AWS Certified Data Analytics Specialty 2021 - Hands-On! - Introduction to Amazon Athena

The video tutorial introduces Amazon Athena, a serverless interactive query service for S3 data lakes. It explains how Athena uses Presto under the hood and supports various data formats like CSV, JSON, ORC, Parquet, and Avro. The tutorial discusses the advantages of columnar and splittable formats for efficient data querying. It highlights Athena's flexibility in handling structured and semi-structured data, its integration with AWS Glue, and its use cases, such as querying web logs and staging data for Redshift. The video also covers Athena's integration with tools like Jupyter, Zeppelin, and QuickSight.

What is a key feature of Amazon Athena that makes it appealing for users?

• It requires extensive server management.

• It only supports structured data.

• It is limited to small datasets.

• It is a serverless service.

Which data format is known for being human-readable and easy to edit in a text editor?

• CSV

• Avro

• Parquet

• ORC

What is a characteristic of columnar data formats like ORC and Parquet?

• They organize data by columns.

• They are human-readable.

• They organize data by rows.

• They are not splittable.

How does Athena handle data that is not structured?

• It only works with structured data.

• It requires manual data structuring.

• It uses AWS Glue to impart structure.

• It cannot process unstructured data.

Which tool can be integrated with Athena for data visualization?

• AWS Lambda

• Amazon QuickSight

• Amazon S3

• AWS CloudTrail