Hive Data Ingestion In AWS

Kevin Feasel

2016-08-26

Hadoop

Songzhi Liu shows how to use the AWS stack to move data into Hive:

S3 bucket
In this framework, S3 is the start point and the place where data is landed and stored. You will configure the S3 bucket notifications as the event source that triggers the Lambda function. When a new object is stored/copied/uploaded in the specified S3 bucket, S3 sends out a notification to the Lambda function with the key information.

Lambda function
Lambda is a serverless technology that lets you run code without a server. The Lambda function is triggered by S3 as new data lands and then adds new partitions to Hive tables. It parses the S3 object key using the configuration settings in the DynamoDB tables.

DynamoDB table
DynamoDB is a NoSQL database (key-value store) service. It’s designed for use cases requiring low latency responses, as it provides double-digit millisecond level response at scale. DynamoDB is also a great place for metadata storage, given its schemaless design and low cost when high throughput is not required. In this framework, DynamoDB stores the schema configuration, table configuration, and failed actions for reruns.

EMR cluster
EMR is the managed Hadoop cluster service. In the framework, you use Hive installed on an EMR cluster.

This is a detailed post, but well worth a read if you’re on AWS.

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