The Definitive Guide to Cloud Database Integration: Connecting Spreadsheets to PostgreSQL, MariaDB, and MySQL (2025/2026 Model)

Spreadsheets, such as Google Sheets and Microsoft Excel, are effective organizational tools for initial data capture, particularly suitable for small-scale businesses managing modest datasets with simple relationships. However, as data volume increases, complexity grows, and the number of concurrent users expands, relying solely on spreadsheets rapidly introduces inefficiency, risks version control issues, and complicates audit trails.

Migrating to a Cloud-Hosted Database (DBaaS) provides a superior platform for accessing, managing, and organizing complex, growing datasets. This transition ensures scalability, enhanced security, and superior data integrity.

This authoritative guide provides a detailed, step-by-step methodology for connecting popular spreadsheet platforms—Microsoft Excel and Google Sheets—to cloud-hosted instances of MariaDB, MySQL, and PostgreSQL to establish a robust data management framework.

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Prerequisites and Essential Integration Tools

To successfully execute the integration steps outlined in this tutorial, ensure the following tools and assets are prepared:

• A populated Google Sheet (referencing a sample sheet is recommended for consistency).
• Coefficient: A no-code connector extension installed within Google Sheets for data export/import.
• Devart Add-in: An external plugin installed within your Microsoft Excel workbook for direct connectivity to SQL databases.
• Graphical User Interfaces (GUIs): pgAdmin4 (for PostgreSQL) and MySQL Workbench (for MariaDB/MySQL) are required for server management and data verification.
• A Cloud-Hosted Database Instance: Access to live, external connection credentials for a MariaDB, MySQL, or PostgreSQL instance (setup via a DBaaS provider is assumed).

The Strategic Value of Cloud Database Integration

Cloud-hosted databases operate on the Database as a Service (DBaaS) model, which offloads the complexity of hardware maintenance, configuration, and infrastructure management from the organization. Key RDBMS choices for this migration include:

• PostgreSQL: An open-source, object-relational database renowned for its robust feature set, high reliability, and exceptional extensibility, making it the industry standard for large, scalable applications.
• MySQL: A highly popular, widely adopted open-source RDBMS known for its scalability, flexibility, and reliability in powering both SQL and NoSQL applications at economical costs.
• MariaDB: A community-developed fork of MySQL, known for being highly compatible with MySQL yet often offering improved scalability and query speed, making it suitable for performance-critical tasks.

Beyond operational convenience, DBaaS platforms guarantee business continuity through automatic backups, integrated version control, and robust disaster recovery mechanisms. Additional advantages include superior security, inherent scalability, operational flexibility, and improved business agility.

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Preparing and Structuring Spreadsheet Data for Migration

Raw spreadsheet data often contains errors, noise, and formatting inconsistencies that can severely compromise data quality during migration. Thorough Data Hygiene is non-negotiable before integration.

Data Preparation Best Practices

• Formatting & Visualization: Modify complex sheets into organized, multiple related sheets. Utilize sorting and conditional formatting to ease visualization and readability.
• Data Cleaning: Remove outliers, duplicate records, and unwanted special characters. Split complex single-text columns into multiple columns to avoid parsing errors.
• Hide Redundant Data: Use spreadsheet features to hide data points that are not immediately relevant but may be needed for future analysis.

Structural Requirements for Database Integration

For a successful SQL database import, the following structural guidelines must be enforced:

• Null vs. Zero Values: Clearly differentiate between a `NULL` value (meaning "no value") and a `0` (zero) value (meaning "a quantity of zero"). Databases strictly enforce this distinction, and misinterpretation can cause constraint errors.
• Field Naming Convention: Avoid special characters, spaces, and Unicode characters in column names. Best practices involve using `snake_case` (e.g., `student_name`) or `camelCase` (e.g., `studentName`).
• Metadata Recording: Documenting the original data structure and origin is critical for accurate field mapping during the transfer process.

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Integrating Google Sheets and Excel with MariaDB/MySQL

MariaDB is a fork of MySQL, meaning that MySQL-compatible tools (like MySQL Workbench and most connectors) are used for interaction. We begin by setting up the database table and then proceeding with the connectivity.

1. Server Setup and Table Creation

First, obtain the external connection credentials (Hostname, Port, Username, Password) from your cloud-hosted MariaDB instance.

Figure 1: Retrieving essential external connection details for the MariaDB instance.

Use MySQL Workbench to connect to and manage the MariaDB instance by creating a new connection profile using these details.

