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Parquet and its Uses

Latest reply: Jul 26, 2022 16:29:21 180 13 3 0 0

What is Parquet:

Apache Parquet is a file format designed for fast complicated data processing. Apache Parquet is column-oriented, unlike row-based formats like CSV or Avro, so table column values are stored next to each other.

Open-source: Under the Apache Hadoop license, Parquet is open source and free to use, and it is compatible with the majority of Hadoop data processing frameworks. Apache Parquet is available for every project, regardless of the data processing architecture, data model, or programming language is chosen, according to the project website.

 

In addition to data, a Parquet file includes metadata, schema, and organization. Each file stores both the data and the standards used to access each record, which makes it simpler to decouple services that write, save, and read Parquet files.

 

 

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Advantages:

Apache Parquet's features make it ideal for storing and analyzing massive amounts of data. Let's examine some in detail.

 

·         Columnar storage formats like Apache Parquet are supposed to be more efficient than row-based formats like CSV. When searching columnar storage, you can easily skip over irrelevant data. Consequently, aggregation searches in column-oriented databases are more efficient. This method of data storing has resulted in hardware savings and reduced data access latency.

·         Apache Parquet is constructed from scratch. Therefore, it can accommodate complex nested data structures. The structure of Parquet data files is optimized for searches that handle gigabytes per file of data.

·         Parquet is built to enable various compression options and efficient encoding algorithms. As each column's data type is relatively similar, compressing each column is trivial (which makes queries even faster). Using one of the available codecs, data can be compressed; as a result, different data files can be compressed differently.

·         Apache Parquet works best with serverless and interactive technologies such as AWS Athena, Amazon Redshift Spectrum, and Google BigQuery.

 

When should parquet be used:

Apache Parquet's features make it ideal for storing and analyzing massive amounts of data. Let's examine some in detail.

 

Parquet is optimized for working with large amounts of complex data and has multiple compression and encoding methods. This method is particularly advantageous for queries that must read specific columns from a huge table. Parquet can only read the required columns, hence drastically reducing IO.Compression shrinks a file. In Parquet, compression is performed column by column and it supports customizable compression settings and expandable encoding schemas by data type. For example, integer and text data can be compressed using separate encoding.

 

 


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MahMush
Moderator Author Created Jul 26, 2022 12:19:37

Using Parquet is a good place to start, but optimizing data lake queries doesn't stop there. To ensure queries are consistently answered quickly and cost-effectively, you must frequently clean, enrich, and transform the data, perform high-cardinality joins, and implement a slew of best practices.
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hanhcao
Created Jul 26, 2022 13:26:40

Good share
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MahMush
MahMush Created Jul 28, 2022 08:35:35 (0) (0)
glad to see your response.  
Saqibaz
Created Jul 26, 2022 13:47:55

Good share
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MahMush
MahMush Created Jul 28, 2022 08:42:04 (0) (0)
please add some of your valuable insights.  
user_3915171
Created Jul 26, 2022 15:14:47

thanks
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GhaziAsad
GhaziAsad Created Jul 26, 2022 16:28:23 (0) (0)
 
GhaziAsad
GhaziAsad Created Jul 26, 2022 16:28:28 (0) (0)
 
Ayeshaali
Created Jul 26, 2022 16:27:52

Good share
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MahMush
MahMush Created Jul 30, 2022 15:11:31 (0) (0)
it's nice to see you liked the post.  
Ayeshaali
Created Jul 26, 2022 16:27:58

Thanks
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Saqib123
Moderator Created Jul 26, 2022 16:29:21

Good Content
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MahMush
MahMush Created Jul 28, 2022 08:19:10 (0) (0)
hope you learned from the content.  

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