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  1. Stackups
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  5. Amazon Athena vs Delta Lake

Amazon Athena vs Delta Lake

OverviewDecisionsComparisonAlternatives

Overview

Amazon Athena
Amazon Athena
Stacks519
Followers840
Votes49
Delta Lake
Delta Lake
Stacks105
Followers315
Votes0
GitHub Stars8.4K
Forks1.9K

Amazon Athena vs Delta Lake: What are the differences?

Introduction

Amazon Athena and Delta Lake are both data storage and processing solutions that are widely used in the industry. While they have similarities in terms of their ability to handle large volumes of data and execute queries, there are key differences that set them apart. In this article, we will highlight the main differences between Amazon Athena and Delta Lake.

  1. Query Engine vs Storage Format: The fundamental difference between Amazon Athena and Delta Lake lies in their core functionality. Amazon Athena is primarily a query engine that allows you to run SQL queries on data stored in different formats like CSV, JSON, or Parquet. On the other hand, Delta Lake is a storage format that enables ACID transactions and schema enforcement, making it well-suited for data lakes and data warehouses.

  2. Data Partitioning: Partitioning data is a crucial technique for improving query performance on large datasets. When it comes to data partitioning, Amazon Athena relies on the directory structure of the underlying data to optimize queries. In contrast, Delta Lake provides an optimized mechanism for partitioning data based on defined columns, which can significantly enhance query execution time.

  3. Data Versioning and Concurrency Control: Versioning and concurrency control are essential features for maintaining data integrity in a multi-user environment. Delta Lake offers built-in support for data versioning and concurrency control, allowing multiple users to concurrently read and write data without conflicts. Amazon Athena, however, does not provide native support for these features and relies on external mechanisms for achieving similar functionality.

  4. Schema Evolution: As data evolves over time, it often requires schema changes to accommodate new fields or alterations in existing ones. Delta Lake offers robust schema evolution capabilities, allowing you to seamlessly evolve schemas across different versions of data. In contrast, Amazon Athena expects the data to adhere to a fixed schema, and any schema changes require manual adjustments.

  5. Performance Optimization: Both Amazon Athena and Delta Lake strive to provide optimal query performance, but they employ different techniques. Amazon Athena leverages query optimizations and parallel execution to achieve fast query speeds. Delta Lake, on the other hand, utilizes various performance optimization techniques, such as data skipping and caching, to boost query execution and reduce data scans.

  6. Integration with Data Ecosystem: Delta Lake integrates well with various data processing frameworks, such as Apache Spark, allowing you to leverage advanced data processing capabilities. Amazon Athena, on the other hand, is tightly integrated with the AWS ecosystem and seamlessly interacts with other AWS services, making it an ideal choice for users heavily reliant on AWS infrastructure.

In summary, Amazon Athena primarily serves as a query engine while Delta Lake is a storage format with built-in features like ACID transactions, schema enforcement, and versioning. Delta Lake offers more advanced capabilities such as optimized data partitioning, schema evolution, and concurrency control. On the other hand, Amazon Athena excels in integration with the AWS ecosystem and provides fast query performance through optimizations and parallel execution.

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Advice on Amazon Athena, Delta Lake

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

Amazon Athena
Amazon Athena
Delta Lake
Delta Lake

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.

An open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads.

-
ACID Transactions; Scalable Metadata Handling; Time Travel (data versioning); Open Format; Unified Batch and Streaming Source and Sink; Schema Enforcement; Schema Evolution; 100% Compatible with Apache Spark API
Statistics
GitHub Stars
-
GitHub Stars
8.4K
GitHub Forks
-
GitHub Forks
1.9K
Stacks
519
Stacks
105
Followers
840
Followers
315
Votes
49
Votes
0
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
No community feedback yet
Integrations
Amazon S3
Amazon S3
Presto
Presto
Apache Spark
Apache Spark
Hadoop
Hadoop
Amazon S3
Amazon S3

What are some alternatives to Amazon Athena, Delta Lake?

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.

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.

Vertica

Vertica

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

Azure Synapse

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.

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