How to load data from Parquet File to Kafka

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

Trusted by data-driven companies

Building your pipeline or Using Airbyte

Airbyte is the only open source solution empowering data teams  to meet all their growing custom business demands in the new AI era.

Building in-house pipelines
Bespoke pipelines are:
  • Inconsistent and inaccurate data
  • Laborious and expensive
  • Brittle and inflexible
Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
After Airbyte
Airbyte connections are:
  • Reliable and accurate
  • Extensible and scalable for all your needs
  • Deployed and governed your way
All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

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 Kafka for your extracted Parquet File data

Select Kafka 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 Kafka 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.

Take a virtual tour

Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

Demo video of Airbyte Cloud

Demo video of AI Connector Builder

What sets Airbyte Apart

Modern GenAI Workflows

Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.

Move Large Volumes, Fast

Quickly get up and running with a 5-minute setup that supports both incremental and full refreshes, for databases of any size.

An Extensible Open-Source Standard

More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.

Full Control & Security

Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

Fully Featured & Integrated

Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

Enterprise Support with SLAs

Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

What our users say

Jean-Mathieu Saponaro
Data & Analytics Senior Eng Manager

"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"

Learn more
Chase Zieman headshot
Chase Zieman
Chief Data Officer

“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”

Learn more
Alexis Weill
Data Lead

“We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria.
The value of being able to scale and execute at a high level by maximizing resources is immense”

Learn more

How to Sync Parquet File to Kafka Manually

  1. Download and Install Kafka: Go to the Apache Kafka website and download the latest release of Kafka. Follow the installation instructions for your operating system.
  2. Start Kafka Services: Run the ZooKeeper service and Kafka broker by executing the following commands in the Kafka installation directory:
    bin/zookeeper-server-start.sh config/zookeeper.properties
    bin/kafka-server-start.sh config/server.properties
  3. Create a Kafka Topic: Create a topic where your Parquet data will be sent using the following command:
    bin/kafka-topics.sh --create --topic parquet-data-topic --bootstrap-server localhost:9092 --replication-factor 1 --partitions 1
  1. Install Java: Make sure you have Java installed on your system. Kafka clients are compatible with Java.
  2. Create a New Java Project: Set up a new Java project in your favorite IDE.
  3. Add Kafka Client Dependency: Add the Kafka client library to your project’s build file. If you’re using Maven, add the following dependency to your pom.xml:
    <dependency>
       <groupId>org.apache.kafka</groupId>
       <artifactId>kafka-clients</artifactId>
       <version>YOUR_KAFKA_VERSION</version>
    </dependency>
  4. Add Parquet Dependencies: Add the necessary Parquet dependencies to your project’s build file. For Maven, add:
    <dependency>
       <groupId>org.apache.parquet</groupId>
       <artifactId>parquet-avro</artifactId>
       <version>YOUR_PARQUET_VERSION</version>
    </dependency>
  1. Read Parquet Files: Write a method to read data from Parquet files. You can use ParquetReader from the Parquet library.
  2. Set Up a Kafka Producer: Create a Kafka producer by configuring the necessary properties, such as bootstrap.servers, key.serializer, and value.serializer.
  3. Send Data to Kafka: Iterate over the records in the Parquet file and send them to the Kafka topic using the producer.

Here is a simplified example of what the code might look like:

import org.apache.kafka.clients.producer.*;
import org.apache.parquet.hadoop.ParquetReader;
import org.apache.parquet.hadoop.util.HadoopInputFile;
import org.apache.parquet.avro.AvroParquetReader;
import org.apache.hadoop.fs.Path;
import java.io.IOException;

public class ParquetToKafkaProducer {

   public static void main(String[] args) throws IOException {
       // Kafka producer properties
       Properties properties = new Properties();
       properties.put("bootstrap.servers", "localhost:9092");
       properties.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
       properties.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

       // Create Kafka producer
       KafkaProducer<String, String> producer = new KafkaProducer<>(properties);

       // Path to Parquet file
       Path path = new Path("path/to/your/parquet/file.parquet");

       // Read Parquet file
       ParquetReader<GenericRecord> reader = AvroParquetReader
               .<GenericRecord>builder(HadoopInputFile.fromPath(path, new Configuration()))
               .build();

       GenericRecord record;
       while ((record = reader.read()) != null) {
           // Extract the data you want to send to Kafka
           String key = "someKey"; // Replace with actual key
           String value = record.toString(); // Replace with actual value extraction logic

           // Send the record to Kafka
           producer.send(new ProducerRecord<>("parquet-data-topic", key, value));
       }

       // Close resources
       reader.close();
       producer.close();
   }
}

Compile and run your Java application. This will start reading data from the Parquet file and send it to the Kafka topic you have set up.

To ensure that your data has been successfully published to Kafka, you can consume messages from the topic using Kafka’s console consumer:

bin/kafka-console-consumer.sh --topic parquet-data-topic --from-beginning --bootstrap-server localhost:9092

This command will display the messages that have been sent to the parquet-data-topic topic.

Potential Issues and Considerations

  • Data Serialization: Depending on the structure of your Parquet data, you may need to serialize it in a way that is compatible with Kafka. This example uses StringSerializer, but you might need to use a different serializer like ByteArraySerializer or your custom serializer.
  • Error Handling: Implement robust error handling to manage any issues that occur during reading or writing operations.
  • Performance: Depending on the size of your Parquet files and the rate of data production, you may need to fine-tune your Kafka producer settings for better performance.
  • Security: If your Kafka cluster has security features enabled (like SSL/TLS, SASL, ACLs), you’ll need to configure your Kafka producer accordingly.

How to Sync Parquet File to Kafka 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 Kafka 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 Kafka 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.

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
high five icon
Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
stars sparkling
Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter