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Retail Rocket Technologies

Retail Rocket Technologies

Retail Rocket software engineering team’s vision is to change the world of e-commerce and make it truly personalized.

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Retail Rocket At A Glance:

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    Analytical cluster of more than 250 servers in 6 different data centers

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    More than 230 million unique visitors use our clients' websites monthly

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    More than 1,000 companies connected to Retail Rocket worldwide

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    Our servers process more than 450,000 external API requests per minute

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    API requests per second at peak: 15,000+

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    More than 200 man-years have been invested in the development of the platform

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    Zero customer data lost in 8 years

Data Science Approach

The essence of Retail Rocket’s work is to identify the needs of an online store customer by analyzing their behavior (clickstream, search queries, transaction history, etc.). In addition, we focus on the product matrix of the retailer and also personalize communications in any channel (website, mobile app, email, SMS, etc.). To form personalized recommendations, our data science team developed a scalable mathematical foundation. The following are several approaches we use today:

  • Content Filtering
  • Bayesian Statistics
  • Collaborative Filtering
  • Real-time Hybrid Personalization Algorithms
  • Predictive Analytics Based on Machine Learning and Markov Chains
  • Many Others.

Technology Stack

Analytical Platform

For machine learning, we use Spark based on the Hadoop Yarn platform – a cluster computing system that is best suited for our current tasks. As for native Hadoop components, we have Apache Kafka for data delivery, the distributed Machine Learning Mahout Library, and the Oozie Task Scheduler.

Retail Rocket team has a repository on GitHub with many interesting projects: an engine for A/B tests in JavaScript, a Spark MultiTool library in Scala, scripts for deploying a Hadoop cluster using Puppet, and others.

  • Apache Spark
    Apache Spark
  • Hadoop
  • Clickhouse
  • Scala
  • Kafka
  • Redis


Almost everything the end-user receives is processed in clusters of Linux servers. The code is written in C#, Asp.Net MVC. All data is stored and distributed in three database management systems: Redis, MongoDB, and PostgreSQL.

When we need to ensure the interaction of distributed components, for example, when calculating a user segment, Thrift is used. Additionally, in order for various subsystems to receive a data stream from online stores, Kafka transport, mentioned above, is used.

  • .NET Core
    .NET Core
  • C#
  • Kafka
  • AWS Lambda
    AWS Lambda
  • PostgreSQL
  • Redis
  • MongoDB

Development Process

In development, our team sticks to the methodology of continuous delivery of new functionality to customers (2,000+ stores are currently connected to us).

We use a bundle of Git + GitLab with the unit tests (as of the beginning of 2021, we have more than 3,000+ tests), acceptance tests, and code review.

  • GitLab
  • YouTrack
  • Jenkins
  • JetBrains Rider
    JetBrains Rider
  • Visual Studio Code
    Visual Studio Code
  • Discord
  • Trello

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