TL;DR

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

1. set up JSON File as a source connector (using Auth, or usually an API key)
2. set up DuckDB as a destination connector
3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is JSON File

JSON (JavaScript Object Notation) is a lightweight data interchange format that is easy for humans to read and write and easy for machines to parse and generate. It is a text format that is used to transmit data between a server and a web application as an alternative to XML. JSON files consist of key-value pairs, where the key is a string and the value can be a string, number, boolean, null, array, or another JSON object. JSON is widely used in web development and is supported by most programming languages. It is also used for storing configuration data, logging, and data exchange between different systems.

What is DuckDB

DuckDB is an in-process SQL OLAP database management system. It has strong support for SQL. DuckDB is borrowing the SQLite shell implementation. Each database is a single file on disk. It’s analogous to “ SQLite for analytical (OLAP) workloads” (direct comparison on the SQLite vs DuckDB paper here), whereas SQLite is for OLTP ones. But it can handle vast amounts of data locally. It’s the smaller, lighter version of Apache Druid and other OLAP technologies.

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Prerequisites

1. A JSON File account to transfer your customer data automatically from.
2. A DuckDB account.
3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including JSON File and DuckDB, for seamless data migration.

When using Airbyte to move data from JSON File to DuckDB, it extracts data from JSON File using the source connector, converts it into a format DuckDB can ingest using the provided schema, and then loads it into DuckDB via the destination connector. This allows businesses to leverage their JSON File data for advanced analytics and insights within DuckDB, simplifying the ETL process and saving significant time and resources.

Methods to Move Data From JSON to duckdb

• Method 1: Connecting JSON to duckdb using Airbyte.
• Method 2: Connecting JSON to duckdb manually.

Method 1: Connecting JSON to duckdb using Airbyte

Step 1: Set up JSON File as a source connector

1. Open the Airbyte platform and navigate to the "Sources" tab on the left-hand side of the screen.
2. Click on the "JSON File" source connector and select "Create new connection".
3. Enter a name for your connection and click "Next".
4. In the "Configuration" tab, enter the path to your JSON file in the "File Path" field. You can also specify a file pattern if you have multiple files with similar names.
5. If your JSON file is password-protected, enter the password in the "Password" field.
6. If your JSON file requires authentication, select the appropriate authentication method (Basic, OAuth2, or Custom) and enter the necessary credentials.
7. Click "Test" to ensure that your connection is working properly.
8. If the test is successful, click "Create" to save your connection.
9. You can now use your JSON File source connector to extract data from your JSON file and load it into your destination of choice.

Step 2: Set up DuckDB as a destination connector

1. Open the Airbyte platform and navigate to the "Destinations" tab on the left-hand side of the screen.
2. Click on the "Add Destination" button located in the top right corner of the screen.
3. Scroll down the list of available destinations until you find "DuckDB" and click on it.
4. Fill in the required information for your DuckDB database, including the host, port, database name, username, and password.
5. Test the connection to ensure that the information you provided is correct and that Airbyte can successfully connect to your DuckDB database.
6. If the connection is successful, click on the "Save" button to save your DuckDB destination connector.
7. You can now use this connector to transfer data from your source connectors to your DuckDB database. Simply select the DuckDB destination connector when setting up your data integration pipelines in Airbyte.

Step 3: Set up a connection to sync your JSON File data to DuckDB

Once you've successfully connected JSON File as a data source and DuckDB as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
2. Choose your source: Select JSON File from the dropdown list of your configured sources.
3. Select your destination: Choose DuckDB from the dropdown list of your configured destinations.
4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
5. Select the data to sync: Choose the specific JSON File objects you want to import data from towards DuckDB. You can sync all data or select specific tables and fields.
6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from JSON File to DuckDB according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your DuckDB data warehouse is always up-to-date with your JSON File data.

