The term “Data Warehouse” is widely used in the data analytics world, however, it’s quite common for people who are new with data analytics to ask the above question.
This post attempts to help explain the definition of a data warehouse, when, and why to consider setting up one.
Ps: This is a section of a guidebook our team is writing, The Analytics Set-up Guidebook. If you are interested to learn more about the high-level or best practices of modern BI stacks, feel free to check out the link to see our progress.
A data warehouse is a type of analytics database that stores and processes your data for the purpose of analytics. Your data warehouse will handle two main functions of your analytics: store your analytical data & process your analytical data. …
You need data analytics. But data analytics is confusing to you.
Data Analytics powers and informs most decision making in organizations, from sales, marketing, partnership to product and engineering. Yet most companies fumble when starting to build their analytics stack.
At Holistics — a self-service BI platform, we’ve been making data analytics tools for over four years, and helped more than a hundred companies build their business intelligence capabilities, sometimes from scratch. …
Update: Confused about the complex analytics landscape? Check out our book: The Analytics Setup Guidebook.
NoSQL databases like MongoDB offer a flexible, scalable and fast data storage for applications. However, the lack of analytical functions and JOIN operation makes it hard to analyze data in NoSQL databases.
In this tutorial, I will walk you through 5 simple steps to not only build a real-time dashboard but also a scalable data analytics stack out of your MongoDB using Holistics: