delete /dɪˈliːt/ - verb: remove or obliterate (written or printed matter), especially by drawing a line through it.

From the data we can see that Darth Vader likes to bet on tennis matches, but his son Luke likes to bet on English Premier League football. So how to make magic and learn from IDs only?

Kafka Streams is a library that comes with Apache Kafka, it enables easy and powerful stream processing of Kafka events. This way, existing applications can use Kafka Streams API by simply importing the library.

When we decided to open up the SmartCat, we had already gained various experience working mostly for outsourcing web development companies. At first, we tried to apply the same processes and steps to build data science solutions...

Are you working on a project where no data is available to you? Maybe you are expected to work with a small dataset or a dataset that has some missing values. Here are some ideas that might help you in the process.

In this 2nd part of the blog post, we’ll try out Cassandra Change Data Capture (CDC), so let’s get started.

“That will only make someone work just hard enough not to get fired” is what, in my opinion, is the main problem of the management approach that is to this day dominating the corporate scene.

This is the first post in a blog series dedicated to Apache Kafka and its usage for solving problems in the Big Data domain.

Chatbots are computer programs that are able to conduct a conversation via auditory or textual methods. Nowadays, chatbots represent a promising, transformative interface method that can enable users to get more engaged in conversations

Deep learning is a phrase that follows us everywhere. Even the non-technical people are aware how artificial intelligence can change our world. I totally agree with Andrew Ng that “AI is the new electricity” and that it can make our world better.

Instead of questioning the obvious, we asked ourselves: “how come there is no ‘paternity leave’ in the corporate world?”

In this part of blog post, I have laid the necessary steps for injecting the Kafka cluster into system ‘before’ the Cassandra cluster. What I have also tackled is the first step Have a mechanism to push each Cassandra change to Kafka with timestamp.

Disclaimer: This blog post contains a tasty dose of profanities. Some may call use of curse words unprofessional, and 90% of the time they would be right, however, this blog post and this company value of SmartCat is about being real..

In this and the following blog posts, I wish to present and discuss some of the well-known approaches to visualize difficult data sets from the viewpoint of pure ergonomics, as well as to suggest some improvements and new ideas here and there.

Life’s too short! Do it right or don’t do it at all. Aim to impress.

Most probably anyone who is even remotely aware of the nature of contemporary Data Science landscape will recognize the truth of the following two statements: (a) Data Wrangling is necessary with almost every new project, and (b) Data Wrangling is difficult and tedious.

Range /reɪn(d)ʒ/ (noun): the area of variation between upper and lower limits on a particular scale.

A short, non-technical narrative on complexity in Data Science projects and how to communicate it. Essentially, on problems that cannot be solved even in principle, and how to live with it. An illustrative example will probably do in place of a proper introduction.

Why did we organize this meeting? Why do I have to write the minutes of meeting after each call? It is important to know why are we doing things we do - we need to have an end goal and acceptance within us to make us want to do things we do. This is why the second of SmartCat company values is:

The best way to test an infrastructure before going into production is to mimic production load and solve problems that arise. One of the main challenges with this approach is having a load generator that can provide both rate and message size as close to production as possible

This article will be a wrap-up of our series related to collaborative filtering techniques and how to apply them on large datasets in R. In the previous post we covered the nearest neighbours method. Here, the focus will be on the model-based algorithm, namely matrix factorization.

When we decided to start our own company, back in 2015, the first thing we did as founders of SmartCat was define our company values.

In this continuation of part 1 I will describe the application of the {ordinal} clm() function to test a new, hybrid content-based, collaborative filtering approach to recommender engines by fitting a class of ordinal logistic models to ratings data from the MovieLens 100K dataset.

I will use {ordinal} clm() (and other cool R packages such as {text2vec} as well) here to develop a hybrid content-based, collaborative filtering, and (obviously) model-based approach to solve the recommendation problem on the MovieLens 100K dataset in R.