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  1. Stackups
  2. Application & Data
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  4. Databases
  5. Amazon Athena vs Vertica

Amazon Athena vs Vertica

OverviewDecisionsComparisonAlternatives

Overview

Vertica
Vertica
Stacks88
Followers120
Votes16
Amazon Athena
Amazon Athena
Stacks519
Followers840
Votes49

Amazon Athena vs Vertica: What are the differences?

  1. Data Storage: Amazon Athena is a query service that allows users to analyze data stored in Amazon S3 without the need for infrastructure management, while Vertica is a high-performance analytics database designed for data warehouses.

  2. Scalability: Amazon Athena is designed to scale automatically to accommodate query workloads, while Vertica requires manual scaling based on hardware resources and system requirements.

  3. Cost Structure: Amazon Athena follows a pay-per-query pricing model, where users are charged based on the amount of data scanned by each query, whereas Vertica typically involves upfront costs for licensing and maintenance.

  4. Query Performance: Amazon Athena is optimized for querying large datasets stored in S3 using standard SQL, whereas Vertica is optimized for high-speed analytical queries on structured data.

  5. Data Ingestion: Amazon Athena does not require data to be loaded into the service as it queries data directly from S3, while Vertica involves the loading of data into its database for analysis.

  6. Ecosystem Integration: Amazon Athena integrates seamlessly with other AWS services such as Glue for data cataloging, whereas Vertica offers integration with various third-party tools and technologies for data management and analytics.

In Summary, Amazon Athena and Vertica differ in terms of data storage, scalability, cost structure, query performance, data ingestion, and ecosystem integration.

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Advice on Vertica, 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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Comments

Detailed Comparison

Vertica
Vertica
Amazon Athena
Amazon Athena

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

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.

Analyze All of Your Data. No longer move data or settle for siloed views;Achieve Scale and Performance;Fear of growing data volumes and users is a thing of the past;Future-Proof Your Analytics
-
Statistics
Stacks
88
Stacks
519
Followers
120
Followers
840
Votes
16
Votes
49
Pros & Cons
Pros
  • 3
    Shared nothing or shared everything architecture
  • 1
    Pre-Aggregation for Cubes (LAPS)
  • 1
    Freedom from Underlying Storage
  • 1
    All You Need for IoT, Clickstream or Geospatial
  • 1
    Flexible architecture suits nearly any project
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
Oracle
Oracle
Golang
Golang
MongoDB
MongoDB
MySQL
MySQL
Sass
Sass
Mode
Mode
PowerBI
PowerBI
Tableau
Tableau
Talend
Talend
Amazon S3
Amazon S3
Presto
Presto

What are some alternatives to Vertica, Amazon Athena?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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