Overview

Amazon Athena

Stacks519

Followers840

Votes49

Apache Kylin

Stacks61

Followers236

Votes24

GitHub Stars3.8K

Forks1.5K

Amazon Athena vs Apache Kylin: What are the differences?

## Introduction
Today, we will compare Amazon Athena and Apache Kylin, two popular technologies used for processing big data.

## 1. Scalability:
Amazon Athena is a serverless interactive query service that scales automatically, handling tasks of all sizes efficiently. On the other hand, Apache Kylin is designed for extreme scalability and can process data in petabytes, making it more suitable for organizations dealing with enormous datasets.

## 2. Data Sources:
Amazon Athena primarily works with data stored in Amazon S3, while Apache Kylin supports multiple data sources such as Apache Hive, Apache HBase, and more, offering flexibility in choosing the data storage system.

## 3. Query Performance:
Amazon Athena is optimized for querying data quickly and efficiently using standard SQL, connecting directly to S3. In comparison, Apache Kylin utilizes pre-built OLAP cubes to provide sub-second query performance, making it ideal for complex analytical queries.

## 4. Cost:
Amazon Athena follows a pay-per-query pricing model, where users are billed based on the amount of data scanned during query execution. Apache Kylin, being an open-source tool, is cost-effective in terms of licensing fees but may require higher hardware resources for deployment and maintenance.

## 5. SQL Compatibility:
Both Amazon Athena and Apache Kylin support SQL queries, enabling users to leverage their existing SQL skills. However, the syntax and capabilities of SQL queries may vary slightly between the two technologies.

## 6. Deployment Complexity:
Amazon Athena's serverless architecture simplifies deployment as there is no infrastructure setup required. Conversely, Apache Kylin involves more setup and configuration to deploy the OLAP engine and build cubes, potentially requiring more expertise and resources for implementation.

In Summary, Amazon Athena and Apache Kylin differ in scalability, data sources, query performance, cost, SQL compatibility, and deployment complexity, catering to different needs in the big data processing domain.

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Advice on Amazon Athena, Apache Kylin

Aditya

Mar 13, 2021

Review

you can use aws glue service to convert you pipe format data to parquet format , and thus you can achieve data compression . Now you should choose Redshift to copy your data as it is very huge. To manage your data, you should partition your data in S3 bucket and also divide your data across the redshift cluster

220k views220k

Comments

Kevin

Co-founder at Transloadit

Dec 18, 2020

Review

Hey there, the trick to keeping costs under control is to partition. This means you split up your source files by date, and also query within dates, so that Athena only scans the few files necessary for those dates. I hope that makes sense (and I also hope I understood your question right). This article explains better https://aws.amazon.com/blogs/big-data/analyze-your-amazon-cloudfront-access-logs-at-scale/.

5.07k views5.07k

Comments

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?

522k views522k

Comments

Detailed Comparison

Amazon Athena	Apache Kylin
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.	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.
-	Extremely Fast OLAP Engine at Scale; ANSI SQL Interface on Hadoop; Interactive Query Capability; MOLAP Cube; Seamless Integration with BI Tools
Statistics
GitHub Stars -	GitHub Stars 3.8K
GitHub Forks -	GitHub Forks 1.5K
Stacks 519	Stacks 61
Followers 840	Followers 236
Votes 49	Votes 24
Pros & Cons
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	Pros 7 Star schema and snowflake schema support 5 Seamless BI integration 4 OLAP on Hadoop 3 Easy install 3 Sub-second latency on extreme large dataset
Integrations
Amazon S3 Presto	Hadoop Apache Spark Tableau PowerBI Superset

What are some alternatives to Amazon Athena, Apache Kylin?

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.

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.