Figure 2: Configuring a new connection profile within MySQL Workbench using the MariaDB credentials.

Once connected, execute a DDL (Data Definition Language) query to create the destination table, ensuring that the column names and data types accurately reflect your spreadsheet data (e.g., `diabetes_table`):

CREATE TABLE `diabetes_table` (
    `id` int(11) NOT NULL AUTO_INCREMENT,
    `Pregnancies` varchar(45) NOT NULL,
    `Glucose` int(11) NOT NULL,
    `BloodPressure` int(11) NOT NULL,
    `BMI` decimal(3,1) NOT NULL,
    `DiabetesPedigreeFunction` decimal(4,3) NOT NULL,
    `Age` int(11) NOT NULL,
    `Outcome` tinyint(4) NOT NULL,
    PRIMARY KEY (`id`),
    UNIQUE KEY `id_UNIQUE` (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=13 DEFAULT CHARSET=utf8mb3

2. Google Sheets to MariaDB via Coefficient

Use the Coefficient Google Sheets extension to establish the export pipeline.

1. Open your target Google Sheet (e.g., the `diabetes.csv` file).

Figure 3: Sample data loaded in Google Sheets ready for export.

1. Launch the Coefficient sidebar from the Extensions menu.

Figure 4: Accessing the Extensions menu in Google Sheets.

Figure 5: Launching the Coefficient connector from the Extensions menu.

1. In Coefficient, select Export to and choose MySQL (MariaDB is compatible). Enter the cloud connection details and click Connect.

Figure 6: Entering MariaDB connection details into the Coefficient connector.

1. Configure the data source (Tab and Header row) and the destination (Table and Action set to `Insert`).

Figure 7: Defining the Source Data tab and header row.

Figure 8: Specifying the Destination Table and the action (Insert).

1. Map the spreadsheet columns to the MariaDB table headings in the Schemas panel and save the mapping.

Figure 9: The Schemas panel displays the spreadsheet columns to be mapped.

Figure 10: Mapping spreadsheet fields to MariaDB table headings.

1. Select the specific rows to export and execute the insert action.

Figure 11: Selecting specific rows for testing the export process.

1. Upon successful export, the spreadsheet will display a Record ID, Result (OK), and Timestamp column.

Figure 12: Confirmation of successful row export to the database.

1. Verify the imported data in MySQL Workbench using a `SELECT` query:

SELECT * FROM <your_db_name>.diabetes_table;

Figure 13: Data verification in the MariaDB table via SQL query.

3. Excel to MariaDB via Devart Plugin

The Devart plugin for Excel enables bidirectional synchronization, allowing you to import database data, edit it locally in Excel, and commit changes back to the cloud database.

1. Open Excel and navigate to the Devart tab (available after plugin installation).

Figure 14: Location of the Devart tab in the Excel ribbon.

1. Click Get Data to launch the Import Data Wizard.

Figure 15: The Get Data button initiates the import process.

1. Select MySQL database as the Data Source and enter your MariaDB connection details. Test the connection, then proceed.

Figure 16: Providing connection details within the Devart Import Data Wizard.

1. Use the Visual Query Builder or a custom SQL query (e.g., `SELECT * FROM diabetes_table;`) to specify the data to be imported.

Figure 17: Specifying data import via a custom SQL query.

1. Click Finish. The Excel sheet will now be populated with live data from the cloud database.

Figure 18: Excel sheet successfully populated with data retrieved from the MariaDB cloud instance.

1. To enable synchronization, click Edit Mode. Add new records or modify existing ones locally.

Figure 19: Entering Edit Mode to enable local data modification.

1. Highlight the new records to be inserted.

Figure 20: Highlighting new records in Excel to be committed to the database.

1. Click Commit to send the changes back to the MariaDB database.
2. Verify the inserted records using MySQL Workbench.

Figure 21: Verification of new records inserted into MariaDB via Excel's Commit function.

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Integrating Google Sheets and Excel with PostgreSQL

PostgreSQL requires precise setup, including sequences for auto-incrementing primary keys, and is typically managed using the pgAdmin4 GUI.

1. PostgreSQL Setup and Table Creation

Use your cloud-hosted PostgreSQL connection details to register a new server within pgAdmin4.

Figure 22: Providing connection details in the pgAdmin4 Register - Server dialog box.

Execute the following SQL commands to create a necessary sequence for the primary key and then create the destination table (`diabetes_table`), ensuring data types are strictly matched to the spreadsheet content.

