters for specific use cases. The first consists of 40 nodes hosting 425 TBs of transaction data. The second is a 16-node cluster with 180 TB of data for fraud detection and the third is a 8-node cluster with 50 TB of data to obtain customer insights.

 

Challenges: Availability and reliability of the data lake is critical to the company, as any kind of downtime or data loss would be disastrous to their Big Data initiatives and have a negative impact on the business and consumer trust. However, managing this large and complex Hadoop environment has been a challenge for the customer. The admin team was under constant pressure handling hardware failures, software updates, security issues, and capacity management. On top of that, they were writing and maintaining many different scripts to back up these Hadoop clusters. The backup operations team is responsible for running the scripts on a nightly basis and addressing data recovery requests on an ad hoc basis. These scripts were very fragile and frequently failed requiring frequent late night calls to the admin team. Data recovery was also a fire drill because it involved multiple steps such as locating the right backups, performing data recovery, and verifying that the right data was recovered. With the growth and complexity of the Hadoop environment, the backup and recovery costs were spiraling and the customer was unable to meet the recovery time objectives (RTO) mandated by their business. At that point the customer started looking for commercial alternatives.

 

Solution: The customer chose and deployed a 10-node Imanis Data cluster in their production environment to address their backup and recovery needs for their Hadoop infrastructure. Imanis Data’s scale-out architecture gives them plenty of room to grow as the size of their primary Hadoop cluster and corresponding data grows or they bring additional Hadoop clusters online in the future. Now that Imanis Data is deployed in their production environment, the admin team is freed from what was becoming an unmanageable situation and backup and recovery is now the sole responsibility of the backup operations team. Using a single UI to manage backup and recovery across their entire Hadoop environment, the backup operations team is now able to focus on providing better and faster service when a business user requests data to be recovered. Backups are completely centralized and automated without relying on fragile scripts and requiring constant monitoring. Their backup storage costs have also gone down significantly due to the Imanis Data storage optimization (de-duplication and compression) and incremental-forever backup methodology.

 

The customer is also now able to meet and exceed their Recovery Point Objective (RPO) and their Recovery Time Objective (RTO) since Imanis Data allows them to back up their data more aggressively on an hourly schedule and recover data very quickly using the Imanis Data one-step restore process.

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