Apache Parquet Pros and Cons

Apache Parquet has proven to be an invaluable tool in my data analytics toolbox. Its efficient columnar storage, cross-platform compatibility, schema evolution support, and performance optimization features have significantly improved my data processing tasks. It has not only enhanced my productivity but has also reduced infrastructure costs. I highly recommend Apache Parquet to anyone dealing with large datasets and seeking a robust, performance-driven storage solution.Apache Parquet has become an essential part of my data analytics toolkit, and I look forward to continued innovation and development in this fantastic open-source project. Kudos to the Parquet development team for creating such a powerful and user-friendly data storage format! Review collected by and hosted on G2.com.

While Parquet does support schema evolution, it does add some complexity to the process, especially when dealing with complex schema changes. Schema evolution can require careful planning and management to ensure data consistency and query compatibility. Review collected by and hosted on G2.com.

Compatibility is the best about the apache parquet, it is designed for the compatibility within a wide range of data processing of the frameworks and tools like apache spark, apache hive and apache impala and other tools that are helping in making its a one of the best choice Review collected by and hosted on G2.com.

Though it is one of the best choice for batch processing it's doesn't supports real-time data storage Review collected by and hosted on G2.com.

Best thing about Apache Parquet is that it is solving the storage requirements very efficiently. As far as I have experience it reduces the storage requirement by one third of data storage. And the parquet format base support might replace hadoop in future. Review collected by and hosted on G2.com.

For now I don't specifically find anything as downside as I have just started exploring this now. But maybe in future I might have some suggestions on some features of this. Review collected by and hosted on G2.com.

It helps to store in columnar format and having schema evolution. It helps to convert data between avro and parquet formats. These files can be read and write by many programming languages Review collected by and hosted on G2.com.

To be very Frank it is not compatible to handle small scale of data I am facing problems to encode and decode the data which effect my performance it also has limited support for complex data type Review collected by and hosted on G2.com.

It stores data in columnar storage which is highly efficient for analytical queries and also supports compression algorithms. It also has cross platform compatibility which makes it easy to integrate into existing data processing pipelines. Review collected by and hosted on G2.com.

Write performance can be improved, and it taked some time to learn it Review collected by and hosted on G2.com.

One of Parquet’s key strengths is its compatibility with various data processing frameworks, including Apache Hive, Apache Spark, and Apache Drill. Review collected by and hosted on G2.com.

However, there are some considerations to keep in mind when using Apache Parquet. While it excels in performance for read-heavy workloads, writing data into Parquet files can be slower compared to other formats like Apache ORC. Review collected by and hosted on G2.com.

It is mostly useful for storing large amount of data that is used for bigdata analytics.

Apache Parquet reduces IO operations, it is better compared to other tools Review collected by and hosted on G2.com.

More complex to setup and maintain compared to rdbms like mysql Review collected by and hosted on G2.com.

compression is the best fature of apache parquet as it delivers various compression techniques to reduce storage space and improve read performance. It also supports multiple compression algorithms Review collected by and hosted on G2.com.

It doesnt support real time data ingestion, but its perfect choice for batch data processing Review collected by and hosted on G2.com.

Goods for storing any type of massive data, including texts, movies, photo's and structured data tables. It utilizes highly effective column wise comparison and customisable encoding algorithm Review collected by and hosted on G2.com.

Using Apache Parquet having problems with lagre files. Higher CPU usage and affects query performance. Less efficient and can write data more slowly than row based formats like CSV. Review collected by and hosted on G2.com.

Data compression and storage

Storage for large amount of data and it's retrieval Review collected by and hosted on G2.com.

Does not support json which is widely used for data exchange and transfer for cross platform and web development data exchange Review collected by and hosted on G2.com.