CREATE SEQUENCE IF NOT EXISTS public.diabetes_table_id_seq
    INCREMENT 1
    START 1
    MINVALUE 1
    MAXVALUE 2147483647
    CACHE 1
    OWNED BY diabetes_table.id;

CREATE TABLE IF NOT EXISTS public.diabetes_table
(
    "Pregnancies" smallint NOT NULL,
    "BloodPressure" smallint NOT NULL,
    "BMI" numeric(3,1) NOT NULL,
    "Glucose" smallint NOT NULL,
    "DiabetesPedigree" numeric(4,3) NOT NULL,
    "Age" smallint NOT NULL,
    "Outcome" boolean,
    id integer NOT NULL DEFAULT nextval('diabetes_table_id_seq'::regclass),
    CONSTRAINT diabetes_table_pkey PRIMARY KEY (id)
)
WITH(
    OIDS = FALSE
)
TABLESPACE pg_default;

2. Google Sheets to PostgreSQL via Coefficient

The process mirrors the MariaDB integration, but you select PostgreSQL within the Coefficient connector:

1. Launch the Coefficient sidebar, click Export to, and select PostgreSQL.
2. Enter your PostgreSQL connection details and click Connect.

Figure 23: Connecting to PostgreSQL via Coefficient using specific connection details.

1. Define the Source Data (Tab and Header row).

Figure 24: Defining Source Data for the PostgreSQL export.

1. In the Destination section, select the newly created table (e.g., `public.diabetes_table`) and the Insert action.

Figure 25: Specifying the PostgreSQL destination table and the Insert action.

1. Map the spreadsheet columns to the PostgreSQL table columns, select the rows to insert, and execute the export.
2. Verify the imported data using the query: `SELECT * FROM diabetes_table;` in pgAdmin4.

3. Excel to PostgreSQL via Devart Plugin

Use the Devart plugin to establish a direct link from Excel to PostgreSQL:

1. In Excel, click the Devart tab and then Get Data.
2. In the Import Data Wizard, select PostgreSQL database as the Data Source and input the credentials. Test the connection.

Figure 26: Providing PostgreSQL connection details in the Devart Import Data Wizard.

1. Use the Visual Query Builder or a custom query to import data from your PostgreSQL table.

Figure 27: Using the Visual Query Builder to define the data import scope.

1. Click Finish. The Excel sheet will populate. Use the Refresh button to pull the latest data.

Figure 28: Using the Refresh button to ensure the Excel data is up-to-date with the database.

1. Click Edit Mode, add new records, and use the Commit button to synchronize the changes back to the cloud PostgreSQL database.

Figure 29: Using the Edit Mode and Commit buttons to synchronize local changes.

1. Verify the new record insertion using pgAdmin4.

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Summary: The Future of Cloud Data Management

Transitioning from decentralized spreadsheets to a centralized cloud-hosted RDBMS (PostgreSQL, MariaDB, or MySQL) fundamentally improves data governance. These databases provide a robust platform for establishing dynamic relationships, ensuring data integrity, and enabling collaborative, scalable management.

By leveraging cloud-hosting providers and specialized connectors like Coefficient and Devart, organizations can efficiently connect popular tools like Google Sheets and Excel to cloud databases, map fields, and execute bidirectional data synchronization. This methodology is the crucial first step in building a resilient and scalable data infrastructure.

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Frequently Asked Questions (FAQ) on Spreadsheet to Database Migration

Q: Why is data cleaning (data hygiene) essential before migration?

A: Data cleaning is critical because RDBMS platforms enforce strict data types and constraints (e.g., NOT NULL, uniqueness). Errors like duplicate entries, non-numeric values in numeric fields, or mislabeled nulls will cause the entire import operation to fail or result in corrupted data, wasting significant time and resources.

Q: What is the main benefit of using a graphical interface (pgAdmin4 or Workbench) vs. command line tools?

A: GUIs simplify complex administrative tasks like server configuration, user management, and visually verifying data integrity after migration. While command line tools offer fine-grained control, GUIs significantly reduce the learning curve and time required for routine database management and data inspection.

Q: Why does PostgreSQL require a sequence for the primary key ID?

A: Unlike MySQL/MariaDB, which often handles auto-increment implicitly, PostgreSQL traditionally uses a separate object called a sequence to generate unique integer identifiers. Although `SERIAL` or `GENERATED ALWAYS AS IDENTITY` simplify this, manually creating the sequence and assigning it via `nextval()` (as shown) provides full control and is necessary when manually defining the primary key.