Full-text search is one of Elasticsearch's strengths.
However, what if we wanted to find similar documents based on something more abstract - like the meaning of a word or the style of writing?
This is where Elasticsearch's dense vector field datatype and script-score queries for vector fields come into play.
This is the first part of a two-part blog post that is concerned with extracting data from Jira and using it for further applications such as visualization, evaluation, and using the power of machine learning to gain valuable insights in the data.
Data scientists often find themselves spending a lot of time with data acquisition and preparation,
yet most tutorials start with ready to use datasets.
This time we will start with nothing but a simple problem and gather the data with scrapy to provide insight into the
process from data gathering to model creation.
Cities are complex systems, characterized by massive numbers of interconnected citizens. In the advent of new technologies, such as IoT or mobile applications, various domains within these systems can be enhanced to support the decisions of its citizens, businesses, or administration.
In this blog post the NoSQL world will be introduced in as simple words as possible. All main types of NoSQL databases will be described incuding some examples for every one of each to have a good understanding of the subject. The world has changed recently and the NoSQL trend is getting stronger and stronger. Old fashion applications working only with relational databases are rare nowadays. The way of developing applications has changed, single server side architecture and heavy clients is not a trend anymore. Till 2010 number of Internet users grew almost linearly – that means more and more…