How to load data from JSON File to DuckDB

Learn how to use Airbyte to synchronize your JSON File data into DuckDB within minutes.

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Set up a JSON File connector in Airbyte

Connect to JSON File or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up DuckDB for your extracted JSON File data

Select DuckDB where you want to import data from your JSON File source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the JSON File to DuckDB in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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How to Sync JSON File to DuckDB Manually

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)

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)

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)

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)

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)

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)

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.

How to Sync JSON File to DuckDB Manually - Method 2:

FAQs

ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

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.

JSON File provides access to a wide range of data types, including:  

- User data: This includes information about individual users, such as their name, email address, and account preferences.
- Product data: This includes information about the products or services offered by a company, such as their name, description, price, and availability.
- Order data: This includes information about customer orders, such as the products ordered, the order status, and the shipping address.
- Inventory data: This includes information about the stock levels of products, as well as any backorders or out-of-stock items.
- Analytics data: This includes information about website traffic, user behavior, and other metrics that can help businesses optimize their online presence.
- Marketing data: This includes information about marketing campaigns, such as email open rates, click-through rates, and conversion rates.
- Financial data: This includes information about revenue, expenses, and other financial metrics that can help businesses track their performance and make informed decisions.  

Overall, JSON File provides a comprehensive set of data that can help businesses better understand their customers, products, and performance.

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 to DuckDB as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from JSON File to DuckDB and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud. 

ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.

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