This blogpost will look into the evolution that AI has made in order to become what it is today. Next to that we will look forward, how AI can likely impact our future. Lastly, we will try to formulate a plan of attack that companies need to consider to be best prepared for the drastic changes that AI will have on our way of working.
Great Expectations, an open-source Python library, provides an excellent framework to kickstart your Data Quality by Design projects, creating visibility for data quality issues, and triggering calls to action for everyone involved.
AWS Glue, the serverless ETL service of AWS, supports two types of jobs: Spark and Python shell. In this article, we'll focus on Python shell jobs and explain how you can make optimal use of your S3 Data Lake using Athena within Python shell jobs.
Data Governance is a collaborative process, and its implementation hinges on user adoption. Fortunately, much can be done to boost Data Governance user adoption. Check out our 3 priceless tips.
Can semantic data discovery deliver on its promise to provide a better understanding of your data and facilitate the automation of your business processes? We know it can. Learn from our experience to understand how it works and what you can achieve.
Chances are you have recently heard a lot about data mesh, a decentralized approach to sharing, accessing, and managing analytical data. So, let's dive into a practical example to help you understand what a data mesh stands for.
Soda is a new kid on the block when it comes to data quality. It excels in making it easy for business users to monitor the health of their data, enabling them to define data quality rules without a high level of technical knowledge.
Data lakehouses are the talk of the town when it comes to data architecture. But why is that? And why is that happening right now? Let's take a refreshing dive into the history of data warehouses, data lakes, and data lakehouses.
Before you can generate insights from your data, you need to move those data from an operational to an analytical environment - a process commonly referred to as data ingestion. An event-driven architecture provides an elegant way to achieve a process marked by timeliness, performance, and cost-effectiveness.
