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  1. Stackups
  2. Application & Data
  3. NoSQL Databases
  4. NOSQL Database As A Service
  5. Amazon Athena vs Google Cloud Bigtable

Amazon Athena vs Google Cloud Bigtable

OverviewDecisionsComparisonAlternatives

Overview

Google Cloud Bigtable
Google Cloud Bigtable
Stacks173
Followers363
Votes25
Amazon Athena
Amazon Athena
Stacks521
Followers840
Votes49

Amazon Athena vs Google Cloud Bigtable: What are the differences?

Introduction

Amazon Athena and Google Cloud Bigtable are two popular data management and analytics services offered by Amazon Web Services (AWS) and Google Cloud Platform (GCP) respectively. Both services have different features and capabilities, making them suitable for different use cases. In this comparison, we will highlight the key differences between Amazon Athena and Google Cloud Bigtable.

  1. Data Storage and Processing Model:

    • Amazon Athena is a serverless interactive query service that allows users to analyze data directly from Amazon S3 using standard SQL. It is designed for ad-hoc querying and can handle a wide variety of data formats.
    • Google Cloud Bigtable, on the other hand, is a NoSQL wide-column store that is optimized for low-latency, high-throughput workloads. It is suitable for applications that require real-time analytics and high-performance data processing.
  2. Underlying Infrastructure:

    • Amazon Athena is built on top of AWS infrastructure, leveraging the scalability and reliability of Amazon S3 and Amazon EMR. It allows users to scale their queries based on their requirements.
    • Google Cloud Bigtable is built on top of Google Cloud Platform, utilizing the underlying infrastructure for distributed storage and processing. It offers high availability and data replication across multiple regions.
  3. Data Modeling and Schema:

    • Amazon Athena does not require users to define schemas or create tables. It can directly query data stored in Amazon S3 without any predefined structure.
    • Google Cloud Bigtable requires users to define column families and column qualifiers for data modeling. It follows a schema-based approach and requires schema definition before ingesting data.
  4. Indexing and Query Performance:

    • Amazon Athena supports indexing of data in Amazon S3 using Apache Hive Metastore. This enables faster query execution by leveraging indexing techniques.
    • Google Cloud Bigtable does not support indexing as it is designed for high-throughput workloads. It provides fast read and write performance by utilizing a distributed storage system.
  5. Pricing Model:

    • Amazon Athena pricing is based on the amount of data scanned during query execution. Users pay per TB of data scanned.
    • Google Cloud Bigtable pricing is based on the amount of storage and the number of read and write operations. Users pay for storage per month and for the number of operations performed.
  6. Integration with other Services:

    • Amazon Athena integrates well with other AWS services such as Amazon CloudWatch, AWS Glue, and AWS Lambda. It can also be used with popular BI tools and data visualization platforms.
    • Google Cloud Bigtable integrates tightly with other GCP services like BigQuery, Dataflow, and Data Studio. It provides seamless integration for building data pipeline and analytics workflows.

In summary, Amazon Athena is a serverless query service optimized for ad-hoc querying on data stored in Amazon S3, while Google Cloud Bigtable is a NoSQL wide-column store designed for high-performance, real-time analytics. They have differences in their data storage and processing models, underlying infrastructure, data modeling and schema, indexing and query performance, pricing models, and integration with other services.

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Advice on Google Cloud Bigtable, 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

Google Cloud Bigtable
Google Cloud Bigtable
Amazon Athena
Amazon Athena

Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.

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.

Unmatched Performance: Single-digit millisecond latency and over 2X the performance per dollar of unmanaged NoSQL alternatives.;Open Source Interface: Because Cloud Bigtable is accessed through the HBase API, it is natively integrated with much of the existing big data and Hadoop ecosystem and supports Google’s big data products. Additionally, data can be imported from or exported to existing HBase clusters through simple bulk ingestion tools using industry-standard formats.;Low Cost: By providing a fully managed service and exceptional efficiency, Cloud Bigtable’s total cost of ownership is less than half the cost of its direct competition.;Security: Cloud Bigtable is built with a replicated storage strategy, and all data is encrypted both in-flight and at rest.;Simplicity: Creating or reconfiguring a Cloud Bigtable cluster is done through a simple user interface and can be completed in less than 10 seconds. As data is put into Cloud Bigtable the backing storage scales automatically, so there’s no need to do complicated estimates of capacity requirements.;Maturity: Over the past 10+ years, Bigtable has driven Google’s most critical applications. In addition, the HBase API is a industry-standard interface for combined operational and analytical workloads.
-
Statistics
Stacks
173
Stacks
521
Followers
363
Followers
840
Votes
25
Votes
49
Pros & Cons
Pros
  • 11
    High performance
  • 9
    Fully managed
  • 5
    High scalability
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
Heroic
Heroic
Hadoop
Hadoop
Apache Spark
Apache Spark
Amazon S3
Amazon S3
Presto
Presto

What are some alternatives to Google Cloud Bigtable, Amazon Athena?

Amazon DynamoDB

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

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.

Azure Cosmos DB

Azure Cosmos DB

Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.

Cloud Firestore

Cloud Firestore

Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale.

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.

Cloudant

Cloudant

Cloudant’s distributed database as a service (DBaaS) allows developers of fast-growing web and mobile apps to focus on building and improving their products, instead of worrying about scaling and managing databases on their own.

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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