Method 2: Connecting JSON to duckdb manually

Moving data from a JSON file to DuckDB can be accomplished using Python with its built-in json module and the duckdb package. Below is a step-by-step guide to achieve this without using third-party connectors or integrations:

Prerequisites

• Ensure you have Python installed on your system.
• Install the duckdb Python package if you haven’t already:

pip install duckdb

Step 1: Read the JSON File

First, you need to read the JSON data from the file.

import json

# Replace 'your_data.json' with the path to your JSON file

with open('your_data.json', 'r') as file:

    json_data = json.load(file)

Step 2: Normalize the JSON Data (Optional)

If your JSON data is nested, you might need to flatten it using pandas.json_normalize or a similar utility to make it suitable for a tabular database like DuckDB.

import pandas as pd

# Flattening JSON data if it's nested

# This step depends on the structure of your JSON data

df = pd.json_normalize(json_data)

Step 3: Initialize DuckDB

Next, initialize DuckDB and create a connection.

import duckdb

# Initialize a DuckDB connection in-memory

# You can also connect to a DuckDB file by passing a file path instead of ':memory:'

conn = duckdb.connect(database=':memory:', read_only=False)

Step 4: Create a DuckDB Table

Create a table in DuckDB that matches the structure of your JSON data.

# Define a SQL statement to create a table

# The columns and types should match the data in your JSON

create_table_sql = """

CREATE TABLE my_table (

    id INTEGER,

    name VARCHAR,

    value FLOAT,

    -- Add more columns as necessary

);

"""

# Execute the SQL statement to create the table

conn.execute(create_table_sql)

Step 5: Insert JSON Data into DuckDB

Insert the data from the JSON file into the newly created DuckDB table.

# Convert the DataFrame to a list of tuples for insertion

# Skip this step if you didn't need to flatten the JSON data

data_to_insert = list(df.itertuples(index=False, name=None))

# Define a SQL statement for insertion

# The placeholders (?) should match the number of columns

insert_sql = "INSERT INTO my_table (id, name, value) VALUES (?, ?, ?)"

# Execute the insert statement for each row in the data

for row in data_to_insert:

    conn.execute(insert_sql, row)

Step 6: Verify the Insertion (Optional)

Optionally, you can verify that the data has been inserted correctly by querying the table.

# Fetch the data from the table to verify

result = conn.execute("SELECT * FROM my_table").fetchall()

print(result)

Step 7: Close the Connection

Once you’re done, remember to close the connection to DuckDB.

conn.close()

Notes

• The above guide assumes that your JSON data is an array of objects where each object corresponds to a row in the database table. Adjustments may be needed if your data has a different structure.
• The data types in the CREATE TABLE statement should match the data types in your JSON file.
• If your JSON file is large, consider using batch inserts or transactions to improve performance.
• Always sanitize and validate your data to prevent SQL injection when inserting data into a database.
• This guide uses an in-memory database for simplicity, but you can also persist the data by specifying a file path when connecting to DuckDB.

By following these steps, you should be able to move data from a JSON file to a DuckDB database without using third-party connectors or integrations.

Use Cases to transfer your JSON File data to DuckDB

Integrating data from JSON File to DuckDB provides several benefits. Here are a few use cases:

1. Advanced Analytics: DuckDB’s powerful data processing capabilities enable you to perform complex queries and data analysis on your JSON File data, extracting insights that wouldn't be possible within JSON File alone.
2. Data Consolidation: If you're using multiple other sources along with JSON File, syncing to DuckDB allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
3. Historical Data Analysis: JSON File has limits on historical data. Syncing data to DuckDB allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: DuckDB provides robust data security features. Syncing JSON File data to DuckDB ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: DuckDB can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding JSON File data.
6. Data Science and Machine Learning: By having JSON File data in DuckDB, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
7. Reporting and Visualization: While JSON File provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to DuckDB, providing more advanced business intelligence options. If you have a JSON File table that needs to be converted to a DuckDB table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

1. Configure a JSON File account as an Airbyte data source connector.
2. Configure DuckDB as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from JSON File to DuckDB after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!