BigQuery: performance optimization

Although BigQuery is a very good tool for querying terabytes, best practices should be adopted to improve performance. Let’s discover tricks for writing queries that execute quickly and save on execution costs. We also look at how you can optimize table storage through partitioning and clustering.

BigQuery: WINDOWS analytics

In many application scenarios, the statistics you need to extract refer to different groupings on the source data. By defining aggregation windows, you can calculate statistics within the same query. Moreover, if necessary, you can also provide different levels of data granularity through the ARRAY data type. Let’s discover these advanced features through two real-world examples.

BigQuery: GIS functions and Geo Vis

Geographic data plays a very important role in various analyses. BigQuery includes GIS functions in addition to the SQL standard to query, manipulate and analyze this kind of information. Let’s find out how to use and visualize them using Geo Vis.

BigQuery: WITH clause

Extracting data and analyzing it is a process that requires knowledge of data sources and the ability to write complex queries. BigQuery, Google’s database, makes it easy to access terabytes of data. Query writing, however, requires method. Let’s discover the WITH clause to increase the readability of our queries.

Jupyter Notebook: user’s guide

The development of data analytics pipelines by Data Scientists requires several skills. Having an easy, intuitive, and interactive development environment is critical. Jupyter Notebook is an open source web application that allows you to create and share interactive textual documents, containing objects such as equations, graphs and executable source code in different languages. Let’s discover its main features.

Chatterbot: create a chatbot in python

Chatbots are a technology that allows you to automate interaction with users. Leveraging the latest artificial intelligence technologies, conversations turn out to be more and more real. Examples of chatbots evolution are virtual assistants like Alexa, Cortana and Siri. Let’s find out how you can develop a simple chatbot in Python using the Chatterbot library.

AutoML Vision: image classification

Developing classification models for unstructured data, such as images or text, is not an easy task. In many cases, very specific development skills are required. Let’s find out how it is possible, using AutoML Vision from Google Cloud, to create an image classification model without writing a line of code but only selecting images for our model.

Google Cloud: introduction to the platform

Big Data is one of the most profound and pervasive evolutions of the digital world. A trend that is destined to remain and to profoundly affect our lives and the way we do business. Managing them requires very powerful computing infrastructures. The big giants of the Web, including Google, Amazon and Microsoft, provide their data centers and platforms to address the challenges offered by Big Data. Let’s find out about the computing power provided by Google Cloud through some case studies.

MongoDB 5: the new features

MongoDB is the most widely used NoSQL database in the world. Its continuous growth is due to the continuous development of new features. Version 5, released at the end of July 2021, introduced some very interesting new features. In this article we will analyze the most relevant and most useful in their daily use.

Pandas and Bokeh: create interactive graphics

Analyzing data also requires graphing the data or the results from the analysis performed. Many libraries in Python provide useful tools for visualization, but the plots produced are static. The Pandas Bokeh library is a great alternative for creating interactive plots and including them in web projects. Let’s find out how to use it and the results we can achieve through some examples.