Sync Data From JDBC Sources

Namig Aliyev
Namig Aliyev
August 1, 2022
April 24, 2022
4 min read
Sync Data From JDBC Sources


‌This is an end-to-end guide about migrating tables from JDBC sources (MySQL, PostgreSQL, etc.) to iomete and display it in the BI dashboard.

First, you need to establish an SSH tunnel between iomete and your database in your private network. See Database Connection Options

Given database to migrate

‌Let's assume that we want to replicate the MySQL database (or any other supported JDBC database) to the iomete warehouse.

In this tutorial, we will be using a publicly accessible iomete-tutorial database instance that contains the Employees Sample Database.
In case of connecting to your own database instance see Database Connection Options for the details‌.

Here are the details of iomete-tutorial public database:

Port: 3306
Username: tutorial_user
Password: 9tVDVEKp

The database contains the following tables:

Table name                                                      Row count

employees                                                       300024

departments                                                    9

dept_manager                                                 24

dept_emp                                                         331603

titles                                                                 443308

salaries                                                             2844047

Create warehouse

‌Create a new warehouse instance:

Querying  Source Table

After having the warehouse created, we create a table using JDBC Sources using the CREATE TABLE command. In the OPTIONS part we specify credentials of the database to which we want to connect as follows (see JDBC Sources):

CREATE TABLE IF NOT EXISTS employees_proxy USING org.apache.spark.sql.jdbc OPTIONS (
    url "jdbc:mysql://",
    dbtable "employees.employees",
    user 'tutorial_user',
    password '9tVDVEKp'

SELECT * FROM employees_proxy limit 100;
This table doesn't hold the actual data. Data will be retrieved from the actual source once we query the table

Migrating Data

To move the data from the source to the warehouse, you can use one of the following options:

Option 1. Create a table from select

-- Create table directly from the query     
CREATE TABLE employees USING delta       
AS SELECT  * FROM employees_proxy;-- 

To inspect the table use the following query     

Option 2. Insert into to existing table

--just append data
INSERT INTO employees
SELECT  * FROM employees_proxy

--or you can use the follwing command to overwrite data

--first clean an existing data and then insert new data   
SELECT  * FROM employees_proxy

Option 3. Merge with existing data

MERGE INTO employees
USING (SELECT  * FROM employees_proxy) updates
ON employees.emp_no = updates.emp_no

Visualize Data

Let's move employees.salaries before moving to BI visualization:

USING org.apache.spark.sql.jdbc
OPTIONS (  url "jdbc:mysql://", 
dbtable "employees.salaries",  user 'tutorial_user',  password '9tVDVEKp');

CREATE TABLE salaries USING delta   
AS SELECT  * FROM salaries_proxy;

Create a view joining employees and salaries tables:

CREATE OR REPLACE VIEW employee_salaries AS
SELECT e.emp_no, e.first_name, e.last_name, e.gender, s.salary 
FROM employees e 
JOIN salaries s ON e.emp_no = s.emp_no;

Open BI Application

To add a new database connection, choose Databases link from the Data menu and click the +Database button:

Choose Database Type

Here you need to choose Apache Hive from the dropdown:

Replace iomete_username and warehouse_name with your values accordingly:


Add new dataset

To add a new dataset, choose Dataset link from the Data menu and click the + Dataset button:

Create a new chart

Click on the newly created dataset employee_salaries which opens chart view. Let's create a table visualization for the Top 10 High Salary Employees:

Save the chart to a dashboard:

Create another chart. This time Female/Male Salary Distribution using PieChart visualization:

Save this chart to the dashboard too and navigate to the dashboard. And, here is the dashboard of the Employees that we just created:

Congratulations! You did it!

Bonus part

There is a dedicated python library to help to automate this table replication with just a configuration. Please, check out Syncing JDBC Sources.

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