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  1. Stackups
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  4. Big Data As A Service
  5. Amazon Athena vs Snowflake

Amazon Athena vs Snowflake

OverviewDecisionsComparisonAlternatives

Overview

Snowflake
Snowflake
Stacks1.2K
Followers1.2K
Votes27
Amazon Athena
Amazon Athena
Stacks521
Followers840
Votes49

Amazon Athena vs Snowflake: What are the differences?

  1. Data Warehouse Architecture: Amazon Athena is a serverless and scalable query service that allows querying data directly from Amazon S3, without the need for infrastructure management. On the other hand, Snowflake is a cloud-based data warehouse platform that provides a multi-cluster shared data architecture, enabling query processing across multiple nodes for better performance.
  2. Data Processing: Athena supports querying structured, semi-structured, and unstructured data using a SQL-like query language. It provides fast and linearly scalable queries with automatic scaling of compute resources. Snowflake, on the other hand, supports both structured and semi-structured data, with advanced data processing capabilities like semi-structured data optimization, result caching, and automatic query optimization.
  3. Concurrency: Athena offers high concurrency with a limit of 20 queries per account, while Snowflake provides higher concurrency with up to 2000 queries per account.
  4. Data Partitioning: Athena partitions data based on the underlying folder structure in Amazon S3, allowing for efficient querying of large datasets. Snowflake enables partitioning based on user-defined columns, which provides more flexibility in managing and optimizing data partitions.
  5. Pricing Model: Athena follows a pay-per-query pricing model, where users only pay for the queries they execute. Snowflake, on the other hand, offers different pricing models, including on-demand and prepaid options based on compute and storage usage.
  6. Data Integration: Amazon Athena integrates seamlessly with other AWS services, such as AWS Glue for data cataloging and AWS Lambda for automation. Snowflake also offers various integrations and connectors with popular data integration platforms, enabling easy data ingestion and integration workflows.

In Summary, Amazon Athena is a serverless and scalable query service designed for querying data directly from Amazon S3, while Snowflake is a cloud-based data warehouse platform with advanced data processing capabilities and a multi-cluster shared data architecture.

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Advice on Snowflake, 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

Snowflake
Snowflake
Amazon Athena
Amazon Athena

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

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.

Statistics
Stacks
1.2K
Stacks
521
Followers
1.2K
Followers
840
Votes
27
Votes
49
Pros & Cons
Pros
  • 7
    Public and Private Data Sharing
  • 4
    Good Performance
  • 4
    Multicloud
  • 4
    User Friendly
  • 3
    Great Documentation
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
Apache Spark
Apache Spark
Node.js
Node.js
Looker
Looker
Periscope
Periscope
Mode
Mode
Amazon S3
Amazon S3
Presto
Presto

What are some alternatives to Snowflake, Amazon Athena?

Google BigQuery

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

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.

Amazon Redshift

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

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.

Altiscale

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

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