Amazon Athena vs Apache Impala

Overview

Apache Impala

Stacks145

Followers301

Votes18

GitHub Stars34

Forks33

Amazon Athena

Stacks519

Followers840

Votes49

Amazon Athena vs Apache Impala: What are the differences?

Introduction

Amazon Athena and Apache Impala are both SQL query engines that are designed to analyze data stored in various formats and provide fast query performance. However, there are several key differences between the two that make them distinct in terms of features, architecture, and use cases.

Data Storage: One of the primary differences between Amazon Athena and Apache Impala is their data storage options. Athena is built on Amazon S3, which means it is suitable for analyzing data stored in S3 buckets. On the other hand, Impala is integrated with Apache Hadoop, allowing it to access data stored in Hadoop Distributed File System (HDFS), HBase, and other Hadoop-compatible file systems.
Data Format: Another significant difference is the supported data formats. Athena can query data in various formats like CSV, JSON, Parquet, and ORC. It provides schema-on-read functionality, which enables querying data without a predefined schema. In contrast, Impala primarily supports Parquet and ORC file formats with a defined schema, making it more suitable for structured data analysis.
Processing Model: Athena follows a serverless and pay-per-query processing model, where it automatically scales resources up or down based on the query workload. Users are only charged for the data scanned during the query execution. Conversely, Impala is a distributed query processing engine that requires a cluster of machines for high-performance analytics. It provides faster query response times but requires manual cluster management.
Integration with Ecosystem: While both Athena and Impala integrate well with their respective ecosystems, Athena has better integration with other Amazon Web Services (AWS) services, making it an ideal choice for AWS-centric deployments. It seamlessly integrates with AWS Glue for metadata catalog and AWS CloudTrail for query auditing. Impala, on the other hand, integrates with the broader Apache Hadoop ecosystem, allowing integration with tools like HDFS, Hive, HBase, and Sentry.
Security and Access Control: Athena offers out-of-the-box integration with AWS Identity and Access Management (IAM) for access control, allowing fine-grained permission management. It also supports column-level encryption for enhanced data security. In comparison, Impala integrates with Apache Sentry for role-based access control and Kerberos for authentication, offering similar security features but with Hadoop-specific implementations.
Query Optimization: As for query optimization, Impala has a more sophisticated query optimizer compared to Athena. Impala uses techniques like predicate pushdown, join reordering, and dynamic partition pruning to optimize query execution plans. Athena, being serverless, relies on automatic query optimization, which may not be as fine-grained.

In summary, Amazon Athena is a serverless, pay-per-query SQL query engine that excels in analyzing data stored in Amazon S3 with strong integration with AWS services. Apache Impala, on the other hand, is a distributed query processing engine that integrates with the broader Apache Hadoop ecosystem and provides faster query response times with manual cluster management.

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Advice on Apache Impala, 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

Apache Impala	Amazon Athena
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.	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.
Do BI-style Queries on Hadoop;Unify Your Infrastructure;Implement Quickly;Count on Enterprise-class Security;Retain Freedom from Lock-in;Expand the Hadoop User-verse	-
Statistics
GitHub Stars 34	GitHub Stars -
GitHub Forks 33	GitHub Forks -
Stacks 145	Stacks 519
Followers 301	Followers 840
Votes 18	Votes 49
Pros & Cons
Pros 11 Super fast 1 Load Balancing 1 Open Sourse 1 High Performance 1 Distributed	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 Mode Redash Apache Kudu	Amazon S3 Presto

What are some alternatives to Apache Impala, 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.

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 Impala: What are the differences?

Introduction

Data Storage: One of the primary differences between Amazon Athena and Apache Impala is their data storage options. Athena is built on Amazon S3, which means it is suitable for analyzing data stored in S3 buckets. On the other hand, Impala is integrated with Apache Hadoop, allowing it to access data stored in Hadoop Distributed File System (HDFS), HBase, and other Hadoop-compatible file systems.
Data Format: Another significant difference is the supported data formats. Athena can query data in various formats like CSV, JSON, Parquet, and ORC. It provides schema-on-read functionality, which enables querying data without a predefined schema. In contrast, Impala primarily supports Parquet and ORC file formats with a defined schema, making it more suitable for structured data analysis.
Processing Model: Athena follows a serverless and pay-per-query processing model, where it automatically scales resources up or down based on the query workload. Users are only charged for the data scanned during the query execution. Conversely, Impala is a distributed query processing engine that requires a cluster of machines for high-performance analytics. It provides faster query response times but requires manual cluster management.
Integration with Ecosystem: While both Athena and Impala integrate well with their respective ecosystems, Athena has better integration with other Amazon Web Services (AWS) services, making it an ideal choice for AWS-centric deployments. It seamlessly integrates with AWS Glue for metadata catalog and AWS CloudTrail for query auditing. Impala, on the other hand, integrates with the broader Apache Hadoop ecosystem, allowing integration with tools like HDFS, Hive, HBase, and Sentry.
Security and Access Control: Athena offers out-of-the-box integration with AWS Identity and Access Management (IAM) for access control, allowing fine-grained permission management. It also supports column-level encryption for enhanced data security. In comparison, Impala integrates with Apache Sentry for role-based access control and Kerberos for authentication, offering similar security features but with Hadoop-specific implementations.
Query Optimization: As for query optimization, Impala has a more sophisticated query optimizer compared to Athena. Impala uses techniques like predicate pushdown, join reordering, and dynamic partition pruning to optimize query execution plans. Athena, being serverless, relies on automatic query optimization, which may not be as fine-grained.

Amazon Athena vs Apache Impala

Overview

Amazon Athena vs Apache Impala: What are the differences?