The MLOps Blog

MAY 17, 2023

However, efficient use of ETL pipelines in ML can help make their life much easier. This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines.

ETL 59

ETL Data Pipeline ML ML 59

Smart Data Collective

OCTOBER 20, 2021

In this new reality, leveraging processes like ETL (Extract, Transform, Load) or API (Application Programming Interface) alone to handle the data deluge is not enough. As per the TDWI survey, more than a third (nearly 37%) of people has shown dissatisfaction with their ability to access and integrate complex data streams.

ML 133

ML ML Big Data Big Data 133

Pickl AI

DECEMBER 10, 2024

Unified Data Fabric Unified data fabric solutions enable seamless access to data across diverse environments, including multi-cloud and on-premise systems. These solutions break down data silos, making it easier to integrate and analyse data from various sources in real-time.

Analytics 52

Analytics Analytics Augmented Analytics Machine Learning 52

phData

SEPTEMBER 27, 2024

If the event log is your customer’s diary, think of persistent staging as their scrapbook – a place where raw customer data is collected, organized, and kept for future reference. In traditional ETL (Extract, Transform, Load) processes in CDPs, staging areas were often temporary holding pens for data.

Data Models 52

Data Models Data Modeling Apache Kafka Data Lakes 52