Data Studio allows you to create interactive reports using data from various sources. And now, it also supports connections to databases. That means you can easily pull data from multiple tables into a single report without writing complex queries.

MongoDB is a NoSQL database that stores documents in JSON format. It was initially designed to store large amounts of unstructured data. In addition to being scalable, it offers high performance and ease of use.

What is MongoDB?

MongoDB is a free and open-source document-oriented database management system. It provides a flexible schema that makes storing documents in JSON format easy. This tutorial explains how you can use MongoDB to store large amounts of unstructured data and make it accessible via web interfaces.

Google Data Studio MongoDB Connection

Google Data Studio makes it easy to analyze large amounts of data across multiple sources. You can combine data from many sources into one dashboard, visualize it in charts and maps, and export reports to PDF, Excel, CSV, and even HTML. But what about connecting to external databases like MongoDB? In this tutorial, I’ll show how to connect Google Data Studio to a MongoDB database using the CData Connect Cloud connector. This lets you import data from MongoDB into Google Data Studio and use it in dashboards and visualizations.

Create Reports from MongoDB Data in Google Data Studio

Google Data Studio authorize you to analyze data from multiple sources in one place. You can use it to build dashboards, visualize trends, and make charts and graphs. This tutorial will show how to create reports from MongoDB data. We’ll start by creating our connection to the database, importing some sample data, building out a dashboard, and exporting the report as a PDF file.

Connect to MongoDB from Connect Server

MongoDB connectors allow you to connect to MongoDB servers without installing additional software. This article explains how to use the MongoDB connector to access MongoDB instances running on Amazon Web Services (AWS).

You can use the MongoDB connector within the Connect Server application to create connections to MongoDB databases. After connecting to the database, you can perform queries and insert data into it.

The MongoDB connector supports automatic discovery of the MongoDB server’s schema. To enable schema discovery, specify the collection name and optionally provide a document type definition file.

To create a connection to a MongoDB instance running on AWS, follow these steps:

1. 2. Select the MongoDB connector.

3. Enter the following information:

4. Click Create.

5. The Connect Server console displays the details of your new connection.

6. Use the MongoDB connector to query or update data in your MongoDB instance.

7. When you’re finished with the connection, click Close.

8. Repeat Steps 1–7 to add more connections to your MongoDB instance.

 

Visualize Live MongoDB Data in Google Data Studio

Google Data Studio makes it easy to explore, analyze, and visualize live data from multiple sources. You can use it to quickly build dashboards, run queries, and create charts. With the help of connectors, you can easily connect to databases like MySQL, SQL Server, PostgreSQL, Oracle, and others. In addition to linking to relational databases, you can connect to MongoDB too.

To learn how to connect to MongoDB, check out our guide here. Once connected, you can start exploring your database’s different views and tables. For example, you can see the number of documents stored in each collection, the total size of each group, and even the total number of records in the entire database.

You can also filter, sort, group, and aggregate your data to find insights into your data. For instance, you could compare the average document sizes across collections to determine whether there is a difference in the amount of data being stored per collection. Or you could count the number of unique values in a field to identify potential problems.

Once you have found some interesting information, you can export it into various formats, including CSV, JSON, Excel, and PDF. This allows you to use many third-party tools to further analyze your data.

For example, you might want to calculate the total revenue generated by each product category over a month. If you do this manually, you must open up every report and add up the totals yourself. But with Google Data Studio, you can automate this process by creating a dashboard that automatically calculates the monthly sales figures for each product category.

Suppose you don’t already have a Google account; head to google.com/signup to sign up for one. Then, log in to your existing Google account to access your Google Data Studio project.

Optional: Connect with the MySQL Connector

The MySQL Connector is used to communicate directly with a MySQL Server. You use it to execute SQL statements against a remote MySQL Server rather than accessing it via ODBC or JDBC.

When connecting to the MySQL Connectors, you must provide a Connection String containing the Host Name, Username, Password, and optionally the Port Number.

SQL Access to MongoDB Data from Applications

MongoDB is one of the most popular open-source document stores. It stores customer information, product catalogs, inventory management, and more. However, it doesn’t have built-in support for relational databases such as SQL. This makes it difficult for developers to access MongoDB data from applications written in languages like Java, Python, PHP, Ruby, etc.

To address this issue, we are releasing a set of drivers that allow developers to connect to MongoDB from their applications. In addition to providing native connectivity to MongoDB, the drivers also provide a unified interface for connecting to multiple different types of databases. For instance, you can now easily connect to a MongoDB database within your Java application via JDBC.

The drivers are based on our MongoDB Driver API and are compatible with both the 2.6 and 3.0 versions of MongoDB. They work seamlessly with existing codebases without requiring any changes. We have tested the drivers extensively across several platforms and operating systems.