AWS Kinesis Data Firehose – KDF

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AWS Kinesis Data Firehose – KDF

  • Amazon Kinesis Data Firehose is a fully managed service for delivering real-time streaming data
  • Kinesis Data Firehose is a fully managed service that automatically scales to match the throughput of the data and requires no ongoing administration or need to write applications or manage resources
  • data transfer solution for delivering real time streaming data to destinations such as S3,  Redshift,  Elasticsearch service, and Splunk.
  • is NOT Real Time, but Near Real Time as it supports batching and buffers streaming data to a certain size (Buffer Size in MBs) or for a certain period of time (Buffer Interval in seconds) before delivering it to destinations.
  • supports batching, compression, and encryption of the data before loading it, minimizing the amount of storage used at the destination and increasing security
  • supports data compression, minimizing the amount of storage used at the destination. It currently supports GZIP, ZIP, and SNAPPY compression formats. Only GZIP is supported if the data is further loaded to Redshift.
  • supports data at rest encryption using KMS after the data is delivered to the S3 bucket.
  • supports multiple producers as datasource, which include Kinesis data stream, Kinesis Agent, or the Kinesis Data Firehose API using the AWS SDK, CloudWatch Logs, CloudWatch Events, or AWS IoT
  • supports out of box data transformation as well as custom transformation using Lambda function to transform incoming source data and deliver the transformed data to destinations
  • supports source record backup with custom data transformation with Lambda, where Kinesis Data Firehose will deliver the un-transformed incoming data to a separate S3 bucket.
  • uses at least once semantics for data delivery. In rare circumstances such as request timeout upon data delivery attempt, delivery retry by Firehose could introduce duplicates if the previous request eventually goes through.
  • supports Interface VPC endpoint (AWS Private Link) to keep traffic between the VPC and Kinesis Data Firehose from leaving the Amazon network. Interface VPC endpoints don’t require an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection

Kinesis Data Firehose

Kinesis Key Concepts

  • Kinesis Data Firehose delivery stream
    • Underlying entity of Kinesis Data Firehose, where the data is sent
  • Record
    • Data sent by data producer to a Kinesis Data Firehose delivery stream
    • Maximum size of a record (before Base64-encoding) is 1024 KB.
  • Data producer
    • Producers send records to Kinesis Data Firehose delivery streams.
  • Buffer size and buffer interval
    • Kinesis Data Firehose buffers incoming streaming data to a certain size or for a certain time period before delivering it to destinations
    • Buffer size and buffer interval can be configured while creating the delivery stream
    • Buffer size is in MBs and ranges from 1MB to 128MB for S3 destination and 1MB to 100MB for Elasticsearch Service destination.
    • Buffer interval is in seconds and ranges from 60 secs to 900 secs
    • Firehose raises buffer size dynamically to catch up and make sure that all data is delivered to the destination, if data delivery to destination is falling behind data writing to delivery stream
    • Buffer size is applied before compression
  • Destination
    • A destination is the data store where the data will be delivered.
    • supports S3,  Redshift, Elasticsearch, and Splunk as destinations.

Kinesis Data Streams vs Kinesis Firehose

Refer blog Kinesis Data Streams vs Kinesis Firehose

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AWS Certification Exam Practice Questions

