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  1. Stackups
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  5. Amazon Athena vs AtScale

Amazon Athena vs AtScale

OverviewDecisionsComparisonAlternatives

Overview

AtScale
AtScale
Stacks25
Followers83
Votes0
Amazon Athena
Amazon Athena
Stacks519
Followers840
Votes49

Amazon Athena vs AtScale: What are the differences?

Introduction:

When comparing Amazon Athena and AtScale, two prominent data analytics tools, there are key differences that set them apart in terms of functionality and use cases.

  1. Data Source Integration: Amazon Athena is designed primarily for querying data stored in Amazon S3, providing a serverless interactive query service. In contrast, AtScale is a BI acceleration platform that creates a virtual cube on top of various data sources, including cloud data warehouses like Snowflake, Redshift, and BigQuery. This allows AtScale to provide a unified view of multiple data sources for faster querying and analytics.

  2. User Interface: Amazon Athena offers a simple SQL-based interface for querying data in S3, making it more suitable for data engineers and analysts familiar with SQL. On the other hand, AtScale provides a business-user-friendly interface with drag-and-drop functionality and visualization capabilities, catering to a broader range of users including business analysts and data scientists.

  3. Query Performance Optimization: While Amazon Athena optimizes query performance through partitions and columnar storage formats like Parquet, AtScale goes a step further by creating intelligent aggregates and cubes to accelerate query processing. This enables AtScale to handle complex queries more efficiently compared to Amazon Athena, especially when dealing with large datasets.

  4. Security and Governance: Amazon Athena integrates with AWS Identity and Access Management (IAM) for access control, encryption, and data security within the AWS ecosystem. In contrast, AtScale provides advanced security features like role-based access control, data masking, and audit trails, making it more suitable for enterprise-grade security and governance requirements across different data sources.

  5. Scalability and Workload Management: Amazon Athena scales automatically based on the demands of the queries and leverages the underlying AWS infrastructure for resource management. AtScale, on the other hand, offers workload management capabilities that allow administrators to prioritize and allocate resources for different queries and users, ensuring optimal performance and resource utilization in a multi-tenant environment.

  6. Pricing Model: Amazon Athena follows a pay-as-you-go pricing model based on the amount of data scanned by queries, making it cost-effective for sporadic or ad-hoc querying. In comparison, AtScale's pricing is based on the number of users and data sources connected, making it more suitable for organizations that require a unified analytics layer across diverse data platforms with predictable costs.

In Summary, Amazon Athena and AtScale differ in terms of data source integration, user interface, query performance optimization, security and governance, scalability, workload management, and pricing model, catering to different use cases and user requirements.

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Advice on AtScale, 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

AtScale
AtScale
Amazon Athena
Amazon Athena

Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.

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.

Multiple SQL-on-Hadoop Engine Support; Access Data Where it Lays; Built-in Support for Complex Data Types; Single Drop-in Gateway Node Deployment
-
Statistics
Stacks
25
Stacks
519
Followers
83
Followers
840
Votes
0
Votes
49
Pros & Cons
No community feedback yet
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
Python
Python
Amazon S3
Amazon S3
Tableau
Tableau
Power BI
Power BI
Qlik Sense
Qlik Sense
Azure Database for PostgreSQL
Azure Database for PostgreSQL
Amazon S3
Amazon S3
Presto
Presto

What are some alternatives to AtScale, Amazon Athena?

Metabase

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

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

Superset

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

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.

Cube

Cube

Cube: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.

Power BI

Power BI

It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.

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.

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