Amazon Athena vs Hue

Overview

Hue

Stacks55

Followers98

Votes0

Amazon Athena

Stacks521

Followers840

Votes49

Amazon Athena vs Hue: What are the differences?

Introduction

In this article, we will discuss the key differences between Amazon Athena and Hue. These two tools are commonly used for data analytics and processing in the big data domain. Understanding their differences will help users choose the right tool for their specific requirements.

Pricing model: Amazon Athena is a serverless, pay-as-you-go service that charges based on the amount of data scanned during query execution. On the other hand, Hue is an open-source web-based interface that is free to use. The pricing model of Athena may be more suitable for users who want to scale the cost based on their usage.
Data source compatibility: Amazon Athena supports querying data stored in Amazon S3 using standard SQL queries, making it compatible with a wide range of data formats. Hue, on the other hand, can connect to various data sources such as Apache Hive, Impala, and more. This difference in compatibility allows Athena users to leverage the benefits of Amazon S3, while Hue users can access data from multiple sources.
User interface: Hue provides a comprehensive user interface that allows users to interact with big data tools through a single interface. It offers a visual query builder, file browser, job scheduler, and other features. Amazon Athena, on the other hand, primarily relies on query execution through SQL queries submitted via the AWS Management Console or API. The user interface provided by Hue may be more convenient for users who prefer a visual and interactive experience.
Integration with other AWS services: Amazon Athena seamlessly integrates with other AWS services, such as AWS Glue for data cataloging and AWS S3 for data storage. This integration enables users to build end-to-end data pipelines and leverage the full suite of AWS services. Hue, being an open-source tool, may require additional configuration and setup to integrate with AWS services. This difference in integration capabilities can influence the choice of tool depending on the user's AWS ecosystem.
Data governance and security: Amazon Athena offers fine-grained access control and encryption options for securing data and complying with industry regulations. Moreover, it integrates with AWS Identity and Access Management (IAM) for user authentication and authorization. While Hue does provide security and authentication features, its capabilities may be more limited compared to the extensive security features of Amazon Athena.
Support and documentation: Amazon Athena is a managed service provided by AWS, which means users can take advantage of AWS's support and documentation resources. On the other hand, Hue being an open-source tool, relies on community support and may not have the same level of professional assistance available. This difference in support and documentation can influence the level of assistance and ease of troubleshooting for users.

In Summary, Amazon Athena and Hue differ in terms of their pricing model, data source compatibility, user interface, integration with other AWS services, data governance and security features, as well as support and documentation. Choosing between these tools depends on specific requirements, preferences, and the existing infrastructure and ecosystem of the user.

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Advice on Hue, Amazon Athena

Pavithra

Mar 12, 2020

Needs adviceon

Amazon S3

Amazon Athena

Amazon 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

Hue	Amazon Athena
It is open source and lets regular users import their big data, query it, search it, visualize it and build dashboards on top of it, all from their browser.	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.
Statistics
Stacks 55	Stacks 521
Followers 98	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
No integrations available	Amazon S3 Presto

What are some alternatives to Hue, Amazon Athena?

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Distributed SQL Query Engine for Big Data

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Related Comparisons

Amazon Athena vs Hue: What are the differences?

Introduction

Pricing model: Amazon Athena is a serverless, pay-as-you-go service that charges based on the amount of data scanned during query execution. On the other hand, Hue is an open-source web-based interface that is free to use. The pricing model of Athena may be more suitable for users who want to scale the cost based on their usage.
Data source compatibility: Amazon Athena supports querying data stored in Amazon S3 using standard SQL queries, making it compatible with a wide range of data formats. Hue, on the other hand, can connect to various data sources such as Apache Hive, Impala, and more. This difference in compatibility allows Athena users to leverage the benefits of Amazon S3, while Hue users can access data from multiple sources.
User interface: Hue provides a comprehensive user interface that allows users to interact with big data tools through a single interface. It offers a visual query builder, file browser, job scheduler, and other features. Amazon Athena, on the other hand, primarily relies on query execution through SQL queries submitted via the AWS Management Console or API. The user interface provided by Hue may be more convenient for users who prefer a visual and interactive experience.
Integration with other AWS services: Amazon Athena seamlessly integrates with other AWS services, such as AWS Glue for data cataloging and AWS S3 for data storage. This integration enables users to build end-to-end data pipelines and leverage the full suite of AWS services. Hue, being an open-source tool, may require additional configuration and setup to integrate with AWS services. This difference in integration capabilities can influence the choice of tool depending on the user's AWS ecosystem.
Data governance and security: Amazon Athena offers fine-grained access control and encryption options for securing data and complying with industry regulations. Moreover, it integrates with AWS Identity and Access Management (IAM) for user authentication and authorization. While Hue does provide security and authentication features, its capabilities may be more limited compared to the extensive security features of Amazon Athena.
Support and documentation: Amazon Athena is a managed service provided by AWS, which means users can take advantage of AWS's support and documentation resources. On the other hand, Hue being an open-source tool, relies on community support and may not have the same level of professional assistance available. This difference in support and documentation can influence the level of assistance and ease of troubleshooting for users.

Amazon Athena vs Hue

Overview