How to load data from Parquet File to MySQL Destination

Learn how to use Airbyte to synchronize your Parquet File data into MySQL Destination within minutes.

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Set up a Parquet File connector in Airbyte

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

Set up MySQL Destination for your extracted Parquet File data

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

Configure the Parquet File to MySQL Destination 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 Parquet File to MySQL Destination Manually

First, you need to have Python installed on your system. Then, install the following Python libraries if you haven’t already:

pip install pandas pyarrow pymysql

pandas is used for data manipulation, pyarrow is for reading Parquet files, and pymysql is a MySQL client library that allows you to interact with MySQL databases.

Import the required libraries and read your Parquet file into a pandas DataFrame:

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)

Make sure you have MySQL installed and running on your system.

  1. Log in to MySQL:

mysql -u username -p

  1. Create a new database (if necessary):

CREATE DATABASE your_database;

  1. Select the database:

USE your_database;

  1. Create a table that matches the schema of the DataFrame:

CREATE TABLE your_table (

    column1 datatype,

    column2 datatype,

    ...

);

Replace column1, column2, ..., and datatype with the appropriate column names and data types that correspond to your Parquet file’s schema.

Now, you can insert the data from the DataFrame into the MySQL table. Use pymysql to connect to the MySQL database and pandas to insert the data:

import pymysql

# Database connection parameters

db_params = {

    'host': 'localhost',

    'user': 'your_username',

    'password': 'your_password',

    'database': 'your_database'

}

# Establish a database connection

connection = pymysql.connect(db_params)

# Function to insert data in chunks

def insert_data(df, table_name, conn):

    # Define the base insert query

    cols = ','.join(list(df.columns))

    placeholders = ','.join(['%s'] * len(df.columns))

    base_query = f"INSERT INTO {table_name} ({cols}) VALUES ({placeholders})"

    # Execute the insert query in chunks

    for chunk in df:

        with conn.cursor() as cursor:

            for row in chunk.itertuples(index=False, name=None):

                cursor.execute(base_query, row)

        conn.commit()

# Optional: Convert DataFrame to chunks if it is too large to fit in memory

chunk_size = 1000  # Define the chunk size

df_chunks = (chunk for chunk in df)

# Insert data

insert_data(df_chunks, 'your_table', connection)

# Close the database connection

connection.close()

Make sure to replace 'your_username', 'your_password', 'your_database', and 'your_table' with your actual MySQL credentials, database name, and table name.

After running the script, log in to your MySQL database and verify that the data has been transferred correctly:

mysql -u username -p

USE your_database;

SELECT * FROM your_table LIMIT 10;

This will display the first 10 rows of the table to confirm that the data has been inserted.

Additional Notes:

  • The code provided above assumes that you can load the entire DataFrame into memory. If your Parquet file is too large, you should read and insert the data in chunks.
  • Adjust the chunk_size according to your system’s memory capacity.
  • The data types in the MySQL table must be compatible with the data types in the DataFrame.
  • Error handling is not included in the code above. In a production environment, you should add try-except blocks and logging to handle and record any issues that may occur during the data transfer process.
  • Always back up your MySQL database before performing bulk insert operations to prevent data loss in case of an error.

How to Sync Parquet File to MySQL Destination 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.

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.

Parquet File's API gives access to various types of data, including:  

• Structured data: Parquet files can store structured data in a columnar format, making it easy to query and analyze large datasets.  
• Semi-structured data: Parquet files can also store semi-structured data, such as JSON or XML, allowing for more flexibility in data storage.  
• Unstructured data: Parquet files can store unstructured data, such as text or binary data, making it possible to store a wide range of data types in a single file.  
• Big data: Parquet files are designed for big data applications, allowing for efficient storage and processing of large datasets.  
• Machine learning data: Parquet files are commonly used in machine learning applications, as they can store large amounts of data in a format that is optimized for processing by machine learning algorithms.  

Overall, Parquet File's API provides access to a wide range of data types, making it a versatile tool for data storage and analysis in a variety of applications.

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 to MySQL 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 Parquet File to MySQL 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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