Learn about the Data Mesh and the alternative choices — Data mesh is an architectural pattern that aims to improve the management and governance of data in organizations. It is based on the idea of creating a decentralized and decentralized data governance structure, where data is treated as a first-class citizen and given its own governance model. Data mesh is…

Caching is a common technique used in big data systems to improve the performance of data processing and analysis by storing data in memory for quick access. This can be especially beneficial for workloads that involve repeated access to the same data, such as in iterative algorithms or machine learning…

A semantic layer in data is like a translator for your data. It’s the middleman that helps your various data sources communicate with each other and make sense to the end user. Let’s say you have sales data coming in from your e-commerce platform, customer data from your CRM, and…

5 Features That Will Make Your Data Processing Hotter Than a Habanero — Apache Spark is an open-source distributed data processing engine that is widely used for large-scale data analytics. It offers several key features that make it an ideal choice for data engineers and data scientists working with big data. One of the key features of Apache Spark is its observability, which…

Change feed change: it’s the buzzword that’s on everyone’s lips these days. But what is it, and why is it so important? First, let’s define our terms. A change feed is a stream of changes made to a database or other data store. This can include inserts, updates, and deletes…

Hey there, Spark fans! Are you tired of slow and clunky Structured Streaming pipelines? Fear not, because I’ve got 5 super effective ways to optimize your Spark Structured Streaming pipelines and make them lightning fast! And, as an added bonus, I’ll provide some handy Pyspark code examples to help you…