As the growth in embedding analytics into a product, application or service continues to accelerate in the market, some issues continue to frustrate our ISV and Data Provider partners in their journey to the cloud.
Fivetran SAP AppConnect enables enterprises to perform log-based change data capture despite common license restrictions for databases managed by SAP applications.
Over the years, vast amounts of satellite data have been collected and ever more granular data are being collected everyday. Until recently, those data have been an untapped asset in the commercial space. This is largely because the tools required for large scale analysis of this type of data were not readily available and neither was the satellite imagery itself. Thanks to Earth Engine, a planetary-scale platform for Earth science data & analysis, that is no longer the case.
Google Earth Engine (GEE) is a groundbreaking product that has been available for research and government use for more than a decade. Google Cloud recently launched GEE to General Availability for commercial use. This blog post describes a method to utilize GEE from within BigQuery’s SQL allowing SQL speakers to get access to and value from the vast troves of data available within Earth Engine.
Migrating data to the cloud can be a daunting task. Especially moving data from warehouses and legacy environments requires a systematic approach. These migrations usually need manual effort and can be error-prone. They are complex and involve several steps such as planning, system setup, query translation, schema analysis, data movement, validation, and performance optimization.
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.
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.
