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.

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.

Five things to know about how to get data from multiple sources: These days, organizations have more data at their fingertips than ever before and collect an incredible number of data sets from various sources. This creates a paradox for businesses such as e-commerce retailers struggling to deal with data complexity. With a deluge of information (and more arriving every day), how can you get data from multiple sources efficiently and unlock the hidden insights that it contains?

8 years ago, when I founded Iguazio together with my co-founders Yaron Haviv, Yaron Segev & Orit Nissan-Messing, I never thought I would be making this announcement on our company blog: McKinsey acquired Iguazio! When we first embarked on this journey, we realized that while AI has the ability to transform any industry - from banking to retail to manufacturing - in reality most data science projects fail.

Many organizations focus on the data engineering or development qualifications they require to connect specific data sources and manage data projects. But that is only half of what is needed. Soft skills are so important and sometimes overlooked. Soft skills support data management success because they help individuals effectively communicate and collaborate with others, understand and anticipate the needs of stakeholders, and make data-driven decisions.

5 important differences when comparing MongoDB vs. Redis.

“Data pipeline” and “Extract, Transform, Load” (ETL) are common phrases encountered in just about every data integration. But what’s the difference?