  1. A user is designing a new service that receives location updates from 3600 rental cars every hour. The cars location needs to be uploaded to an Amazon S3 bucket. Each location must also be checked for distance from the original rental location. Which services will process the updates and automatically scale? ​
    1. Amazon EC2 and Amazon EBS
    2. Amazon Kinesis Firehose and Amazon S3
    3. Amazon ECS and Amazon RDS
    4. Amazon S3 events and AWS Lambda
  2. You need to perform ad-hoc SQL queries on massive amounts of well-structured data. Additional data comes in constantly at a high velocity, and you don’t want to have to manage the infrastructure processing it if possible. Which solution should you use?
    1. Kinesis Firehose and RDS
    2. EMR running Apache Spark
    3. Kinesis Firehose and Redshift
    4. EMR using Hive
  3. Your organization needs to ingest a big data stream into their data lake on Amazon S3. The data may stream in at a rate of hundreds of megabytes per second. What AWS service will accomplish the goal with the least amount of management?
    1. Amazon Kinesis Firehose
    2. Amazon Kinesis Streams
    3. Amazon CloudFront
    4. Amazon SQS
  4. A startup company is building an application to track the high scores for a popular video game. Their Solution Architect is tasked with designing a solution to allow real-time processing of scores from millions of players worldwide. Which AWS service should the Architect use to provide reliable data ingestion from the video game into the datastore?
    1. AWS Data Pipeline
    2. Amazon Kinesis Firehose
    3. Amazon DynamoDB Streams
    4. Amazon Elasticsearch Service
  5. A company has an infrastructure that consists of machines which keep sending log information every 5 minutes. The number of these machines can run into thousands and it is required to ensure that the data can be analyzed at a later stage. Which of the following would help in fulfilling this requirement?
    1. Use Kinesis Firehose with S3 to take the logs and store them in S3 for further processing.
    2. Launch an Elastic Beanstalk application to take the processing job of the logs.
    3. Launch an EC2 instance with enough EBS volumes to consume the logs which can be used for further processing.
    4. Use CloudTrail to store all the logs which can be analyzed at a later stage.

References

6 thoughts on “AWS Kinesis Data Firehose – KDF

    1. there are 2 aspects here Kinesis can handle real time data for consumption and thats what the question focuses on. It can easily scale to handle this load. The focus of the question is data ingestion platform and the other options mentioned do not fit the requirement.

  1. Hi,

    Could you explain what’s the answer of this question ?

    An organization has 10,000 devices that generate 100 GB of telemetry data per day, with each record size around 10 KB. Each record has 100 fields, and one field consists of unstructured log data with a String data type in the English language. Some fields are required for the real-time dashboard, but all fields must be available for long-term generation. The organization also has 10 PB of previously cleaned and structured data, partitioned by Date, in a SAN that must be migrated to AWS within one month.
    Currently, the organization does not have any real-time capabilities in their solution. Because of storage limitations in the on-premises data warehouse, selective data is loaded while generating the long-term trend with ANSI SQL queries through JDBC for visualization. In addition to the one-time data loading, the organization needs a cost-effective and real-time solution.
    How can these requirements be met? (Choose two.)

    A.Use AWS IoT to send data from devices to an Amazon SQS queue, create a set of workers in an Auto Scaling group and read records in batch from the queue to process and save the data. Fan out to an Amazon SNS queue attached with an AWS Lambda function to filter the request dataset and save it to Amazon Elasticsearch Service for real-time analytics.
    B. Create a Direct Connect connection between AWS and the on-premises data center and copy the data to Amazon S3 using S3 Acceleration. Use Amazon Athena to query the data.
    C. Use AWS IoT to send the data from devices to Amazon Kinesis Data Streams with the IoT rules engine. Use one Kinesis Data Firehose stream attached to a Kinesis stream to batch and stream the data partitioned by date. Use another Kinesis Firehose stream attached to the same Kinesis stream to filter out
    the required fields to ingest into Elasticsearch for real-time analytics.
    D. Use AWS IoT to send the data from devices to Amazon Kinesis Data Streams with the IoT rules engine. Use one Kinesis Data Firehose stream attached to a Kinesis stream to stream the data into an Amazon S3
    bucket partitioned by date. Attach an AWS Lambda function with the same Kinesis stream to filter out the required fields for ingestion into Amazon DynamoDB for real-time analytics.
    E. Use multiple AWS Snowball Edge devices to transfer data to Amazon S3, and use Amazon Athena to query the data.

    1. Would go with D and E. D for real time ingestion, filtering and Dynamodb for analytics. Snowball for one time transfer.
      A & C would not work for real time and B would not work for one time transfer.

  2. Hi, for question 1, shouldn’t the answer be d(s3 and lambda)? How will kinesis firehose do the calculation:Each location must also be checked for distance from the original rental location?

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