Learn how to install and configure MariaDB ColumnStore, how to deal data visualization and Tableau integration, and how to use the window function.
Looking for an easy way to get started with analytics? MariaDB ColumnStore provides a simple, open, and scalable analytics solution. It leverages a pluggable storage engine to handle analytic workloads while keeping the same ANSI SQL interface that is used across the MariaDB portfolio. This blog provides a quick five-step tutorial to help you get started with MariaDB ColumnStore.
Before you begin, please download the sample dataset, including:
In this step, you will learn how to download and install MariaDB ColumnStore.
MariaDB ColumnStore does not require you to set up index and partitioning. It provides an easy way to create a table and load data without help from DBAs. In addition, when ColumnStore loads data, it uses cpimport, which leverages parallel query loading capability. To learn more about cpimport, watch this presentation by our solutions engineer, Anders Karlsson.
Leveraging the MariaDB Server interface, we can use “Dimension Tables” from the InnoDB storage engine and join those with the “Fact Table” data in ColumnStore. In this demo, we join a loan stats fact table and dimension table to create a sample quarterly report on the loan amount.
Another benefit of ColumnStore is built-in analytics queries like window functions. Without writing complex code, users can run window functions in SQL to run time series analysis or run averages on a certain dataset. In this example, with one SQL query, you can report on the top ranked delinquent loan amounts in five specific states.
ColumnStore provides an easy way to connect to third-party BI tools like Tableau using a generic ODBC driver, enabling you to better visualize your data.
Hope you enjoyed the tutorial! Here are some additional resources to help you along the way:
Cloud: Deploy MariaDB ColumnStore with Amazon AMI
Container: Deploy MariaDB ColumnStore on Docker or Vagrant
MariaDB ColumnStore Use Case: Visual analysis of health data by the Institute for Health Metrics and Evaluation (IHME)
