If you’re moving data in and out of Kafka, chances are you’re using Apache Kafka Connect. The strength of the Kafka community means we have a large catalog of available connectors for hundreds of different technologies, helping to accelerate the integration of Kafka with 3rd party systems.
While humans are ever-adapting, the recent pandemic has forced a complete revamp of how we work and play. As in-person meetings and conventions remain sparse, networking in other ways has become a new normal. Mobile app developers are applying key lessons and trends of online networking and socializing to capitalize on increasing global demand for virtual connectivity.
Several years ago when I was working on a big data project, I saw something a data engineer shouldn’t see. Curious to understand the level of detail in a new credit score dataset we’d received in our data lake, I queried it. I was surprised at how easily and suddenly my screen was flooded with the mortgage history, overdraft limits and year-end financial statements of my colleagues, and I felt deeply uneasy.
Although the issue of silos in IT and data management are well known, companies appear to be falling back into this trap by not distributing their artificial intelligence (AI) and machine learning (ML) capabilities across their business. New research from Qlik and IDC revealed that just 20 percent of businesses widely distribute these capabilities across the organization.
Migrating a data warehouse from a legacy environment requires a massive upfront investment in resources and time. There is a lot to consider before and during migration. You may need to replan your data model, use a separate platform for tasks scheduling, and handle changes in the application’s database driver. Therefore, organizations must take a strategic approach to streamline the process. This article presents a step-by-step approach for migrating a data warehouse to the cloud.
