Parquet files, known for their efficiency in storing and processing large datasets, are becoming increasingly popular in data analytics. This article explores how to seamlessly import Parquet data into SQL Server using Airbyte, an open-source data integration platform. We'll walk through the process to help data engineers streamline their data pipeline and enhance their analytical capabilities.

What is Parquet?

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 MS SQL Server?

Microsoft SQL Server is a relational database management (RDBMS) built by Microsoft. As a database server, its primary function is to store and retrieve data upon the request of other software applications, either from the same computer or a different computer across a network—including the internet. To serve the needs of different audiences and workload sizes, Microsoft offers multiple editions (at least 12) of its Microsoft SQL Server.

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Methods to Import Parquet File to SQL Server

• Method 1: Connecting Parquet to MS SQL server using Airbyte.
• Method 2: Connecting Parquet to MS SQL server manually.

Method 1: Connecting Parquet to MS SQL server using Airbyte

Prerequisites

1. A Parquet File account to transfer your customer data automatically from.
2. A MS SQL Server 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 MS SQL Server, for seamless data migration.

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

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 MS SQL Server as a destination connector

1. Open the Airbyte platform and navigate to the "Destinations" tab on the left-hand side of the screen.
2. Scroll down until you find the "MSSQL - SQL Server" connector and click on it.
3. Click on the "Create new destination" button.
4. Fill in the required information, including the destination name, host, port, database name, username, and password.
5. Click on the "Test connection" button to ensure that the connection is successful.
6. Once the connection is successful, click on the "Save" button to save the destination.
7. Navigate to the "Sources" tab on the left-hand side of the screen and select the source that you want to connect to the MSSQL - SQL Server destination.
8. Click on the "Create new connection" button.
9. Select the MSSQL - SQL Server destination that you just created from the drop-down menu.
10. Fill in the required information for the source, including the source name, host, port, database name, username, and password.
11. Click on the "Test connection" button to ensure that the connection is successful.
12. Once the connection is successful, click on the "Save" button to save the connection.13. You can now start syncing data from your source to your MSSQL - SQL Server destination.

Step 3: Set up a connection to sync your Parquet File data to MS SQL Server

Once you've successfully connected Parquet File as a data source and MS SQL Server 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 MS SQL Server 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 MS SQL Server. 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 MS SQL Server according to your settings.

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

Method 2: Connecting Parquet to MS SQL server manually

To move data from Parquet files to Microsoft SQL Server without using third-party connectors or integrations, you can use Python with the pyodbc and pandas libraries to read the Parquet file and insert the data into SQL Server. Here’s a step-by-step guide to accomplish this task:

Prerequisites:

• Python is installed on your system.
• pyodbc and pandas Python libraries installed. You can install them using pip:

pip install pyodbc pandas

• Microsoft ODBC Driver for SQL Server installed on your system.
• Access to an SQL Server instance and the necessary permissions to create a table and insert data.
• Parquet file that you want to move to SQL Server.

1. Import Libraries

Open your Python editor or an interactive shell and import the required libraries.

import pyodbc

import pandas as pd

2. Read Parquet File

Use pandas to read the Parquet file.

parquet_file = 'path_to_your_parquet_file.parquet'

df = pd.read_parquet(parquet_file)

3. Connect to SQL Server

Establish a connection to your SQL Server instance using pyodbc.

server = 'your_sql_server'

database = 'your_database'

username = 'your_username'

password = 'your_password'

cnxn_str = f'DRIVER={{ODBC Driver 17 for SQL Server}};SERVER={server};DATABASE={database};UID={username};PWD={password}'

cnxn = pyodbc.connect(cnxn_str)

cursor = cnxn.cursor()

4. Create Table in SQL Server

Create a table in SQL Server that matches the schema of the Parquet file. You can generate the SQL create table statement by inspecting the dataframe schema.

# Example: Assuming the dataframe `df` has columns 'id', 'name', and 'age'

create_table_sql = """

CREATE TABLE YourTableName (

id INT,

name NVARCHAR(100),

age INT

)

"""

cursor.execute(create_table_sql)

cnxn.commit()

5. Insert Data into SQL Server

Insert the data from the dataframe into the SQL Server table. You can use the to_sql method from pandas.

# Define a function to insert the data using a transaction

def insert_data(df, table_name, cnxn):

placeholders = ','.join('?' for _ in df.columns)

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

for index, row in df.iterrows():

cursor.execute(insert_sql, tuple(row))

cnxn.commit()

‍

# Call the function to insert data

table_name = 'YourTableName'

insert_data(df, table_name, cnxn)

6. Verify Data Transfer

Run a SELECT query to verify that the data has been transferred correctly.

cursor.execute(f"SELECT * FROM {table_name}")

for row in cursor.fetchall():

print(row)

7. Close the Connection

After verifying the data transfer, close the cursor and the connection to SQL Server.

cursor.close()

cnxn.close()

Notes:

• Make sure to replace placeholders like path_to_your_parquet_file.parquet, your_sql_server, your_database, your_username, your_password, and YourTableName with your actual file path and SQL Server details.
• The column types in the SQL create table statement should match the data types in the Parquet file. You may need to adjust the SQL data types accordingly.
• For large datasets, consider using batch insertion or a more efficient method than inserting row by row, as this can be slow.
• Always handle exceptions and errors by implementing proper error handling mechanisms.
• If your data contains special characters or binary data, make sure to handle these appropriately when inserting into SQL Server.

Keep in mind that while this method doesn’t use third-party connectors or integrations, it does rely on Python and associated libraries, which are third-party tools. However, they are commonly used and widely accepted in the developer community.

Use Cases to import your Parquet File data to MS SQL Server

1. Data Warehouse Integration

E-commerce company collects vast amounts of customer behavior data from its website and mobile app. This data is initially stored in Parquet files on a distributed file system. The continuous flow of data is then integrated with existing customer and product databases in SQL Server using Airbyte. The company can now run complex queries to gain insights into customer behavior, optimize inventory management, and personalize marketing campaigns using up-to-date data in SQL Server.

2. Log Analysis for IT Operations

An IT department collects system logs from various servers and applications, storing them in Parquet format for efficiency. These logs are imported into SQL Server for centralized analysis and monitoring.

3. Scientific Data Analysis

A research institution collects large volumes of sensor data from scientific experiments, initially storing it in Parquet files. This data is imported into SQL Server for long-term storage and analysis.

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 MS SQL Server as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from Parquet File to MS SQL Server 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!