AWS Workshop
  • About
  • Immersion Day
    • 2025.06 - EKS
    • 2025.03 - Generative AI
    • 2025.02 - ECS
    • 2024.09 - EKS
    • 2024.07 - Security
    • 2024.05 - ECS
    • 2024.01 - Generative AI
    • 2023.10 - Security
    • 2023.08 - MSK
    • 2023.06 - DevOps
    • 2023.04 - AI/ML
    • 2023.02 - EKS
    • 2023.01 - General
    • 2022.12 - 광주소프트웨어마이스터고등학교
    • 2022.12 - Data Analytics
      • Getting started
        • Create a Kinesis Data Stream
      • Produce data to Kinesis Data Streams
        • Cofigure AWS Cloud 9 IDE
        • Using the Amazon SDK with Kinesis
          • Working with the SDK
        • Using the Kinesis Producer Library
          • A Simple Producer
          • Error Analysis and Resiliency
          • CloudWatch Metrics from the Kinesis Producer Library
      • Lambda with Kinesis Data Firehose
        • Overview
        • Create Glue Table
        • Create Kinesis Data Firehose Delivery Stream
        • Stream the data and validate output
      • Clean, Aggregate, and Enrich Events with Kinesis Data Analytics
        • Setup Kinesis Data Analytics Studio
        • Stream the data and validate output
    • 2022.11 - General
    • 2022.10 - EKS
    • 2022.09 - General
    • 2022.08 - 큐피스트
    • 2022.08 - Security
    • 2022.07 - General
    • 2022.06 - Serverless
    • 2022.05 - EKS
    • 2022.05 - 백패커
    • 2022.05 - General
    • 2022.04 - DevOps
    • 2022.04 - Data Analytics
      • Getting started
        • Create a Kinesis Data Stream
      • Produce data to Kinesis Data Streams
        • Cofigure AWS Cloud 9 IDE
        • Using the Amazon SDK with Kinesis
          • Working with the SDK
        • Using the Kinesis Producer Library
          • A Simple Producer
          • Error Analysis and Resiliency
          • CloudWatch Metrics from the Kinesis Producer Library
      • Lambda with Kinesis Data Firehose
        • Overview
        • Create Glue Table
        • Create Kinesis Data Firehose Delivery Stream
        • Stream the data and validate output
      • Clean, Aggregate, and Enrich Events with Kinesis Data Analytics
        • Setup Kinesis Data Analytics Studio
        • Stream the data and validate output
    • 2022.03 - General
    • 2022.02 - AI/ML
    • 2022.01 - General
    • 2021.12 - Container
    • 2021.11 - General
    • 2021.10 - General
  • Seminar
    • 2024.07 - 동국대 특강
    • 2024.06 - Generative AI
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  1. Immersion Day
  2. 2022.12 - Data Analytics
  3. Lambda with Kinesis Data Firehose

Create Glue Table

PreviousOverviewNextCreate Kinesis Data Firehose Delivery Stream

Last updated 2 years ago

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이 섹션에서는 Kinesis Data Firehose가 데이터 형식 변환을 위한 스키마로 사용할 외부 테이블을 생성합니다.

1. Athena 콘솔로 이동하여 쿼리 편집기에서 Settings을 선택하고 Manage 선택

2. Browse S3를 클릭하고 버킷 목록에서 kinesis-bucket으로 시작하는 S3 버킷을 선택합니다. S3 URI에 /nyctaxitrips/를 추가하고 Save를 클릭합니다.

i.e. s3://kinesis-bucketXXXXXx-XXXXX/nyctaxitrips/

3. Editer 탭으로 돌아가서 Database 리스트에서 kinesislab을 선택하세요.

4.아래의 SQL 구문을 Query 1 창에 붙여넣고 Run을 클릭해서 쿼리를 실행하세요.

23번 라인에 있는 <BUCKET_NAME> 값에 위에서 선택한 S3 버킷 이름(kinesis-bucketXXX-XXXX)로 변경하고 쿼리를 실행해야 합니다.

CREATE EXTERNAL TABLE `nyctaxitrips`
(
`id` string,
`vendorId` int,
`pickupDate` string,
`dropoffDate` string,
`passengerCount` int,
`pickupLongitude` double,
`pickupLatitude` double,
`dropoffLongitude` double,
`dropoffLatitude` double,
`storeAndFwdFlag` string,
`gcDistance` double,
`tripDuration` int,
`googleDistance`int,
`googleDuration`int,
`source`string
)
PARTITIONED BY ( `year` string, `month` string, `day` string, `hour` string)
ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
STORED AS INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
LOCATION 's3://<<BUCKET-NAME>>/nyctaxitrips/'

"Query successful" 메시지가 표시됩니다.