Integrate data from SQLite to Databricks using Matillion

Creating a User Identity in SQLite

Unlike many full-featured database management systems, SQLite does not have built-in support for managing user accounts or authentication at the database engine level. However, you can implement a user concept within your database by creating a

users

1. Define a Users Table

Begin by creating a table to hold user information. It's common to include columns for a unique identifier, username, hashed password, and optionally other metadata.

CREATE TABLE users (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    username TEXT NOT NULL UNIQUE,
    password_hash TEXT NOT NULL,
    email TEXT,
    created_at DATETIME DEFAULT CURRENT_TIMESTAMP
);

• id

  : An auto-incrementing primary key.

• username

  : The user's unique login name.

• password_hash

  : Store a hash of the password, never store plain text passwords.

• email

  : User's contact email (optional).

• created_at

  : Timestamp of record creation.

2. Insert a New User

To add a user, insert a row into the

users

INSERT INTO users (username, password_hash, email) 
VALUES ('alice', 'e3c123fa97328e...', '[email protected]');

Note: Use a secure hashing algorithm in your application (e.g., bcrypt, Argon2, PBKDF2). SQLite itself does not natively hash passwords; hashing must occur in your application code before executing the

INSERT

.

3. Example: Inserting a User Using SQLite’s

sqlite3

CLI

bash
sqlite3 mydatabase.db

Within the CLI:

``
-- Assuming you have already created the

INSERT INTO users (username, password_hash, email) VALUES ('bob', 'b8cb10a8adc199...', '[email protected]'); ```

4. Retrieving User Information (Example)

You can query the database to retrieve user data by username:

SELECT id, username, email, created_at
FROM users
WHERE username = 'alice';

This approach implements user identities at the application level using conventional SQL within SQLite. For any authentication or user management features, your application code must handle password verification and user authentication logic.

Installing the JDBC driver

At the time of writing, the JDBC driver for SQLite is not bundled by default with Matillion Data Productivity Cloud due to licensing or redistribution restrictions. Consequently, users who wish to connect to SQLite databases must manually download and install the JDBC driver prior to use.

Follow the steps below to acquire, install, and configure the JDBC driver within Matillion Data Productivity Cloud:

1. Downloading the JDBC Driver

You will need a Type 4 JDBC driver to ensure full compatibility and performance. The most widely adopted Type 4 driver for SQLite is available from the xerial project. Please visit the official repository at https://github.com/xerial/sqlite-jdbc to obtain the latest release of the driver (typically in the form of a

.jar

Make certain to download the appropriate version for your environment and review any accompanying notes regarding supported JDBC features and driver compatibility.

2. Installing the JDBC Driver in Matillion Data Productivity Cloud

Once you have downloaded the JDBC driver

.jar

Review these instructions thoroughly and follow the documented process for uploading the SQLite JDBC driver to your Matillion Agent or environment. Typically, this will involve:

• Navigating to the designated area of the Matillion Agent user interface or file system
• Uploading the JDBC driver

  .jar

  file
• Restarting relevant Matillion services to acknowledge and load the new driver, if required

Refer directly to the linked documentation for information on supported file formats, location requirements, and troubleshooting during driver installation.

3. Using the Driver in Matillion

After installation, guidance on connecting to and querying SQLite databases through Matillion can be found at: https://docs.matillion.com/data-productivity-cloud/designer/docs/database-query/. This documentation explains how to configure connections using the uploaded JDBC driver, including necessary parameters for establishing connections to SQLite data sources.

Be sure to follow the steps and connection properties outlined in these Matillion usage instructions to ensure reliable connectivity and optimal performance when working with SQLite databases via the JDBC interface.

Checking network connectivity

To connect Matillion Data Productivity Cloud to your SQLite database, you must ensure that your database accepts incoming network connections from the appropriate sources, depending on your deployment configuration:

• Full SaaS agent configuration:
  Allow incoming connections from all IP addresses listed in the official Matillion documentation: https://docs.matillion.com/data-productivity-cloud/security/docs/allowing-ip-addresses/.

• Hybrid SaaS deployment:
  Allow incoming connections from your own virtual private cloud (VPC) where your Hybrid SaaS agent is hosted. Matillion provides utilities and guidance to check your network configuration at https://exchange.matillion.com/data-productivity-cloud/pipeline/check-network-access/.

If your SQLite database is referenced using a DNS hostname, ensure that the Full SaaS or Hybrid SaaS agent is able to resolve the DNS address correctly. This may require that DNS records are publicly accessible or that any required internal DNS configuration is in place.

Querying Data from a SQLite Database

This guide explains how to query data from a SQLite database, demonstrates using SQL

SELECT

1. Querying with SQL SELECT Statements

You use the

SELECT

Select all rows and all columns from a table called

customers

SELECT * FROM customers;

Select specific columns (

id

name

email

customers

SELECT id, name, email FROM customers;

Add filtering criteria using a

WHERE

SELECT id, name, email 
FROM customers 
WHERE signup_date >= '2024-01-01';

Order results and limit the number of rows:

SELECT id, name, email 
FROM customers 
ORDER BY signup_date DESC 
LIMIT 100;

2. Datatype Conversion: SQLite to Databricks

When transferring data from SQLite into Databricks, be aware that datatype conversion may occur due to differences in datatype systems:

• Conversion may affect data precision and integrity.
• It is recommended to review the Databricks documentation for details on data type mapping.

3. Initial Load vs. Incremental Load

A robust data loading pattern uses an initial full load followed by recurring incremental loads. In both cases, the same Database Query component should be used.

Initial Load

Pattern:
- No filter applied in your SQL query—retrieve the full dataset.

Example:

SELECT * FROM customers;

Incremental Load

Pattern:
- Apply a filter clause to select only new or updated rows since the last load. - Filters often use date columns, incremental numeric IDs, or

updated_at

Example (using a

last_load_timestamp

SELECT * FROM customers
WHERE updated_at > '2024-06-01 00:00:00';

Read more: See details on incremental loading strategies at Matillion Exchange.

Tip: Use variables or workflow orchestration in your data integration pipeline to pass the correct parameter (e.g.,

last_load_timestamp

Data Integration Architecture

Loading data into the target Databricks database before integration is an effective way to divide and conquer the data integration challenge by splitting it into two distinct steps: loading (extract and load) and integrating (transform). This approach, characteristic of the Extract, Load, Transform (ELT) architecture, simplifies the overall process and grants greater flexibility in how and when data is transformed. Data integration itself invariably requires data transformation—the process of cleaning, enriching, and reshaping data so that disparate sources can be joined together meaningfully. Data transformation pipelines provide the best platform for performing these tasks, as they accommodate complex logic and are easily automated. Another significant advantage of the ELT architecture is that both data transformation and integration occur directly within the target Databricks database. This ensures fast, on-demand, and highly scalable workflows without the overhead or cost of running additional data processing infrastructure; all computation is handled by Databricks’ robust platform.