Understanding some of the key characteristics to consider when evaluating and comparing streaming technologies. — Introduction As data architectures are becoming more and more mature, streaming is no longer considered a luxury but a technology with a wide range of applications across different industries. Because of technical and resource limitations, batch processing was in fact always the preferred way to process and deliver applications, although with…

Showcasing Python applications on the web without any server — Introduction Using popular Python visualization libraries it can be relatively straightforward to create locally charts and dashboards of different forms. Although, it can be much more complicated to share your results with other people on the web. One possible approach to do this is using libraries such as Streamlit, Flask, Plotly…

Powering the future of high-performance numerical computing and ML research — Introduction JAX is a Python library developed by Google to perform high-performance numerical computing on any type of device (CPU, GPU, TPU, etc…). …

Getting started with performing geographical data analysis in Python using OSMnx and Kepler.gl — Introduction Geospatial data is ubiquitous and used for many different applications across all businesses (e.g. calculating the risk of properties depending on their location, designing new architecture development, planning shipment of goods, and finding possible routes between different locations). Geospatial data is typically stored in two possible formats: Raster and Vector:

Using Darts to streamline your Python time series analysis development — Introduction Time Series Forecasting is a unique field in Machine Learning. When working with time series in fact there is an inherent time dependency between the different points in the series and therefore the different observations are highly dependent on each other. …

A review of some of the most common Spark performance problems and how to address them — Introduction Apache Spark is currently one of the most popular big data technologies used in the industry, supported by companies such as Databricks and Palantir. One of the key responsibilities of Data Engineers when using Spark, is to write highly optimized code in order to fully take advantage of Spark's distributed…

Exploring some of the key concepts associated with Spark, and what defined its success in the Big Data realm — Introduction One of the main issues that characterized the inception of the internet as we know it today was the inability to scale (e.g. being able to search large amounts of data in a short time, support a constantly varying amount of users without any downtime, etc…). This was ultimately due…

Exploring some of the different approaches which can be used in order to create a side income online. — Introduction Working as a Data Scientist for a company can be a really rewarding opportunity, with average salaries in the US reaching about $117,806 per year in 2021 [1]. …

A guide on how to use Plotly Chart Studio and Datapane to share Python plots on the web — Introduction One of the most important steps in the Data Science pipeline is Data Visualization. In fact, thanks to Data Visualization, Data Scientists can be able to quickly gather insights about the data they have available and any possible anomaly. Traditionally, Data Visualization consisted of creating static images and summary statistics…