Data Lake and Lake Formation

Questions and Answers

What type of data can Amazon Redshift handle?

Only semi-structured data

Only structured data

Only unstructured data

Both structured and semi-structured data (correct)

Which of these is NOT a benefit of using Amazon Redshift Serverless?

Automatic provisioning and scaling of data warehouse capacity

Pay-as-you-go pricing model

Requires manual infrastructure management (correct)

Provides fast performance for demanding workloads

Which service allows you to process streaming data using Apache Kafka?

Amazon MSK (correct)

Amazon Redshift

Amazon Athena

Amazon QuickSight

What is the primary function of Amazon Redshift?

Storing and analyzing large datasets Signup and view all the answers

Which service can be used to analyze data stored in an Amazon S3 data lake?

All of the above Signup and view all the answers

What is the main advantage of using Amazon Redshift over traditional on-premises solutions?

Significantly lower costs Signup and view all the answers

What does Amazon Redshift use to achieve fast query completion?

Massively parallel query execution Signup and view all the answers

What is the primary function of Lake Formation?

To create and manage data lakes by automating various tasks. Signup and view all the answers

Which of the following is NOT a benefit of using a data lake?

Simplified data management with minimal automation Signup and view all the answers

Which of these is NOT a use case for Amazon Redshift Serverless?

Developing serverless functions Signup and view all the answers

What is the primary type of database management system used by Amazon Redshift?

Relational Signup and view all the answers

Which service enables interactive data analysis and querying of data stored in Amazon S3?

Amazon Athena Signup and view all the answers

How does Amazon Redshift Serverless ensure cost-effectiveness for users?

By charging only for the resources used Signup and view all the answers

Which service provides a fully managed data warehouse solution?

Amazon Redshift Signup and view all the answers

Which service can be used to build machine learning models using data stored in an Amazon S3 data lake?

SageMaker Signup and view all the answers

Which of the following is NOT a task typically involved in setting up and managing a data lake?

Setting up and managing relational database schemas Signup and view all the answers

What is one primary function of AWS Entity Resolution?

To remove duplicate records and create customer profiles Signup and view all the answers

What does AWS Glue primarily assist with?

Extracting, transforming, and loading data for analytics Signup and view all the answers

Which of the following engines does AWS Glue Data Integration provide access to?

Apache Spark, PySpark, and Python Signup and view all the answers

How does AWS Glue Data Quality help users?

By automatically detecting and monitoring data quality issues Signup and view all the answers

What is the primary purpose of AWS Lake Formation?

To help set up secure data lakes quickly Signup and view all the answers

In the context of AWS services, what does ETL stand for?

Extract, Transform, Load Signup and view all the answers

Which of the following describes the role of AWS Glue Data Catalog?

A repository of metadata that makes data easily searchable and queryable Signup and view all the answers

What common feature does AWS Glue offer for scaling workloads?

Integration with big data processing frameworks Signup and view all the answers

Study Notes

Data Lake

A centralized, curated, and secured repository that stores all data, both in its original form and prepared for analysis.
Enables breaking down data silos and combining different types of analytics to gain insights and guide better business decisions.

Lake Formation

Simplifies setting up and managing data lakes by defining data sources and applying access and security policies.
Collects and catalogs data from databases and object storage, moves data into Amazon S3, cleans and classifies data using ML algorithms, and secures access to sensitive data.
Provides a centralized catalog of data that describes available data sets and their usage.

AWS Glue

A fully managed extract, transform, and load (ETL) service that prepares and loads data for analytics.
Discovers data, stores metadata in the AWS Glue Data Catalog, and makes data searchable, queryable, and available for ETL.
Provides access to data using Apache Spark, PySpark, and Python, and can scale workloads using Ray.

AWS Glue Data Quality

Measures and monitors data quality of Amazon S3 based data lakes, data warehouses, and other data repositories.
Automatically computes statistics, recommends quality rules, and monitors and alerts when detecting missing, stale, or bad data.

Amazon Redshift

A cloud data warehouse that makes it fast, simple, and cost-effective to analyze all data using standard SQL and existing Business Intelligence (BI) tools.
Allows running complex analytic queries against terabytes to petabytes of structured and semi-structured data.
Provides fast performance, scalable storage, and cost-effective pricing.

Amazon Redshift Serverless

Makes it easier to run and scale analytics without managing data warehouse infrastructure.
Automatically provisions and scales data warehouse capacity to deliver fast performance for demanding workloads.
Provides flexible, familiar SQL features in an easy-to-use, zero administration environment.

Amazon Managed Streaming for Apache Kafka (Amazon MSK)

A fully managed service that makes it easy to build and run applications that use Apache Kafka to process streaming data.

AWS Entity Resolution

Uses flexible, configurable ML and rule-based techniques to remove duplicate records, create customer profiles, and personalize experiences across advertising and marketing campaigns.
Can create a unified view of customer interactions by linking recent events, such as ad clicks, cart abandonment, and purchases, into a unique match ID.