DBSCAN: how it works
Clustering algorithms allow data to be grouped according to their inherent characteristics. There are many algorithms that have been developed over the years. Among the most famous ones, we cannot forget DBSCAN. Let’s find out, step by step, how this method succeeds in identifying clusters of data of any shape and size thanks to only two parameters.
ChatGPT: what it is and what the revolutionary new text generation tool can generate
ChatGPT is OpenAI’s flagship product. This chatbot, which is completely free, allows us to interact with artificial intelligence to generate ideas, articles, code, and more. Let’s discover its features and some possible application areas.
Elasticsearch: aggregation pipeline
Elasticsearch offers the ability to extract statistics and data sets using aggregation functions. In many contexts, however, it is necessary to concatenate the results of these analyses to obtain more refined results. Let us see how aggregation pipelines allow us to obtain the results of our interest.
Elasticsearch: bucket aggregations [part 2]
With Elasticsearch’s bucket aggregations we can create groups of documents. Having seen in the previous article aggregations based on keyword type fields, we will now focus on other functions oriented to other data types. In particular, we will use aggregations for definitions of numeric ranges, dates, or groups based on georeferenced data.
Elasticsearch: bucket aggregations [part 1]
With Elasticsearch’s bucket aggregations we can create groups of documents. In this article we will mainly focus on aggregations based on keyword type fields in indexes. We will use several examples to understand the main differences between the available aggregation functions.
Elasticsearch: metric aggregations
In addition to text search, Elasticsearch allows analysis on data using aggregations. Among the various types of aggregation available, the metric ones are aimed precisely at calculating statistics on one or more fields. Through examples we will see what information we can extract with this type of aggregation.
Elasticsearch: the aggregation types
Elasticsearch is a widely used NoSQL database for developing search engines because of its ability to index text appropriately. But it does not stop at just that. Thanks to aggregations, Elasticsearch can be used to analyze data and extract statistics from large masses of data. Let’s learn about this functionality of his that underlies many visualizations used by Kibana.
How to sell your API: the best marketplaces
To date, many companies expose their products on the Web through APIs. Some startups, especially in the fintech sector, base much of their economics and growth solely on APIs. Monetizing an API therefore becomes a critical step for any developer. There are various approaches to making money from one’s code. In this article we will discover how to reach millions of potential users and sell our APIs quickly through the most popular API marketplaces.
Elasticsearch: join and bonus queries
Elasticsearch is a very good NoSQL database for performing efficient searches on textual and structured data. Despite this, it does not natively support joins between documents. However, there are queries that, by means of an appropriate schema definition, allow searches on related documents between them. We will find out how to write join queries and also some particular queries that might be useful in our projects.
Elasticsearch: compound query
Elasticsearch offers a very valuable tool for performing simple as well as complex searches. In this article we will understand how to include multiple conditions in the same query and modify the score calculation based on custom functions and data values.
