When people in analytics talk about data modelling, they usually dive right into the technical aspects. Into the pros and cons of data vault vs star schema modelling, for example. Providing a broad definition can provide some context to avoid getting stuck in the details.
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Introduction In today’s data-driven world, businesses are constantly seeking ways to leverage the power of data for insights and decision-making. One popular approach is building a dashboard, a visual representation of key metrics and data points that provides a quick…
Data Build Tool (dbt) is a command line tool that helps data analysts and engineers transform, analyze, and visualize data in their warehouse. It is designed to work with popular warehouses such as Snowflake, Redshift, and BigQuery, and integrates with…
Snowflake is a cloud-based data warehouse that is designed to handle the increasing volume and complexity of modern data. It is a fully managed service that allows you to store, analyze, and query your data using SQL, without the need…
Most businesses today are data-driven, and to make sound decisions, they need access to accurate and timely data. However, setting up and maintaining a data warehouse can be costly and time-consuming. Apache Superset is an open-source tool that makes it…
Building a data warehouse can be a costly endeavor, with several factors impacting the final price tag. Factors such as the size and complexity of the data, the tools and technologies used, the number of users, and the level of…
In today’s data-driven world, companies are constantly on the lookout for new and innovative ways to extract value from the vast amounts of data they generate and collect. To meet this demand, there are three key approaches that have emerged…
When it comes to designing a data warehouse, there are two main schema models that are commonly used: the star schema and the snowflake schema. Both of these models have their own unique characteristics and benefits, but they also have…
As a data engineer, you know that the field is full of challenges. From data quality to data transformation, there are always issues to be addressed and problems to be solved. In this post, we’ll take a look at some…