Big Data Analytics

When delving into crafting a new and efficient Spark job, or optimising an existing one, multiple implementation and design choices may have a significant impact on the job’s performance. One of the most prominent aspects influencing a Spark job’s efficiency is the fundamental difference between actions and transformations.

In the digital realm where data reigns supreme, managing cloud expenses is no longer just an option, it is absolutely vital. With this in mind, have you evaluated your cloud spending recently? Are you fully aware of where your resources are allocated and confident that no hidden costs are draining your budget? If these issues have crossed your mind, our recent webinar may hold the answers you seek.

In this article we will present some key areas that affect the future of Big Data analytics, while focusing on such topics as cloud migration, the development of native data platforms for cloud environments, real-time data processing and more.

dataset-logger, our open-source tool for Apache Spark, allows tracking and analyzing how particular data records change during Spark job execution.

An exploration of how Datumo's expert implementation of the Google Cloud Platform transformed iTaxi's data infrastructure, empowering data democratization and accelerating data-driven decision-making processes.

Deploying a Big Data platform doesn’t need to happen on a large scale. This article shows how to seamlessly bootstrap an Apache Kafka instance on your local Minikube/Kubernetes cluster.