Download PDF
---nasdaq--amzn_14.jpg "Amazon Web Services Logo")
Salesforce DMP Leverages AWS for Efficient Data Processing and Enhanced Customer Engagement
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
Salesforce DMP
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
Quantitative Benefit
Related Case Studies.
Case Study
Smart Water Filtration Systems
Before working with Ayla Networks, Ozner was already using cloud connectivity to identify and solve water-filtration system malfunctions as well as to monitor filter cartridges for replacements.But, in June 2015, Ozner executives talked with Ayla about how the company might further improve its water systems with IoT technology. They liked what they heard from Ayla, but the executives needed to be sure that Ayla’s Agile IoT Platform provided the security and reliability Ozner required.
Case Study
IoT enabled Fleet Management with MindSphere
In view of growing competition, Gämmerler had a strong need to remain competitive via process optimization, reliability and gentle handling of printed products, even at highest press speeds. In addition, a digitalization initiative also included developing a key differentiation via data-driven services offers.
Case Study
Predictive Maintenance for Industrial Chillers
For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.
Case Study
Premium Appliance Producer Innovates with Internet of Everything
Sub-Zero faced the largest product launch in the company’s history:It wanted to launch 60 new products as scheduled while simultaneously opening a new “greenfield” production facility, yet still adhering to stringent quality requirements and manage issues from new supply-chain partners. A the same time, it wanted to increase staff productivity time and collaboration while reducing travel and costs.
Case Study
System 800xA at Indian Cement Plants
Chettinad Cement recognized that further efficiencies could be achieved in its cement manufacturing process. It looked to investing in comprehensive operational and control technologies to manage and derive productivity and energy efficiency gains from the assets on Line 2, their second plant in India.
Case Study
Integration of PLC with IoT for Bosch Rexroth
The application arises from the need to monitor and anticipate the problems of one or more machines managed by a PLC. These problems, often resulting from the accumulation over time of small discrepancies, require, when they occur, ex post technical operations maintenance.
