Data hygiene in ETL and reverse ETL
Good data hygiene means data is correct and easily used to draw insight. This definition then begs the question: How do you achieve it?
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Analytics
How to develop a flexible low-code ETL process
Retrieving data from a source, ensuring it suits business requirements, and moving that data into a target data source is critical to any data strategy. Low-code tools can help create robust and flexible ETL processes that automate your data loading.
Built with BigQuery: Solving scale and complexity in retail pricing with BigQuery, Looker, Analytics Hub and more
Building a data-driven pricing platform for speed, scale and automation with BigQuery, Looker and more.
Hearing The Voice Of Your Customer
Trans World Airlines (TWA). Pan American Airlines. Sports Authority, Staples, Woolworth's, and Banana Republic. Aside from some minor similarities, these companies are all very diverse.
Friday the 13th Fun Facts - Did you know?
Using Qlik Cloud and Qlik Sense - I can quickly explore my data with a few clicks and uncover findings I wasn't aware of by simply looking at the associative relationships in my data.
Pentaho Community Edition or Enterprise - which is right for you
This video compares Pentaho Community Edition (CE) vs Pentaho Enterprise Edition (EE).
How to practice responsible AI - Scott Zoldi
This episode features an interview with Scott Zoldi. He is the Chief Analytics Officer at FICO where he is responsible for the analytic development of FICO’s product and technology solutions. Scott is involved in developing new analytic products and applications, and has authored more than 100 patents. His current focus is on self-learning analytics to detect cyber security attacks. On this episode, Scott talks about how to attract and retain world-class data scientists, the importance of following a model governance process, and responsible AI.
A Simplified Guide to Cloud Data Platform Architecture
Since the 2006 launch of Amazon Web Services (AWS), the world’s first hyper-scale public cloud provider, thousands of data-driven businesses have shifted on-premise data storage and analytics workloads into the cloud by architecting or adopting a cloud data platform. As the volume, variety, and velocity of enterprise data continues to grow in 2023, cloud data platforms with legacy tech and complex architectures are becoming increasingly time-consuming and costly to manage.
Building a scalable data stack: Three lessons from Backcountry
Backcountry, the specialty retailer of premium outdoor gear and apparel, shares key lessons on using a modern data stack to overcome data silos, complexities with legacy systems and improve its customer experience.
Top 3 data visualizations for finance professionals
Data plays a profound role in finance. In fact, some might argue that finance professionals are some of the most data-driven individuals in an organization. That’s because finance data, and the insights you draw from it, can literally make or break a company. This is especially true in times of economic uncertainty, when businesses are trying to make data-driven decisions about where to invest and cut resource allocation.