Amazon Kinesis Data Firehose is a fully managed service for delivering real-time streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and other services. One of the powerful features of Firehose is its ability to transform data in transit using AWS Lambda. This process allows users to enrich, filter, or transform data before delivery to its final destination.
### Firehose Data Transformation with Lambda
1. **Setup and Data Flow**:
– **Kinesis Data Firehose**: You define a Kinesis Data Firehose delivery stream where the incoming data (from producers like IoT devices, applications, etc.) is ingested.
– **Lambda Transformation**: You create a Lambda function and associate it with your Firehose delivery stream. This Lambda function is invoked to process each batch of data records before they are delivered to the specified destination.
– **Data Delivery**: The transformed data is finally delivered to the configured destination like S3 or Redshift.
2. **Transformation Process**:
– **Invocation**: When records are sent to the Firehose stream, the associated Lambda function is triggered with a batch of records.
– **Processing**: The Lambda function processes these records. This can include:
– Parsing and restructuring,
– Converting formats (e.g., CSV to JSON),
– Enriching data (e.g., adding additional fields, merging external data),
– Filtering out records that do not meet certain criteria,
– Aggregating and summarizing data,
– **Return**: The Lambda function sends the transformed batch back to the Firehose stream. If any errors occur during transformation, you can configure error handling to manage them appropriately.
### Examples of Enriching or Filtering Data
1. **Data Enrichment**:
– Suppose your streaming data consists of IoT sensor readings with device IDs. You can use a Lambda function to query a database (e.g., DynamoDB) to fetch metadata about each device (such as location, type, or owner information) and merge this data with the original record. This enriched data is then delivered to the destination for further analysis.
2. **Filtering Data**:
– Imagine you are receiving log data from various applications but only want logs with a severity level of “ERROR” to be stored. You can write a Lambda function that examines each log record, filters out records where the severity is not “ERROR”, and only forwards the relevant records to the destination.
3. **Format Conversion**:
– Consider a scenario where you receive data in CSV format, but your data lake requires JSON format. A Lambda function can be used to parse each CSV record, transform it into a JSON object, and deliver the JSON data to your data lake.
4. **Aggregation**:
– For streaming clickstream data, if you wish to aggregate data to obtain session-level details, a Lambda function can process each click event, group and count them by session ID, and forward a summary or aggregated data instead of individual events.
### Steps to Implement the Transformation
1. **Create a Lambda Function**: Write and deploy a Lambda function that contains your transformation logic in your preferred programming language (e.g., Python, Node.js).
2. **Attach Lambda to Firehose**:
– In the Kinesis Data Firehose management console, navigate to your delivery stream settings.
– Under the “Transform source records with AWS Lambda” option, select your Lambda function.
3. **Configure Error Handling**:
– You can configure retry behavior and set up destinations for failed records (e.g., sending to an S3 bucket for analysis).
4. **Test and Deploy**:
– Test your configuration to ensure that the Lambda function properly processes incoming data.
– Deploy the configuration once the testing is complete, enabling the transformation pipeline in production.
By integrating Lambda with Firehose for data transformation, you add a robust, flexible layer of processing that tailors your streaming data to the specific needs of your storage and analysis systems.