E-Commerce

Deliver hyper-personalized, omnichannel experiences with ease

User Personalization
User Personalization

Description

Build an application that can tailor a service, content or a product in an on-going manner such that it is more relevant to specific individual users as well as groups or segments of users. The aim is to improve customer satisfaction, digital sales conversion, branding and other such key metrics.

Requirements

  • Needs to serve end-user facing queries with low latencies in the foreground, and analyze data in the background constantly.

  • Data for user personalization flows in from multiple sources. Examples of these sources include the user’s profile, their interaction history (order history) with the business and their search patterns on the website. The data layer should be able to handle a high write throughput from all these sources.

  • The collected data needs to be analyzed in order to come up with good recommendations for personalization for that user, which are written back into the serving tier to show the user. Apache Spark is a commonly used framework to perform this analysis.

  • The analysis needs to happen in a nearly realtime fashion, therefore using a traditional data warehouse which takes multiple hours for analysing the data is not ideal.

User Personalization

YugaByte DB
Why YugaByte DB?

Strong Consistency with High Performance

Strong Consistency with High Performance

Strong consistency by default, without giving up on performance. Multiple deployment options to power low latency reads and writes.

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Unified Data Platform for Distributed OLTP and Fast Data Apps

Unified Data Platform for Distributed OLTP and Fast Data Apps

Data tier simplification through unificaiton of the serving tier that serves the data to the end user and the analytics tier that computes the recommendations for the users. Eliminate the need for an ETL pipeline and a data warehouse. Easy to deploy and manage while ensuring delivery of real-time results.

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 Reliable, Fast Scale-Out

Reliable, Fast Scale-Out

Scale out the operational DB tier reliably and efficiently without any impact to the currently running application. Dynamically resize the cluster for the serving tier as well as the analytics tier. Change machine types without any interruption to the application.

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Strong Consistency with High Performance

Integrated with Apache Spark and the SMACK stack

Natively integrated with Apache Spark for fast analytics and with Apache Kafka for distributed streaming. Stores data in a time-partitioned manner for efficient retrieval of recent data.

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Reliability, Performance and Agility in a Single Database