In a previous article, we gave a theoretical introduction to Spark Structured Streaming where we analysed in depth the high-level API that Spark provides for processing massive real-time data streams (Structured Streaming). There, we looked at the essential theoretical concepts…
In recent years, data processing with low latency, practically in real time, is becoming a requirement increasingly demanded by companies in their big data processes. It is in this context where the concept of stream processing is introduced, which refers…
Apache Spark’s Structured Streaming API is a powerful tool for processing real-time data streams. In this context, there are certain use cases where ensuring the accuracy of the processed data is not trivial due to the time dimension that inherently…
In 2020 Apache Spark released version 3.0.0.0 which introduced some changes to the API for defining custom data sources, known within the Spark environment as Custom Data Source. These were previously used through DatasourceV2, which generated confusion and an unintuitive…
We know that there is a large number of products in the data engineering ecosystem to perform the data processing of a company and that most of them provide the necessary tools to be able to perform many types of…
In this post we are going to talk about how DBT integrates with Spark and how this integration can be useful for us. DBT is a framework that facilitates the design of data modeling throughout the different data modeling cycles.…
Apache Spark is an open source framework that allows us to process large volumes of data in a distributed way. How? By dividing the large volumes of data, impossible to process in one machine, and distributing them among the different…
When processing data we often find ourselves in a situation where we want to calculatevariables over certain subset of observations. For example, we might be interested in theaverage value per group or the maximum value for each group. The groupBy…
