Parquet File is a columnar storage format optimized for big data processing. It improves performance by reducing I/O, enabling efficient compression, and supporting complex data types.
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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.
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.
Set up a source connector to extract data from in Airbyte
Choose from one of 400 sources where you want to import data from. This can be any API tool, cloud data warehouse, database, data lake, files, among other source types. You can even build your own source connector in minutes with our no-code no-code connector builder.
Configure the connection in Airbyte
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The only open solution empowering data teams to meet growing business demands in the new AI era.
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Ship more quickly with the only solution that fits ALL your needs.
As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Ship more quickly with the only solution that fits ALL your needs.
As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Move large volumes, fast.
Change Data Capture.
Security from source to destination.
We support the CDC methods your company needs
Log-based CDC
Timestamp-based CDC
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Including for your Airbyte Open Source instance with our premium support.
Airbyte supports a growing list of destinations, including cloud data warehouses, lakes, and databases.
Airbyte supports a growing list of destinations, including cloud data warehouses, lakes, and databases.
Airbyte supports a growing list of sources, including API tools, cloud data warehouses, lakes, databases, and files, or even custom sources you can build.
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FAQs
What is ETL?
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.
What is ELT?
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.
Difference between ETL and ELT?
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 is ETL?
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.
What is ELT?
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.
Difference between ETL and ELT?
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 is ETL?
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.
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.
What is ELT?
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.
Difference between ETL and ELT?
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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