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  5. Amazon Athena vs Apache Parquet

Amazon Athena vs Apache Parquet

OverviewDecisionsComparisonAlternatives

Overview

Apache Parquet
Apache Parquet
Stacks97
Followers190
Votes0
Amazon Athena
Amazon Athena
Stacks519
Followers840
Votes49

Amazon Athena vs Apache Parquet: What are the differences?

Introduction

Amazon Athena and Apache Parquet are both widely used technologies in the field of big data analytics. While Amazon Athena is a serverless interactive query service that enables users to analyze data directly from Amazon S3 using SQL, Apache Parquet is a columnar storage file format that is optimized for analytical processing.

  1. Data Storage: One key difference between Amazon Athena and Apache Parquet is their approach to data storage. Amazon Athena uses Amazon S3 as its data source, where data is stored in its original format. On the other hand, Apache Parquet is a file format that stores data in a columnar format, which is more efficient for analytical processing. This columnar storage allows for faster query performance, especially when only a subset of columns needs to be accessed.

  2. Data Schema: Another difference lies in how the data schema is managed. Amazon Athena does not require predefined schema definitions and can infer the schema dynamically at runtime, making it more flexible for ad-hoc queries. In contrast, Apache Parquet requires a predefined schema to be defined and enforced, providing stronger data validation and consistency.

  3. Data Partitioning: Amazon Athena supports automatic data partitioning based on the data stored in Amazon S3. This means that queries can be optimized by targeting specific partitions, resulting in faster execution times. On the other hand, Apache Parquet does not have built-in support for automatic data partitioning and requires manual configuration.

  4. Query Performance: When it comes to query performance, Amazon Athena provides a serverless, highly scalable and performant solution. It can handle large-scale parallel query execution, resulting in fast query response times. Apache Parquet, being a columnar storage format, offers faster query performance due to its ability to selectively read only the required columns for a query, minimizing disk I/O.

  5. Data Compression: Both Amazon Athena and Apache Parquet support data compression techniques, but they have different approaches. Amazon Athena utilizes ORC (Optimized Row Columnar) or Parquet columnar storage formats and uses compression techniques like Snappy and Zlib. Apache Parquet, being a columnar storage format itself, employs a wide range of compression options such as Snappy, Gzip, and LZO, allowing users to choose the most suitable compression algorithm for their data.

  6. Data Accessibility: Amazon Athena is a managed service provided by Amazon Web Services (AWS) and can be accessed directly through the AWS Management Console or API, making it easily accessible to AWS users. Apache Parquet, being an open-source file format, can be accessed through various tools and frameworks including Apache Hive, Apache Spark, and Apache Arrow, providing flexibility for users to choose the best toolset for their specific needs.

In Summary, Amazon Athena and Apache Parquet differ in their storage approach, data schema management, data partitioning capabilities, query performance, data compression options, and accessibility, making them suitable for different use cases and preferences.

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Advice on Apache Parquet, Amazon Athena

Pavithra
Pavithra

Mar 12, 2020

Needs adviceonAmazon S3Amazon S3Amazon AthenaAmazon AthenaAmazon RedshiftAmazon Redshift

Hi all,

Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?

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Detailed Comparison

Apache Parquet
Apache Parquet
Amazon Athena
Amazon Athena

It is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language.

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Columnar storage format;Type-specific encoding; Pig integration; Cascading integration; Crunch integration; Apache Arrow integration; Apache Scrooge integration;Adaptive dictionary encoding; Predicate pushdown; Column stats
-
Statistics
Stacks
97
Stacks
519
Followers
190
Followers
840
Votes
0
Votes
49
Pros & Cons
No community feedback yet
Pros
  • 16
    Use SQL to analyze CSV files
  • 8
    Glue crawlers gives easy Data catalogue
  • 7
    Cheap
  • 6
    Query all my data without running servers 24x7
  • 4
    No data base servers yay
Integrations
Hadoop
Hadoop
Java
Java
Apache Impala
Apache Impala
Apache Thrift
Apache Thrift
Apache Hive
Apache Hive
Pig
Pig
Amazon S3
Amazon S3
Presto
Presto

What are some alternatives to Apache Parquet, Amazon Athena?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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