As it is free and open source so we use this and there are also some advantages of Apache parquet.

Also it consumes very less space compared to its competitives. Review collected by and hosted on G2.com.

As it is free and open source but still HBase is more popular and we in our organisation are using HBase. I am not criticizing but we people go for popularity and easa of usability. Review collected by and hosted on G2.com.

Best thing that I like about the parquet file format was compression and predictive push down that helps to select only those columns that needed not the whole data will come Review collected by and hosted on G2.com.

The thing that we don't like for parquet files is that it does not support for the use case where our dataset schema frequently changing that pushes me use other file format Review collected by and hosted on G2.com.

Faster execution of the query.Best compression technics which will help to reduce the storage. Review collected by and hosted on G2.com.

As abatch processing system its not ideal for scenarios where we have to need real time updates. Review collected by and hosted on G2.com.

Columnar storage, schema evolution, predicates pushdown, compression, compatibility across various data processing tools, supports wide range of data types like nested structures and arrays. Review collected by and hosted on G2.com.

Limited use case since may not be used for transactional or operational data, overhead when reading or writing data due to its compression and encoding technique Review collected by and hosted on G2.com.

Apache Parquet is best solution for handling large datasets. It saves time and makes data loading easier than in JSON or CSV formats. Review collected by and hosted on G2.com.

Need more schemas for different datasets. Review collected by and hosted on G2.com.

Performance and cross-platform compatible Review collected by and hosted on G2.com.

Learning curve is quite steep, complexity is high Review collected by and hosted on G2.com.

The best about Apce Parquet is it's ability to handle synchronisation and concurrent connections. It provides efficient data storage speed. Review collected by and hosted on G2.com.

The daya retrieval speed for concurrent connections Review collected by and hosted on G2.com.

I love the way it is created to store columnar data. The best thing i like most is that it is built to support very efficient compression and encoding schemes and can be used by anyone. And this is very much helpful in Big Data Analysis. Review collected by and hosted on G2.com.

It was a bit hard to learn by myself but the Apache parquet site provides all the configurations in step by step procedures. So that was not a big issue with me. So honestly there is not much I dislike about it. Review collected by and hosted on G2.com.

Certain times with various large datasets its difficult to process during an etl pipeline for hadoop. This makes it easier since connectivity to other platforms with parquet files is easier to command. It makes the data load easier to handle than json or csv. Review collected by and hosted on G2.com.

There needs to be more schemas available for different business solutions. Review collected by and hosted on G2.com.

I love how easy it is to use to store columnar data. Once you learn the details, it makes Hadoop-use a breeze. Column-store data has many benefits, and Parquet is such a help. Review collected by and hosted on G2.com.

There is definitely a learning curve with the environment, but it is minimal. There honestly is not much I dislike about it. Review collected by and hosted on G2.com.