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  4. Big Data As A Service
  5. Amazon Athena vs Qubole

Amazon Athena vs Qubole

OverviewDecisionsComparisonAlternatives

Overview

Qubole
Qubole
Stacks36
Followers104
Votes67
Amazon Athena
Amazon Athena
Stacks521
Followers840
Votes49

Amazon Athena vs Qubole: What are the differences?

# Key Differences Between Amazon Athena and Qubole

Amazon Athena and Qubole are two popular query services for big data analytics in the cloud. Here are the key differences between them:

1. **Service Integration**: Amazon Athena is tightly integrated with AWS services, making it easy to query data stored in Amazon S3 and other AWS data sources. On the other hand, Qubole supports multiple cloud providers, such as AWS, Azure, and Google Cloud, providing more flexibility for users who work across different cloud platforms.

2. **Pricing Model**: Amazon Athena follows a pay-per-query pricing model, where users pay only for the data they scan with their queries. In contrast, Qubole offers a subscription-based pricing model that includes different tiers of service with varying levels of support and features. This allows users to choose a pricing plan that best fits their needs and budget.

3. **Query Optimization**: Amazon Athena optimizes queries automatically, using a query execution engine based on Presto. Qubole, on the other hand, provides advanced query optimization tools and features, such as workload-aware query routing and query caching, to improve query performance and reduce costs.

4. **Data Processing Capabilities**: Amazon Athena is primarily designed for ad-hoc querying and interactive analysis of data stored in S3. Qubole, on the other hand, offers a broader range of data processing capabilities, including data ingestion, processing, and machine learning, making it a more comprehensive solution for big data analytics workflows.

5. **User Interface**: Amazon Athena provides a simple web-based query editor for running SQL queries and viewing results. Qubole offers a more advanced user interface with features like a notebook interface, collaborative workspaces, and dashboards for data visualization, making it easier for users to work with data and share insights.

6. **Security and Compliance**: Amazon Athena integrates with AWS Identity and Access Management (IAM) for access control and supports encryption at rest and in transit for data security. Qubole provides more granular security controls, role-based access control, and compliance certifications, making it suitable for enterprise environments with strict security and compliance requirements.

In Summary, Amazon Athena is tightly integrated with AWS services and offers automatic query optimization, while Qubole supports multiple cloud providers, provides advanced query optimization tools, and offers a more comprehensive set of data processing capabilities and advanced security features for enterprise environments. 

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Advice on Qubole, 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

Qubole
Qubole
Amazon Athena
Amazon Athena

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

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.

Intuitive GUI;Optimized Hive;Improved S3 Performance;Auto Scaling;Spot Instance Pricing;Managed Clusters;Cloud Integration;Cluster Lifecycle Management
-
Statistics
Stacks
36
Stacks
521
Followers
104
Followers
840
Votes
67
Votes
49
Pros & Cons
Pros
  • 13
    Simple UI and autoscaling clusters
  • 10
    Feature to use AWS Spot pricing
  • 7
    Real-time data insights through Spark Notebook
  • 7
    Optimized Spark, Hive, Presto, Hadoop 2, HBase clusters
  • 6
    Easy to configure, deploy, and run Hadoop clusters
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
Google Compute Engine
Google Compute Engine
Microsoft Azure
Microsoft Azure
Amazon S3
Amazon S3
Presto
Presto

What are some alternatives to Qubole, 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.

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.

Snowflake

Snowflake

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.

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