NortonLifeLock offers a VPN product as a freemium service to users. In addition to NortonLifeLock’s implementation, this post provides key lessons learned and best practices for rapid development of real-time analytics workloads. NortonLifeLock is taking a methodical approach to real-time analytics on AWS while using serverless technology to deliver on key business drivers such as time to market and total cost of ownership. These services include managed Hadoop infrastructure services on Amazon EMR as well as serverless options such as Kinesis Data Analytics and AWS Glue.Īmazon EMR also supports multiple programming options for capturing business logic, such as Spark Streaming, Apache Flink, and SQL.Īs a customer, it’s important to understand organizational capabilities, project timelines, business requirements, and AWS service best practices in order to define an optimal architecture from performance, cost, security, reliability, and operational excellence perspectives (the five pillars of the AWS Well-Architected Framework). Real-time streaming analytics helps capture this value and provide new insights that can create new business opportunities.ĪWS offers a rich set of services that you can use to provide real-time insights and historical trends. This lost value can equate to lost revenue and wasted resources.
NortonLifeLock has been an AWS customer since 2014.įor any organization, the value of operational data and metrics decreases with time. NortonLifeLock believes the digital world is only truly empowering when people are confident in their online security. NortonLifeLock is a global cybersecurity and internet privacy company that offers services to millions of customers for device security, and identity and online privacy for home and family.
In addition, this post shows the design approach that the engineering team at NortonLifeLock took to build out an operational analytics platform that processes usage data for their VPN services, consuming petabytes of data across the globe on a daily basis. This post presents a reference architecture and optimization strategies for building serverless data analytics solutions on AWS using Amazon Kinesis Data Analytics.
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