Athena Query Optimization

A company is using Athena to analyze data in S3. They notice that certain queries, especially those run repeatedly on large datasets, take a significant amount of time. The data in S3 does not change frequently. Which optimization technique would be MOST effective in reducing the query time and cost?

A data analyst needs to query data from multiple, disparate data sources including Amazon RDS, DocumentDB, and S3. The goal is to create a unified view of customer profile data. Which Athena feature would BEST facilitate this?

A financial company uses Athena to perform analytics on their sales data stored in S3. To improve query performance, they decide to implement partitioning. However, manually managing these partitions becomes time-consuming as the data grows. Which feature can help automate the partition management process and speed up queries?

An organization has multiple teams using Athena to query data in S3. Each team has different use cases, access requirements, and cost considerations. What is the MOST suitable Athena feature to isolate queries, control query execution settings, and manage costs for each team?

A data engineer is designing a data lake solution on S3 and plans to use Athena for ad-hoc querying. The data is continuously ingested, and they want to ensure that Athena can efficiently query this data with minimal overhead. Which of the following combinations of techniques would be MOST effective for optimizing Athena's query performance in this scenario?

A data engineer is tasked with optimizing Athena query performance for a large dataset stored in S3. The dataset is frequently queried, and the underlying data rarely changes. Which optimization technique would be the MOST effective in this scenario?

An organization uses Athena to analyze clickstream data stored in S3. The data is partitioned by date, but analysts often need to query specific date ranges. Manually updating the partition metadata is cumbersome. Which feature would BEST automate partition management and improve query performance?

A company wants to use Athena to query data from multiple sources, including Amazon RDS for customer information, DynamoDB for session data, and S3 for marketing analytics. How can they achieve this in Athena?

A data analytics team is using Athena to run queries on a shared data lake. They are experiencing performance issues due to resource contention and want to isolate their queries to better manage costs and control query execution settings. Which Athena feature should they implement?

A financial company is using Athena to analyze large volumes of transaction data stored in S3. To improve query performance, they decide to use AWS Glue to manage partitions. Which of the following BEST describes how AWS Glue optimizes Athena's query performance?

An organization is using Athena to analyze a large dataset in S3. They have noticed that certain queries, especially those run repeatedly on the same unchanged data, take a significant amount of time. Which of the following optimization techniques would be the MOST effective in reducing the query time and cost without modifying the underlying data?

A data analyst needs to query data from multiple, disparate data sources including Amazon RDS, DynamoDB, and S3. The goal is to create a unified view of customer behavior. Which Athena feature would BEST facilitate this?

A data engineering team is using Athena to query data in S3, which is partitioned by date. They want to automate the process of partition management to avoid manually updating partition metadata. Which feature would be MOST suitable for achieving this?

An organization has multiple teams using Athena to query data in S3. Each team has different access requirements, cost considerations, and query execution needs. What is the MOST suitable Athena feature to manage costs, isolate queries, and control query execution settings for each team?

A company uses Athena to analyze website clickstream data stored in S3. To improve query performance, they want to ensure that Athena retrieves only the relevant partitions based on query predicates. Which optimization technique would be MOST effective?

Glue Crawler Cost

What are DPUs?

Data Catalog Pricing

Glue ETL Job Cost

Minimum DPUs for ETL

Cost of DPUs

Glue Notebook Cost

Stateful System

Stateless System

Kinesis Data Processing

Data Pipeline Orchestration

Data Extraction Sources

Data Transformation Types

Glue Workflows

Workflow Triggers

Spark ETL Jobs

Spark Streaming ETL Jobs

Python Shell Jobs

Ray Jobs

Standard Execution Type

Flexible Execution Type

Glue Partitioning

Glue DataBrew

DataBrew Project

DataBrew Step

DataBrew Recipe

DataBrew Job

DataBrew Schedules

DataBrew Profiling

DataBrew Cost

What is Athena?

What are Ad-hoc queries?

What is a Federated Query?

What is Partition Pruning?

What are Workgroups in Athena?

Athena's Core Function

Athena: Serverless

Athena Pricing Model

Athena: Partition Projection

Athena: Query Result Reuse

Athena's Primary Use

Data Lake Analytics

Athena Federated Query

AWS Glue Partition Indexes

Athena Workgroups