Victoria Livschitz

OPINION: Coronavirus is rapidly reshaping our world. It is demanding businesses to redefine how they engage customers, employees, and supply chain by shifting operations online. Learn best practices for designing and launching new digital services at lightning speed by adopting an “agile co-creatio...

Many large brick-and-mortar retailers lack a strong mobile app experience, which is a problem on days like Black Friday and Cyber Monday, when online traffic soars. In the 2018 holiday season, 79% of all purchases involved a mobile device - this includes customers that made a purchase, looked up...

Write once, run anywhere. This is what mobile application developers have long been promised. One code for both Android and iOS No performance issues One engineering and QA team for all mobile development One common user interface utilized across all platforms that is visually...

The retail industry is embracing big data, analytics, and machine learning (ML) to improve customer engagement, optimize operations, and drive sales. Increasingly, we’re seeing business intelligence systems that were once based on historic data, offline modeling, and traditional reporting being r...

If you are an enterprise omni-channel retailer with a significant and growing online presence, it is likely that Oracle ATG has been your platform of choice for many years. You have probably invested thousands of hours and tens of millions of dollars in the customization, integration and tuning of...

In the previous blog post we explained our overall approach to the DevOps stack used for the deployment and management of the In-Stream Processing blueprint. In this post we’ll focus on more details of Mesos and Marathon, and provide you with scripts to provision the complete computational environm...

This post is about the approach to the “DevOps” part of our In-Stream Processing blueprint — namely, deploying the platform on a dynamic cloud infrastructure, making the service available to its intended users and supporting it through the continuous lifecycle of development, testing, and roll-ou...

In the previous post we discussed which models we tried for sentiment classification and which one has demonstrated the best performance. In this post, we’ll show you how to visualize our under-the-hood findings so that others can see the results of our analysis. You can see our twitter senti...

In previous posts we have discussed the steps needed to understand and prepare the data for Social Movie Reviews. Finally, it is time to run the models and learn how to extract meanings hidden in the data. This blog post deals with the modeling step in the Data Scientist’s Kitchen. At the...

In the previous post we discussed how we created an appropriate data dictionary. In this post we’ll address the process of building the training data sets and preparing the data for analysis. The training process aims to reveal hidden dependencies and patterns in the data that will be analyzed...

In the previous post we discussed the structure of the tweet data. In this post we’ll address the process of selecting or building the right data dictionary for our purpose. What constitutes a good dictionary? A crucial data set for any kind of text mining is a dictionary. As for sentiment...

In the previous post we outlined the basic scientific method used and formalized the problem statement we are solving, which is, “Based on of the tweets of English-speaking population of the United States related to selected new movie releases, can we identify patterns in the public’s sentiments t...

There is a broad and fast-growing interest in data science and machine learning. It is fueled by an explosion in business applications that rely on automated detection of patterns and behaviors hidden in the data, that can be found by software and exploited to dramatically improve the way we mark...

In the previous four blog posts in this series we covered the reference architecture of a general purpose In-Stream Processing Service blueprint. To recap, here is a list of shortcuts to the blogs in that series: … Launch new digital services faster with distributed teams and agile co-c...

As we explained in our introduction to this series of posts, we are exploring a data scientist’s methods of extracting hidden patterns and meanings from big data in order to make better applications, services, and business decisions. We will perform a simple sentiment analysis of a real publ...

In the course of delivering many successful Continuous Performance Testing (CPT) implementations for enterprise customers, Grid Dynamics engineering teams have developed a number of basic design principles to guide their actions. Your requirements may be unique, but just as all custom race cars hav...

Solr/Lucene has emerged over the last few years as a leading open source search platform for large-scale e-commerce search engines. Systems based on Solr power major sites including Macy’s, Kohl’s, Walmart, Etsy, and many others. An increasing number of tier-1 digital retailers are building their...

This article introduces the Grid Dynamics blueprint for in-stream processing. It is based on our experience and the lessons we have learned from multiple large-scale client implementations. We have included cloud-ready configuration examples for Apache Kafka, Spark...

This post contains a brief survey of better-known products related to in-stream processing that are available on the market at the time of this writing. In this survey, we focus specifically on critical architectural differentiations, rather than functional differences, that affect why custome...

Now that we have introduced the high-level concepts behind In-Stream Processing and how it fits into the Big Data and Fast Data landscapes, it is time to dive deeper and explain how In-Stream Processing works. As we already know, In-Stream Processing is a service that takes events as input and...

In-stream processing is a powerful technology that can scan huge volumes of data coming from sensors, credit card swipes, clickstreams and other inputs, and find actionable insights nearly instantaneously. For example, in-stream processing can detect a single fraudulent transaction in a stream...