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This blog post will provide guidance to administrators currently using or interested in using Kafka nodes to maintain cluster changes as they scale up or down to balance performance and cloud costs in production deployments. Kafka brokers contained within host groups enable the administrators to more easily add and remove nodes. This creates flexibility to handle real-time data feed volumes as they fluctuate.

Kong Gateway provides dynamic plugin ordering allowing administrators to control plugin execution order. Dynamic plugin ordering was added in Kong Enterprise 3.0 and the full technical reference is available in the official documentation. (Check out the overview of what’s new in Kong Gateway 3.0 announced at Kong Summit 2022.)

Principal Component Analysis (PCA) is one of the most commonly used unsupervised machine learning algorithms across a variety of applications: exploratory data analysis, dimensionality reduction, information compression, data de-noising, and plenty more. In this blog, we will go step-by-step and cover: Before we delve into its inner workings, let’s first get a better understanding of PCA. Imagine we have a 2-dimensional dataset.

As your data volumes grow, your operations slow down. Data ingestion - extraction of all underlying datasets, transformation, and loading in a storage destination (such as a PostgreSQL or MySQL database) - becomes sluggish, impacting processes down the line. Affecting your data analytics and time to insights. Change Data Capture (CDC) makes data available faster, more efficiently, and without sacrificing data accuracy. In this blog we are going to overview the 7 best change data capture tools of 2022.