Systems | Development | Analytics | API | Testing

When setting up a modern data stack, data warehouse modeling is often the very first step. It is important to create an architecture that supports the data models that you wish to build. I often see people going straight to writing complex transformations before thinking about how they want to organize the databases, schemas, and tables within their warehouse. To succeed, it is key to design your data warehouse with your models in mind before starting the modeling process.

Effective control and governance over your data assets is vital for long-term business success. By keeping your data available, reliable and usable for analysis, consumption and sharing, you can ensure data quality, data security and data reliability is consistently met. However, many organizations today struggle to implement governance frameworks over their data, which has once again highlighted the importance of data governance. So, what is data governance?

The best ETL tools help you extract, transform, and load data from multiple data sources to the data destination of your choice. But not all ETL tool providers are created equal. Some are built for high-performance big data analytics, while others offer more intuitive and cost-effective workflows for the non-technical user.

When I first became an industry analyst, I was fascinated with vendor focus on key capability sets and engineering. The market shifted between acquisition cycles, new start ups, and development cycles for a go-to market strategy based on differentiated tools and capability sets. All of this jockeying for position based on feature sets created strong technology stacks that paved the way for a platform approach and eventual modernization and the transition to cloud.

Just one pre-written line of code for each data source is all that is needed to orchestrate automated data ingestion effectively.