Analytics

Implementing MLOps: 5 Key Steps for Successfully Managing ML Projects

MLOps accelerates the ML model deployment process to make it more efficient and scalable. This is done through automation and additional techniques that help streamline the process. Looking to improve your MLOps knowledge and processes? You’ve come to the right place. In this blog post, we detail the steps you need to take to build and run a successful MLOps pipeline.

Visualize Your Spreadsheet Data with Databox!

How to visualize your spreadsheet data with Databox? From numbers in a spreadsheet to visual metrics and spreadsheets in minutes, thanks to the Metric Builder function, that lets you integrate your spreadsheets with Databox. And if your data in structured in a non-conventional way (data in rows instead of in columns, data on different sheets, or when row numbers don't line up) we have the Manual Setup, allowing you to easily select the right cells to visualize your data - without using any code at all!

How Yellowfin Self Service Analytics Helps Automate Data Insights

For our regular readers at Yellowfin, the role of self-service analytics in today’s organization is more than clear. This type of analytics is aimed at helping more people across the company access, analyze and understand their business data, so they can make data-led decisions.

Data warehouse modernization: Diving deeper into Qlik Talend data integration and quality scenarios

Step right up, ladies and gentlemen, and witness the grand spectacle of the digital age! In a world where data is king, where information reigns supreme, and cloud data warehouses are multiplying like rabbits, there's a technology initiative like no other— data warehouse modernization! This article is the second in the series "Seven Data Integration and Quality Scenarios for Qlik and Talend," and answers everything you wanted to know about data warehouse modernization but were afraid to ask.

How to Monitor and Debug Your Data Pipeline

Picture this: during a bustling holiday season, a global e-commerce giant faces a sudden influx of online orders from customers worldwide. As the company's data pipelines navigate a labyrinth of interconnected systems, ensuring the seamless flow of information for timely product deliveries becomes paramount. However, a critical error lurking within their data pipeline goes undetected, causing delays, dissatisfied customers, and significant financial losses.

Kensu Brings Data Observability to Data Engineers

What can an organization do to troubleshoot flawed data sets before they get into the hands of end-users? In this episode of “Powered by Snowflake,” host Daniel Myers explores that topic with Andy Petrella, Founder and CPO of Kensu, which offers a data observability platform built specifically for data engineers. The conversation includes a demo of the platform that spotlights how it enables data engineers to proactively identify data problems before the data gets to stakeholders.