Amazon Web Services > Case Studies > Salesforce DMP Leverages AWS for Efficient Data Processing and Enhanced Customer Engagement

Salesforce DMP Leverages AWS for Efficient Data Processing and Enhanced Customer Engagement

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 Salesforce DMP Leverages AWS for Efficient Data Processing and Enhanced Customer Engagement - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Machine Learning
  • Application Infrastructure & Middleware - Database Management & Storage
Applicable Industries
  • Cement
  • Equipment & Machinery
Use Cases
  • Construction Management
  • Infrastructure Inspection
Services
  • Data Science Services
  • System Integration
The Challenge
Salesforce DMP, a leading data management platform, is embedded within every digital interaction between its clients and their consumers. It collects, stores, and makes every piece of audience data continuously available to its clients, managing more than 10 petabytes of on-demand data. This comprehensive data collection approach provides clients with greater accuracy and sophisticated segmentation for targeting and analytics. However, this approach puts enormous demands on the Salesforce DMP system to quickly and efficiently process massive quantities of data. Fast performance and unlimited scaling capacity are crucial to deliver both real-time personalized experiences to consumers and data-driven insights to clients. Salesforce DMP needed tools to ensure it could deliver high return on investment to its clients while continuously developing and bringing new platform features to market.
The Customer
About The Customer
Salesforce DMP, formerly known as Krux, is a leading data management platform that unifies, segments, and activates audiences to increase engagement with users, prospects, and customers. It delivers more relevant and valuable customer experiences by capturing, unifying, and activating data signatures across every device and channel in real time. Salesforce DMP interacts with more than three billion browsers and devices, supports more than 200 billion data collection events, processes more than three billion CRM records, and orchestrates more than 200 billion personalized consumer experiences every month.
The Solution
Salesforce DMP turned to AWS to manage data processing requirements that cover multiple modalities—including near-real time, on-demand, and batch mode—and to perform on-demand analysis of petabytes of data. Salesforce DMP uses a combination of Apache Hadoop on Amazon Elastic MapReduce, (Amazon EMR) and Apache Spark to run machine learning jobs and extract/transform/load (ETL) workloads, with Amazon Simple Storage Service (Amazon S3) as its core distributed storage system. Salesforce DMP leverages the Amazon EMR infrastructure using Amazon EC2 Spot instances to gain access to compute functionality at reduced costs. Salesforce DMP uses the AWS Data Pipeline, a service that moves data between different AWS compute and storage services, to schedule Apache Hadoop and Apache Spark jobs. Salesforce DMP uses Amazon EMR to manage clusters at any time to support its on-demand batch and data-science data processing framework. It also uses Amazon DynamoDB to store user-segment membership data, which is available on different devices and applications globally.
Operational Impact
  • The AWS services have helped Salesforce DMP scale and deliver a highly flexible data-processing service to a global customer base. By using Apache Spark with Amazon EMR and AWS Data Pipeline, Salesforce DMP has seen an increase in the performance of its iterative jobs, making its systems faster and more efficient. The use of Amazon EC2 Spot instances has resulted in significant cost savings. The flexibility provided by AWS allows Salesforce DMP's clients to change their segmentation strategies without limits, enabling them to experiment and innovate using the platform to ensure their business results. This would not have been possible with traditional, non-cloud-based infrastructure environments.
Quantitative Benefit
  • Increased the processing speed for its data science and ETL work by 100 percent.
  • Saved up to 40 percent over Amazon EC2 Reserved-instance pricing.
  • Saved up to 80 percent over on-demand pricing.

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