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 Parquet 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 Parquet File

Parquet File is a columnar storage file format that is designed to store and process large amounts of data efficiently. It is an open-source project that was developed by Cloudera and Twitter. Parquet File is optimized for use with Hadoop and other big data processing frameworks, and it is designed to work well with both structured and unstructured data. The format is highly compressed, which makes it ideal for storing and processing large datasets. Parquet File is also designed to be highly scalable, which means that it can be used to store and process data across multiple nodes in a distributed computing environment.

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.

 {{COMPONENT_CTA}}

Prerequisites

1. A Parquet 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 Parquet File and DuckDB, for seamless data migration.

When using Airbyte to move data from Parquet File to DuckDB, it extracts data from Parquet 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 Parquet File data for advanced analytics and insights within DuckDB, simplifying the ETL process and saving significant time and resources.

Methods to Move Data From Parquet to duckdb

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

Method 1: Connecting Parquet to duckdb using Airbyte

Step 1: Set up Parquet File as a source connector

1. Open the Airbyte dashboard and click on "Sources" on the left-hand side of the screen.
2. Click on the "Create Connection" button and select "Parquet File" from the list of available connectors.
3. Enter a name for your connection and click on "Next".
4. In the "Configuration" tab, enter the path to your Parquet file in the "File Path" field.
5. If your Parquet file is password-protected, enter the password in the "Password" field.
6. If your Parquet file is encrypted, select the appropriate encryption type from the "Encryption Type" dropdown menu and enter the encryption key in the "Encryption Key" field.
7. Click on "Test Connection" to ensure that your credentials are correct and that Airbyte can connect to your Parquet file.
8. If the test is successful, click on "Create" to save your connection.
9. You can now use this connection to create a new Airbyte pipeline and start syncing data from your Parquet file to your destination.

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 Parquet File data to DuckDB

Once you've successfully connected Parquet 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 Parquet 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 Parquet 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 Parquet 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 Parquet File data.

Method 2: Connecting Parquet to duckdb manually

Moving data from Parquet to DuckDB without the use of third-party connectors or integrations requires a few steps where you'll read the data from the Parquet file and then insert it into DuckDB. This guide will use Python, as it has native support for both Parquet files and DuckDB through the `pandas` and `duckdb` libraries, respectively. If you don't have Python installed, you'll need to install it first.

Here are the detailed steps:

Step 1: Install Required Python Libraries

First, you need to install `pandas` and `duckdb`. You can install them using `pip`:

```bash

pip install pandas duckdb

```

Step 2: Read Parquet File

Next, you'll read the Parquet file using `pandas`. You need to know the path to your Parquet file for this step.

```python

import pandas as pd

# Replace 'your_data.parquet' with the path to your Parquet file

parquet_file = 'your_data.parquet'

df = pd.read_parquet(parquet_file)

```

Step 3: Install DuckDB

If you haven't already installed DuckDB, you can do so by running:

```bash

pip install duckdb

```

Step 4: Write Data to DuckDB

Now you will write the data from the DataFrame to DuckDB. You can either write it to an in-memory database or a file-based persistent database.

#In-memory Database:

```python

import duckdb

# Connect to an in-memory DuckDB database

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

‍

# Write the DataFrame to DuckDB

con.execute("CREATE TABLE my_table AS SELECT * FROM df", {'df': df})

```

#File-based Persistent Database:

```python

# Replace 'my_database.duckdb' with the path to your DuckDB database file

database_file = 'my_database.duckdb'

con = duckdb.connect(database=database_file, read_only=False)

# Write the DataFrame to DuckDB

con.execute("CREATE TABLE my_table AS SELECT * FROM df", {'df': df})

```

Step 5: Verify Data Transfer

After writing the data to DuckDB, you may want to verify that the transfer was successful.

```python

# Query the newly created table to verify its contents

result = con.execute("SELECT * FROM my_table").fetchdf()

print(result.head())  # Displays the first few rows of the table

```

Step 6: Close the Connection

Once you have finished working with the database, make sure to close the connection.

```python

con.close()

```

Additional Notes

- Make sure that the column names and data types in the DataFrame are compatible with DuckDB before attempting to write to the database.

- If your Parquet file is very large, consider reading and writing the data in chunks to avoid memory issues.

- You can also perform transformations on the DataFrame before writing it to DuckDB if needed.

- If you encounter any issues, check the documentation for `pandas` and `duckdb` or look for any error messages that may indicate what went wrong.

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

Use Cases to transfer your Parquet File data to DuckDB

Integrating data from Parquet 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 Parquet File data, extracting insights that wouldn't be possible within Parquet File alone.
2. Data Consolidation: If you're using multiple other sources along with Parquet 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: Parquet 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 Parquet 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 Parquet File data.
6. Data Science and Machine Learning: By having Parquet 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 Parquet 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 Parquet 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 Parquet 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 Parquet 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!