Systems | Development | Analytics | API | Testing

With inflation and other disruptive market dynamics massively impacting consumer behavior, is it any surprise that personalization tops the list of strategic actions for CMOs in 2023? Yep, people tend to stick around when digital products and experiences fulfill their personal needs quickly and accurately. And topping the list of powerful tools for personalization? Machine learning and AI, of course, from product recommendations to targeted offers based on digital customer and behavioral data.

A new Fivetran Airflow provider developed by Astronomer allows data engineers to run Fivetran data syncs more efficiently in Airflow 2.2+

We’ve established that we’re living in the defining decade of data. Data underpins the seismic technology shifts of the past few years, transforming the way we buy, work, make business decisions, even value our companies. As ThoughtSpot’s co-founder Ajeet Singh said, “Once in a generation, the opportunities to create a legacy increase massively. It happens when truly tectonic shifts happen in the ecosystem. We’re living through one of those times.”

According to the U.S. Small Business Administration’s Office of Advocacy, small businesses account for approximately 99.9% of all businesses. That’s a massive chunk of the U.S. economy. While they are high in number, the issues they face are relatively higher, too. Nearly 35% of small business owners report ‌that they aren’t generating any profits, with inflation being the biggest of their worries.

This Eckerson Group report gives you a good understanding of how the Unravel platform addresses multiple categories of data observability—application/pipeline performance, cluster/platform performance, data quality, and, most significant, FinOps cost governance—with automation and AI-driven recommendations.

Analytics engineer is the latest role that combines the technical skills of a data engineer with the business knowledge of a data analyst. They are typically coding in SQL, building dbt data models, and automating data pipelines. You could say they own the steps between data ingestion and orchestration. Whether you are a seasoned analytics engineer or new to the field, it’s important to continually learn new things and improve the work you’ve already done.