VarshaUN commented on code in PR #2983: URL: https://github.com/apache/parquet-java/pull/2983#discussion_r1712986559
########## parquet-avro/README.md: ########## @@ -44,3 +44,80 @@ Apache Avro integration | `parquet.avro.add-list-element-records` | `boolean` | Flag whether to assume that any repeated element in the schema is a list element.<br/>The default value is `true`. | | `parquet.avro.write-parquet-uuid` | `boolean` | Flag whether to write the [Parquet UUID logical type](https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#uuid) in case of an [Avro UUID type](https://avro.apache.org/docs/current/spec.html#UUID) is present.<br/>The default value is `false`. | | `parquet.avro.writeFixedAsInt96` | `String` | Comma separated list of paths pointing to Avro schema elements which are to be converted to `INT96` Parquet types.<br/>The path is a `'.'` separated list of field names and does not contain the name of the schema nor the namespace. The type of the referenced schema elements must be `fixed` with the size of 12 bytes.<br/>**NOTE: The `INT96` Parquet type is deprecated. This option is only to support old data.** | + +## Apache Parquet-Avro is a combination of two technologies: + +1. **Apache Parquet**: A columnar storage format for Hadoop and other big data platforms, optimized for querying large datasets. + +2. **Apache Avro**: A data serialization system that provides a compact, fast, and efficient way to serialize and deserialize data. + +## Major aspects of Apache Parquet-Avro: + +**Parquet aspects**: + +1. **Columnar storage**: Stores data in columns instead of rows, reducing storage and improving query performance. +2. **Efficient compression**: Supports various compression algorithms, reducing storage and improving data transfer. +3. **Schema evolution**: Allows for schema changes without rewriting existing data. +4. **High-performance querying**: Optimized for fast querying and data retrieval. + +**Avro aspects**: + +1. **Data serialization**: Provides a compact, fast, and efficient way to serialize and deserialize data. +2. **Schema-based**: Uses a schema to define data structures, ensuring data consistency and compatibility. +3. **Language-independent**: Supports multiple programming languages, including Java, Python, and C++. +4. **Rich data structures**: Supports complex data structures, including records, enums, and arrays. + +## Parquet-Avro integration: + +1. **Avro schema integration**: Uses Avro schemas to define Parquet data structures. +2. **Efficient Avro data storage**: Stores Avro data in Parquet files, leveraging Parquet's columnar storage and compression. +3. **Seamless data exchange**: Enables easy data exchange between Avro and Parquet formats. + +By combining Parquet's columnar storage and Avro's data serialization, Parquet-Avro provides a powerful and efficient way to store and query large datasets. + +## Specific and practical applications of Apache Parquet-Avro + +1. **Data ingestion and processing**: Using Parquet-Avro to efficiently store and process large amounts of data from various sources, like logs, sensors, or social media. + +2. **Data transformation and aggregation**: Leveraging Parquet-Avro's columnar storage to perform fast data transformations and aggregations, like data warehousing or business intelligence workloads. + +3. **Real-time data analytics**: Utilizing Parquet-Avro's efficient storage and querying capabilities to power real-time data analytics, like fraud detection or live dashboards. + +4. **Machine learning data preparation**: Using Parquet-Avro to store and prepare large datasets for machine learning model training, feature engineering, and data augmentation. + +5. **Data archiving and compliance**: Employing Parquet-Avro's compression and encryption features to archive large datasets for long-term storage and compliance purposes. + +6. **Cloud data migration**: Migrating large datasets to cloud storage services, like Amazon S3 or Google Cloud Storage, using Parquet-Avro for efficient data transfer and storage. + +7. **Data integration and ETL**: Using Parquet-Avro as a common format for data integration and ETL (Extract, Transform, Load) processes, ensuring data consistency and compatibility. + +8. **IoT data processing and analytics**: Storing and processing large amounts of IoT sensor data using Parquet-Avro, enabling efficient data analytics and insights. + +## Parquet-Avro API Overview Review Comment: my bad, I added some examples now @wgtmac -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
