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.
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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.
1. First, ensure that you have the necessary credentials to access your Oracle DB. This includes the hostname, port number, database name, username, and password.
2. Open the Airbyte platform and navigate to the "Destinations" tab on the left-hand side of the screen.
3. Scroll down until you find the "Oracle DB" destination connector and click on it.
4. Click on the "Create new connection" button to begin setting up your Oracle DB destination.
5. Enter a name for your connection and fill in the required fields with your Oracle DB credentials.
6. Test the connection to ensure that Airbyte can successfully connect to your Oracle DB.
7. Once the connection is successful, you can configure the settings for your Oracle DB destination. This includes selecting the tables you want to sync, setting up any transformations or mappings, and scheduling the sync frequency.
8. Save your settings and start the sync process. Airbyte will begin pulling data from your source and pushing it to your Oracle DB destination.
9. Monitor the sync process to ensure that it is running smoothly and troubleshoot any issues that may arise.
10. Once the sync is complete, you can access your data in your Oracle DB and use it for analysis, reporting, or any other purposes.
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!
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Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
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Frequently Asked Questions
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.