Upgrade & Secure Your Future with DevOps, SRE, DevSecOps, MLOps!

We spend hours on Instagram and YouTube and waste money on coffee and fast food, but won’t spend 30 minutes a day learning skills to boost our careers.
Master in DevOps, SRE, DevSecOps & MLOps!

Learn from Guru Rajesh Kumar and double your salary in just one year.


Get Started Now!

What is Amazon Athena?

What is Amazon Athena?

Amazon Athena is a serverless, interactive query service offered by AWS that allows you to analyze data stored in Amazon S3 using standard SQL. It’s built on Presto and optimized for reading large datasets directly from S3, making it ideal for ad-hoc data analysis without the need to manage infrastructure.

Athena automatically scales resources, and you only pay for the data scanned by your queries.


Major Use Cases of Amazon Athena

  1. Ad-hoc Data Analysis
    • Quickly run SQL queries on structured, semi-structured, and unstructured data stored in S3.
    • Example: Analyze JSON logs stored in S3 to detect anomalies in user behavior.
  2. Log Analysis
    • Analyze large volumes of application, network, or security logs stored in S3 without extracting the data.
    • Example: Use Athena to query Apache access logs to monitor website traffic and detect errors.
  3. Data Lake Querying
    • Query data stored in a data lake built on S3 using SQL.
    • Example: Business teams can query and generate reports directly from the S3-based data lake without building ETL pipelines.
  4. Business Intelligence (BI) Integration
    • Connect Athena to BI tools like Qlik, Tableau, or Power BI for real-time visualization.
    • Example: Use Qlik to visualize sales performance based on data queried by Athena.
  5. Big Data Analytics and ETL
    • Analyze data from multiple sources and transform it before loading it into another system.
    • Example: Query raw IoT data and convert it into structured formats for further analysis.
  6. Security and Compliance Auditing
    • Query AWS CloudTrail logs to monitor API activities for compliance checks.
    • Example: Detect suspicious activity by querying CloudTrail logs for unauthorized access patterns.

How Qlik Works with Amazon Athena

Qlik Sense can directly integrate with Amazon Athena to perform data visualization and interactive analytics on data stored in Amazon S3.

Integration Workflow:

  1. Connect Qlik Sense to Amazon Athena:
    • Use Qlik’s ODBC connector for Athena to establish a secure connection.
  2. Query Data in S3 through Athena:
    • Perform SQL queries in Athena and retrieve the result sets into Qlik Sense.
  3. Create Dashboards and Visualizations:
    • Visualize the data in real-time with charts, graphs, and KPIs in Qlik Sense.
  4. Monitor and Analyze Big Data:
    • Use Qlik to drill down into large datasets and discover patterns.

Benefits:

  • No need to move data out of S3—Qlik reads directly from Athena.
  • Cost-effective data exploration at scale.
  • Fast, serverless querying with Athena complements Qlik’s visualization capabilities.

Features of Amazon Athena

  1. Serverless Architecture
    • No infrastructure to manage; automatically scales to handle queries.
  2. Standard SQL Support
    • Supports SQL queries for structured, semi-structured (JSON, Parquet, ORC), and unstructured data.
  3. Integration with AWS Services
    • Works seamlessly with Amazon S3, AWS Glue (for data cataloging), CloudTrail, Lambda, and QuickSight.
  4. Data Lake Integration
    • Ideal for querying large datasets in S3-based data lakes.
  5. Pay-as-You-Go Pricing
    • You pay only for the data scanned by your queries, making it cost-efficient for large-scale data analysis.
  6. Supports Multiple Data Formats
    • Works with CSV, JSON, Parquet, ORC, Avro, and other file formats in S3.
  7. Security and Encryption
    • Integrated with AWS Identity and Access Management (IAM) and supports data encryption at rest and in transit.

Best Alternatives to Amazon Athena

AlternativeDescription
Google BigQueryFully-managed, serverless data warehouse with real-time SQL querying. Strong integration with Google Cloud.
SnowflakeCloud-based data warehouse optimized for SQL analytics and data sharing across clouds.
Azure Synapse AnalyticsIntegrates big data and data warehousing services in a single platform for real-time analytics.
Presto (Open-source)Distributed SQL query engine for querying large datasets in various sources (built into Athena).
DruidHigh-performance, real-time analytics database optimized for time-series data.
Redshift Spectrum (AWS)Extends Amazon Redshift to allow querying S3 data without loading it into the Redshift cluster.

Comparison of Amazon Athena with Alternatives

ParameterAmazon AthenaGoogle BigQuerySnowflakeAzure SynapseRedshift Spectrum
ArchitectureServerlessServerlessCloud-basedIntegratedRedshift extension
SQL SupportStandard SQLStandard SQLANSI SQLT-SQL, SQLStandard SQL
Data SourceAmazon S3Google Cloud StorageMultiple (S3, Azure, GCP)Multiple (Azure, Data Lake)Amazon S3
PricingPay-per-query (per GB)Pay-per-queryUsage-basedUsage-basedUsage-based
Best Use CaseAd-hoc S3 queryingReal-time analyticsData warehouseData warehousing + big dataData lake analytics

Which Tool Should You Choose?

  • Amazon Athena: Best for S3-based data lakes and ad-hoc analysis without managing infrastructure.
  • Google BigQuery: If you’re on Google Cloud and need real-time analytics on large datasets.
  • Snowflake: Ideal for multi-cloud data warehousing and seamless data sharing.
  • Azure Synapse: Great for Microsoft Azure users integrating data warehousing and big data processing.
  • Redshift Spectrum: If you’re already using Amazon Redshift and want to extend querying to S3 data.

What is Presto in the Context of Amazon Athena?

Presto is an open-source distributed SQL query engine designed for fast and interactive querying of large datasets. In the context of Amazon Athena, Presto serves as the underlying query engine that powers Athena’s ability to run SQL queries on data stored in Amazon S3.

Amazon Athena uses Presto under the hood to process SQL queries, enabling ad-hoc analysis of structured and semi-structured data (like JSON, Parquet, ORC, and Avro) without requiring any data loading or complex ETL processes.

Subscribe
Notify of
guest
0 Comments
Newest
Oldest Most Voted
Inline Feedbacks
View all comments

Certification Courses

DevOpsSchool has introduced a series of professional certification courses designed to enhance your skills and expertise in cutting-edge technologies and methodologies. Whether you are aiming to excel in development, security, or operations, these certifications provide a comprehensive learning experience. Explore the following programs:

DevOps Certification, SRE Certification, and DevSecOps Certification by DevOpsSchool

Explore our DevOps Certification, SRE Certification, and DevSecOps Certification programs at DevOpsSchool. Gain the expertise needed to excel in your career with hands-on training and globally recognized certifications.

0
Would love your thoughts, please comment.x
()
x