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WorksheetsAWS Learning Day - Datalake & Analytics on AWS
Total questions: 10
Worksheet time: 50secs
What is the final purpose of Big Data analysis?
Create competitive advantages
Generate even more data
Answer questions, have insights
Create a model of the data
Data become "big data" when datasets are so large that they are difficult to?
Collect and Store
Sell and Buy
Organize and Analyze
Move and Share
Generate and Delete
Amazon Kinesis provides key capabilities in the form of
Kinesis Firehose – to easily load streaming data into AWS
Kinesis Analytics – to easily process and analyze streaming data with standard SQL;
Kinesis Storm - to easily process unbounded streams of data.
Kinesis Video Streams - to capture, process, and store video streams for analytics.
Kinesis Streams – to build custom applications that process and analyze data.
What are the main differences between a Data Lake (DL) vs Data Warehouse (DWH)?
DL is schema on read while DWH is schema on write.
DL is schema on write while DWH is schema on read.
DL is fast in reading, DWH is slow in reading.
DL is slow in reading, DWH is fast in reading.
DWH structured/semi-structured/unstructured data, DL structured data only
Amazon S3 can be used as:
Datalake
Data Warehouse
Amazon Redshift can be used as:
Datalake
Data Warehouse
Amazon DynamoDB can be used for:
Files data
Key-Value data
Transactional data
Cold data
Name two benefits of Amazon Athena for ad hoc querying.
ANSI SQL support, ease of use and administration
Unmanaged service with full configuration control
Query directly on Amazon S3 data
Query directly on Amazon DynamoDB data
Ad Hoc and easy to use Language
List three components of AWS Glue.
AWS Glue Data Catalog
Storage
Streaming Processor
Job Orchestration
ETL Engine
What AWS service is a very fast, cloud-powered business intelligence service?
Amazon Elasticsearch Service
TIBCO
Amazon QuickSight
Amazon Kinesis
Tableau
