Let’s start with a real-world example from one of my past machine learning (ML) projects: We were building a customer churn model. “We urgently need an additional feature related to sentiment analysis of the customer support calls.” Creating the data pipeline to extract this dataset took about 4 months! Preparing, building, and scaling the Spark MLlib code took about 1.5-2 months!

Customers interact with your business multiple times before reaching any goal. These repeated digital interactions are what make up the customer journey. Your customers’ overall experience across the different channels as they engage with your organization (websites, social media, email, etc.) make up the customer experience. Customer journey analytics refers to the process of analyzing the experience of customers across multiple touchpoints in the customer journey.

Many technologies of the last century are out of date now, but flat file databases are still very much in use today and likely will be for a long while yet. They’ve stood the test of time for over four decades and are still going strong for a variety of reasons.

A new Python package from Fivetran and Astronomer enables connector management in Airflow.

Today’s enterprise data analytics teams are constantly looking to get the best out of their platforms. Storage plays one of the most important roles in the data platforms strategy, it provides the basis for all compute engines and applications to be built on top of it. Businesses are also looking to move to a scale-out storage model that provides dense storages along with reliability, scalability, and performance.