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 Apache Iceberg 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 Apache Iceberg

For huge analytical tables, Apache Iceberg is a high-performance format. Using Apache Iceberg, engines such as Spark, Trino, Flink, Presto, Hive and Impala can safely work with the same tables, at the same time, providing the reliability and simplicity of SQL tables to big data. With Apache Iceberg, you can merge new data, update existing rows, and delete specific rows. Data files can be eagerly rewritten or deleted deltas can be used to make updates faster.

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Prerequisites

1. A Parquet File account to transfer your customer data automatically from.
2. A Apache Iceberg 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 Apache Iceberg, for seamless data migration.

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

Methods to Move Data From Parquet to apache iceberg

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

Method 1: Connecting Parquet to apache iceberg 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 Apache Iceberg 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 "Apache Iceberg" destination connector and select "Create new connection."
3. Enter a name for your connection and provide the necessary credentials for your Apache Iceberg database, including the host, port, database name, username, and password.
4. Test the connection to ensure that it is successful. 5. Select the tables or data sources that you want to replicate to your Apache Iceberg database.
6. Configure any additional settings or options for your connection, such as the frequency of data replication or any transformations that you want to apply to your data.
7. Save your connection and start the replication process.
8. Monitor the progress of your data replication and troubleshoot any issues that may arise.
9. Once the replication process is complete, verify that your data has been successfully replicated to your Apache Iceberg database.
10. Use your Apache Iceberg database to analyze and query your data as needed.

Step 3: Set up a connection to sync your Parquet File data to Apache Iceberg

Once you've successfully connected Parquet File as a data source and Apache Iceberg 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 Apache Iceberg 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 Apache Iceberg. 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 Apache Iceberg according to your settings.

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

Method 2: Connecting Parquet to apache iceberg manually

Moving data from Parquet files to Apache Iceberg tables without using third-party connectors or integrations involves several steps. Below is a detailed guide to accomplishing this task:

Prerequisites:

- Java Development Kit (JDK) installed

- Apache Spark with Iceberg support (Iceberg is integrated with Spark since version 3.0)

- Access to a Hadoop-compatible file system (like HDFS, S3, or local filesystem)

- The Parquet files that you want to move

- Basic knowledge of Spark and Iceberg APIs

Step 1: Set Up Your Environment

1. Install Apache Spark: Make sure you have Apache Spark installed and properly configured. You can download it from the official Apache Spark website.

2. Add Iceberg Dependencies: If you're using Spark 3.x, you'll need to include Iceberg's Spark runtime jar in your Spark session:

   ```shell

   spark-shell --packages org.apache.iceberg:iceberg-spark3-runtime:0.13.0

   ```

Step 2: Initialize Spark Session

In your Java/Scala/Python application, initialize the Spark session with Iceberg support:

```scala

val spark = SparkSession.builder()

  .appName("ParquetToIceberg")

  .config("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions")

  .config("spark.sql.catalog.local", "org.apache.iceberg.spark.SparkCatalog")

  .config("spark.sql.catalog.local.type", "hadoop")

  .config("spark.sql.catalog.local.warehouse", "path_to_your_iceberg_warehouse")

  .getOrCreate()

```

Step 3: Load Parquet Data

Load your Parquet data into a Spark DataFrame:

```scala

val parquetData = spark.read.parquet("path_to_your_parquet_files")

```

Step 4: Create an Iceberg Table

Create an Iceberg table that will store your data:

```scala

spark.sql("CREATE TABLE local.db.table_name (id bigint, data string) USING iceberg")

```

Make sure to replace `db.table_name` with your desired database and table name, and define the schema (`id bigint, data string`) according to the actual schema of your Parquet files.

Step 5: Write Data to the Iceberg Table

Append the data from the Parquet DataFrame to the Iceberg table:

```scala

parquetData.write.format("iceberg").mode("append").save("local.db.table_name")

```

Step 6: Verify the Data

After writing, you can verify that your data has been successfully moved to the Iceberg table:

```scala

val icebergData = spark.read.format("iceberg").load("local.db.table_name")

icebergData.show()

```

Step 7: Optimize the Iceberg Table

Optionally, you can perform optimizations like compaction or partitioning on the Iceberg table to improve query performance:

```scala

// Example of adding partitioning

spark.sql("ALTER TABLE local.db.table_name WRITE ORDERED BY id")

```

Step 8: Clean Up

After successfully moving your data, make sure to stop the Spark session:

```scala

spark.stop()

```

Notes:

- The steps provided are for Spark with Scala, but similar steps can be taken with PySpark or Spark with Java.

- Replace `"path_to_your_iceberg_warehouse"` and `"path_to_your_parquet_files"` with the actual paths to your Iceberg warehouse and Parquet files, respectively.

- The table schema defined in the `CREATE TABLE` statement should match the schema of the Parquet files.

- If you're using a version of Spark earlier than 3.0, you'll need to use the Iceberg Spark 2.x compatibility module.

- Always ensure the Iceberg version you're using is compatible with your Spark version.

By following these steps, developers should be able to move data from Parquet files to Apache Iceberg tables without any third-party connectors or integrations.

Use Cases to transfer your Parquet File data to Apache Iceberg

Integrating data from Parquet File to Apache Iceberg provides several benefits. Here are a few use cases:

1. Advanced Analytics: Apache Iceberg’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 Apache Iceberg 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 Apache Iceberg allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: Apache Iceberg provides robust data security features. Syncing Parquet File data to Apache Iceberg ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: Apache Iceberg 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 Apache Iceberg, 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 Apache Iceberg, providing more advanced business intelligence options. If you have a Parquet File table that needs to be converted to a Apache Iceberg 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 Apache Iceberg as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from Parquet File to Apache Iceberg 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!