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  1. Stackups
  2. Application & Data
  3. Infrastructure as a Service
  4. Cluster Management
  5. Amazon Athena vs Apache Aurora

Amazon Athena vs Apache Aurora

OverviewDecisionsComparisonAlternatives

Overview

Apache Aurora
Apache Aurora
Stacks69
Followers96
Votes0
Amazon Athena
Amazon Athena
Stacks521
Followers840
Votes49

Amazon Athena vs Apache Aurora: What are the differences?

Introduction:

Amazon Athena and Apache Aurora are two popular data processing and management tools used in the computing world. Both serve unique purposes and offer distinct features to meet various user needs.

  1. Deployment and Use Case: Amazon Athena is a serverless interactive query service that enables users to analyze data in Amazon S3 using standard SQL. It is commonly used for ad-hoc querying and analysis of large amounts of data. On the other hand, Apache Aurora is a mesos framework for long-running services and cron jobs, ideal for service-oriented architectures and scalable applications.

  2. Cost Structure: Amazon Athena follows a pay-as-you-go pricing model, where users are charged only for the amount of data scanned by their queries. In contrast, Apache Aurora is open-source software, meaning there are no licensing fees associated with its deployment. However, users may incur costs related to infrastructure and maintenance.

  3. Scaling Capabilities: Amazon Athena is designed to automatically scale query processing based on the amount of data being scanned, allowing users to efficiently handle varying workloads. Apache Aurora, on the other hand, requires users to configure and manage the scaling of their services manually, providing greater control over resource allocation.

  4. Data Source Integration: Amazon Athena is specifically optimized for querying data stored in Amazon S3, offering seamless integration with a wide range of AWS services. In comparison, Apache Aurora can be used with various storage systems and databases, providing more flexibility in data source connectivity.

  5. Resource Management: Amazon Athena handles resource management internally, allowing users to focus solely on querying data without the need for infrastructure provisioning. Apache Aurora, on the other hand, requires users to configure resource allocation and scheduling for their services, requiring additional setup and maintenance.

  6. Community Support and Development: Amazon Athena is a fully managed service by AWS, receiving continuous updates and enhancements from a dedicated development team. Apache Aurora, being open-source, benefits from a community of contributors and developers who actively improve the software and provide support to users.

In Summary, Amazon Athena and Apache Aurora differ in deployment use cases, cost structure, scaling capabilities, data source integration, resource management, and community support, catering to distinct user needs and preferences in the data processing and management domain.

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Advice on Apache Aurora, 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 Aurora
Apache Aurora
Amazon Athena
Amazon Athena

Apache Aurora is a service scheduler that runs on top of Mesos, enabling you to run long-running services that take advantage of Mesos' scalability, fault-tolerance, and resource isolation.

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.

Deployment and scheduling of jobs;The abstraction a “job” to bundle and manage Mesos tasks;A rich DSL to define services;Health checking;Failure domain diversity;Instant provisioning
-
Statistics
Stacks
69
Stacks
521
Followers
96
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
Apache Mesos
Apache Mesos
Vagrant
Vagrant
Amazon S3
Amazon S3
Presto
Presto

What are some alternatives to Apache Aurora, Amazon Athena?

Apache Spark

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

Presto

Distributed SQL Query Engine for Big Data

Apache Flink

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

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

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.

Nomad

Nomad

Nomad is a cluster manager, designed for both long lived services and short lived batch processing workloads. Developers use a declarative job specification to submit work, and Nomad ensures constraints are satisfied and resource utilization is optimized by efficient task packing. Nomad supports all major operating systems and virtualized, containerized, or standalone applications.

Apache Mesos

Apache Mesos

Apache Mesos is a cluster manager that simplifies the complexity of running applications on a shared pool of servers.

Apache Kylin

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

Splunk

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

Apache Impala

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.

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