Systems | Development | Analytics | API | Testing

Data Warehouses

integrate

Fivetran vs. Matillion vs. Integrate.io: A Comprehensive Comparison

In today's increasingly digital world, businesses of all sizes rely on data to make informed decisions and drive growth. This is why more and more organizations have started using data warehouse platforms. These crucial tools help businesses store, manage, and analyze data in one central location. In addition, a data warehouse platform makes accessing and processing large amounts of data easier, enabling businesses to gain valuable insights and improve their operations.

hevo

Data Warehouse Best Practices: 6 Factors to Consider in 2023

Data warehousing is the process of collating data from multiple sources in an organization and store it in one place for further analysis, reporting and business decision making. Typically, organizations will have a transactional database that contains information on all day to day activities. Organizations will also have other data sources – third party or internal operations related. Data from all these sources are collated and stored in a data warehouse through an ELT or ETL process.

integrate

From Data Warehouse to Lakehouse

This is a guest post for Integrate.io written by Bill Inmon, an American computer scientist recognized as the "father of the data warehouse." Inmon wrote the first book and first magazine column about data warehousing, held the first conference about this topic, and was the first person to teach data warehousing classes.

integrate

Data Warehousing and Data Mesh: Different Types of Goals

The world is full of different types of goals. Consider football. The goal in football is at the end of the field. A runner either crosses the goal or they don't, when trying to make a touchdown. Or, consider basketball. In basketball, when a player shoots the ball, the player’s shot either goes through the net or it doesn’t. Alternatively, consider ice hockey. When a hockey player shoots the puck, it either goes into the net — or it doesn’t.

thoughtspot

Data modeling techniques for data warehousing

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.