Amazon Athena vs Apache Spark

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Amazon Athena

Stacks521

Followers840

Votes49

Amazon Athena vs Apache Spark: What are the differences?

Amazon Athena and Apache Spark are two popular data processing tools. Let's discuss the key differences between them.

Data processing model: Amazon Athena is a query service that enables users to analyze data in Amazon S3 using standard SQL queries. It is serverless and doesn't require any infrastructure setup or management. On the other hand, Apache Spark is a distributed processing framework that allows for the parallel processing of big datasets across a cluster of computers. It provides a wide range of APIs for data processing, including batch, interactive, and real-time analytics.
Scalability and Performance: With Amazon Athena, the performance scales automatically based on the query complexity and data size, as it leverages the underlying power of Amazon S3 and Presto engine. However, when dealing with large datasets or complex workflows, Apache Spark provides better scalability as it can distribute the workload across multiple nodes in a cluster, resulting in faster processing times.
Data Sources: Amazon Athena primarily works with data stored in Amazon S3, allowing users to perform queries directly on files in CSV, JSON, Parquet, or other formats. In contrast, Apache Spark has a more extensive range of data source connectors, enabling it to interact with various data storage systems like Hadoop Distributed File System (HDFS), HBase, Cassandra, and more.
Computational Model: Amazon Athena is a serverless, on-demand service where users are only billed based on the queries executed and the amount of data scanned. It automatically takes care of query execution, maintaining metadata, and scaling resources. In contrast, Apache Spark requires users to set up dedicated clusters, manage resources, and deploy applications. Spark also offers the flexibility to perform complex data manipulations and transformations using its Resilient Distributed Dataset (RDD) abstraction.
Real-Time Processing: While both Amazon Athena and Apache Spark can handle batch processing, Apache Spark has a specific focus on real-time processing. Spark provides various streaming APIs (such as Structured Streaming) that enable near-real-time data processing and analytics. This capability makes Apache Spark suitable for use cases requiring low-latency data processing and real-time analytics.
Ecosystem and Integration: Apache Spark has a vast ecosystem with support for various machine learning libraries (MLlib), graph processing (GraphX), and stream processing (Spark Streaming). It seamlessly integrates with other popular big data tools like Apache Hadoop, Apache Hive, and Apache Kafka. In comparison, Amazon Athena offers a more focused ecosystem around querying data in Amazon S3, with limited direct integrations.

In summary, Amazon Athena is a serverless, query-based service specifically designed for analyzing data stored in Amazon S3, offering easy setup and scalability. On the other hand, Apache Spark is a distributed processing framework that allows for parallel data processing, provides a wider range of data source connectors, and offers more extensive options for real-time processing and integration with various big data tools.

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Advice on Apache Spark, Amazon Athena

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

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k

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/.

5.08k views5.08k

Comments

Detailed Comparison

Apache Spark	Amazon Athena
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.	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.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	-
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 521
Followers 3.5K	Followers 840
Votes 140	Votes 49
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	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 Apache Spark, Amazon Athena?

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.

Apache Kudu

A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.

Related Comparisons

Amazon Athena vs Apache Spark: What are the differences?

Amazon Athena and Apache Spark are two popular data processing tools. Let's discuss the key differences between them.

Data processing model: Amazon Athena is a query service that enables users to analyze data in Amazon S3 using standard SQL queries. It is serverless and doesn't require any infrastructure setup or management. On the other hand, Apache Spark is a distributed processing framework that allows for the parallel processing of big datasets across a cluster of computers. It provides a wide range of APIs for data processing, including batch, interactive, and real-time analytics.
Scalability and Performance: With Amazon Athena, the performance scales automatically based on the query complexity and data size, as it leverages the underlying power of Amazon S3 and Presto engine. However, when dealing with large datasets or complex workflows, Apache Spark provides better scalability as it can distribute the workload across multiple nodes in a cluster, resulting in faster processing times.
Data Sources: Amazon Athena primarily works with data stored in Amazon S3, allowing users to perform queries directly on files in CSV, JSON, Parquet, or other formats. In contrast, Apache Spark has a more extensive range of data source connectors, enabling it to interact with various data storage systems like Hadoop Distributed File System (HDFS), HBase, Cassandra, and more.
Computational Model: Amazon Athena is a serverless, on-demand service where users are only billed based on the queries executed and the amount of data scanned. It automatically takes care of query execution, maintaining metadata, and scaling resources. In contrast, Apache Spark requires users to set up dedicated clusters, manage resources, and deploy applications. Spark also offers the flexibility to perform complex data manipulations and transformations using its Resilient Distributed Dataset (RDD) abstraction.
Real-Time Processing: While both Amazon Athena and Apache Spark can handle batch processing, Apache Spark has a specific focus on real-time processing. Spark provides various streaming APIs (such as Structured Streaming) that enable near-real-time data processing and analytics. This capability makes Apache Spark suitable for use cases requiring low-latency data processing and real-time analytics.
Ecosystem and Integration: Apache Spark has a vast ecosystem with support for various machine learning libraries (MLlib), graph processing (GraphX), and stream processing (Spark Streaming). It seamlessly integrates with other popular big data tools like Apache Hadoop, Apache Hive, and Apache Kafka. In comparison, Amazon Athena offers a more focused ecosystem around querying data in Amazon S3, with limited direct integrations.

Amazon Athena vs Apache Spark

Overview