Bimotics views on Cloud Data Warehouse, Big Data, Business Intelligence, Analytics, Google Cloud Platform and Google BigQuery.
Monday, March 7, 2016
Building an Online Data Warehouse Part 2
Google Cloud Platform is a great option for companies looking to build a Data Warehouse on the cloud. As described in Part 1, you have two options: hire a developer to code and integrate on top of those great APIs, or use tools like marvin. to help you automate some of the processes that are required to build an online data warehouse. In Part 2 and 3, we will discuss the most valuable sections of a Data Warehouse, Analyzing and Visualizing your Data.
Analyzing the Data
marvin.’s super powers come from Google BigQuery API, an analytical database on the cloud. marvin. automates the process of inserting and storing data uploaded in the previous step. Together, Google BigQuery and marvin. are the analytical engine for your data warehouse. Your new Data Warehouse is limitless in terms of size and analytical computing power. Even better, there is nothing to code, no servers to manage, and no hard drives to setup. To learn more check out our blog: Google BigQuery: 7 fascinating facts.
With marvin., you create datasets, make tables that host data, and preview that data in a table format. The data is no longer raw. Instead, your precious data is stored like in a huge spreadsheet that can be analyzed very quickly. Think billions of records in seconds. Think Big Data. Think cloud bi.
What if your business only has a few thousand records? No worries. marvin. will manage small data just as well. After all, a small business does not need to analyze a billion records of its data to make informed decisions and leverage insight to compete with other businesses.
Your new analytical engine also allows users to interact with simple SQL statements. If your team does not know SQL, it is also compatible with multiple visualization engines like Tableau and BIME, popular programs that present your data graphically. Visualizing the data is the last piece of the 4 steps of the simplified process. We will explore data visualization in Part 3.