allows real-time processing of streaming big data and the ability to read and replay records to multiple Amazon Kinesis Applications.
Amazon Kinesis Client Library (KCL) delivers all records for a given partition key to the same record processor, making it easier to build multiple applications that read from the same Amazon Kinesis stream (for example, to perform counting, aggregation, and filtering).
offers a reliable, highly-scalable hosted queue for storing messages as they travel between applications or microservices.
It moves data between distributed application components and helps decouple these components.
provides common middleware constructs such as dead-letter queues and poison-pill management.
provides a generic web services API and can be accessed by any programming language that the AWS SDK supports.
supports both standard and FIFO queues
Scaling
Kinesis Data streams is not fully managed and requires manual provisioning and scaling by increasing shards
SQS is fully managed, highly scalable and requires no administrative overhead and little configuration
Ordering
Kinesis provides ordering of records, as well as the ability to read and/or replay records in the same order to multiple Kinesis Applications
SQS Standard Queue does not guarantee data ordering and provides at least once delivery of messages
SQS FIFO Queue guarantees data ordering within the message group
Data Retention Period
Kinesis Data Streams stores the data for up to 24 hours, by default, and can be extended to 365 days
SQS stores the message for up to 4 days, by default, and can be configured from 1 minute to 14 days but clears the message once deleted by the consumer
Delivery Semantics
Kinesis and SQS Standard Queue both guarantee at least one delivery of the message.
SQS FIFO Queue guarantees Exactly once delivery
Parallel Clients
Kinesis supports multiple consumers
SQS allows the messages to be delivered to only one consumer at a time and requires multiple queues to deliver messages to multiple consumers
Use Cases
Kinesis use cases requirements
Ordering of records.
Ability to consume records in the same order a few hours later
Ability for multiple applications to consume the same stream concurrently
Routing related records to the same record processor (as in streaming MapReduce)
SQS uses cases requirements
Messaging semantics like message-level ack/fail and visibility timeout
Leveraging SQS’s ability to scale transparently
Dynamically increasing concurrency/throughput at read time
Individual message delay, which can be delayed
AWS Certification Exam Practice Questions
Questions are collected from Internet and the answers are marked as per my knowledge and understanding (which might differ with yours).
AWS services are updated everyday and both the answers and questions might be outdated soon, so research accordingly.
AWS exam questions are not updated to keep up the pace with AWS updates, so even if the underlying feature has changed the question might not be updated
Open to further feedback, discussion and correction.
You are deploying an application to track GPS coordinates of delivery trucks in the United States. Coordinates are transmitted from each delivery truck once every three seconds. You need to design an architecture that will enable real-time processing of these coordinates from multiple consumers. Which service should you use to implement data ingestion?
Amazon Kinesis
AWS Data Pipeline
Amazon AppStream
Amazon Simple Queue Service
Your customer is willing to consolidate their log streams (access logs, application logs, security logs etc.) in one single system. Once consolidated, the customer wants to analyze these logs in real time based on heuristics. From time to time, the customer needs to validate heuristics, which requires going back to data samples extracted from the last 12 hours? What is the best approach to meet your customer’s requirements?
Send all the log events to Amazon SQS. Setup an Auto Scaling group of EC2 servers to consume the logs and apply the heuristics.
Send all the log events to Amazon Kinesis develop a client process to apply heuristics on the logs (Can perform real time analysis and stores data for 24 hours which can be extended to 7 days)
Configure Amazon CloudTrail to receive custom logs, use EMR to apply heuristics the logs (CloudTrail is only for auditing)
Setup an Auto Scaling group of EC2 syslogd servers, store the logs on S3 use EMR to apply heuristics on the logs (EMR is for batch analysis)
Amazon Kinesis Data Streams is a streaming data service that enables real-time processing of streaming data at a massive scale.
Kinesis Streams enables building of custom applications that process or analyze streaming data for specialized needs.
Kinesis Streams features
handles provisioning, deployment, ongoing-maintenance of hardware, software, or other services for the data streams.
manages the infrastructure, storage, networking, and configuration needed to stream the data at the level of required data throughput.
synchronously replicates data across three AZs in an AWS Region, providing high availability and data durability.
stores records of a stream for up to 24 hours, by default, from the time they are added to the stream. The limit can be raised to up to 7 days by enabling extended data retention.
Data such as clickstreams, application logs, social media, etc can be added from multiple sources and within seconds is available for processing to the Kinesis Applications.
Kinesis provides the ordering of records, as well as the ability to read and/or replay records in the same order to multiple applications.
Kinesis is designed to process streaming big data and the pricing model allows heavy PUTs rate.
Multiple Kinesis Data Streams applications can consume data from a stream, so that multiple actions, like archiving and processing, can take place concurrently and independently.
Kinesis Data Streams application can start consuming the data from the stream almost immediately after the data is added and put-to-get delay is typically less than 1 second.
Kinesis Streams is useful for rapidly moving data off data producers and then continuously processing the data, be it to transform the data before emitting to a data store, run real-time metrics and analytics, or derive more complex data streams for further processing
Accelerated log and data feed intake: Data producers can push data to Kinesis stream as soon as it is produced, preventing any data loss and making it available for processing within seconds.
Real-time metrics and reporting: Metrics can be extracted and used to generate reports from data in real-time.
Real-time data analytics: Run real-time streaming data analytics.
Complex stream processing: Create Directed Acyclic Graphs (DAGs) of Kinesis Applications and data streams, with Kinesis applications adding to another Amazon Kinesis stream for further processing, enabling successive stages of stream processing.
Kinesis limits
stores records of a stream for up to 24 hours, by default, which can be extended to max 365 days
maximum size of a data blob (the data payload before Base64-encoding) within one record is 1 megabyte (MB)
Each shard can support up to 1000 PUT records per second.
S3 is a cost-effective way to store the data, but not designed to handle a stream of data in real-time
Kinesis Data Streams Terminology
Data Record
A record is the unit of data stored in a Kinesis data stream.
A record is composed of a sequence number, partition key, and data blob, which is an immutable sequence of bytes.
Maximum size of a data blob is 1 MB
Partition key
Partition key is used to segregate and route records to different shards of a stream.
A partition key is specified by the data producer while adding data to a Kinesis stream.
Sequence number
A sequence number is a unique identifier for each record.
Kinesis assigns a Sequence number, when a data producer calls PutRecord or PutRecords operation to add data to a stream.
Sequence numbers for the same partition key generally increase over time; the longer the time period between PutRecord or PutRecords requests, the larger the sequence numbers become.
Data Stream
Data stream represents a group of data records.
Data records in a data stream are distributed into shards.
Shard
A shard is a uniquely identified sequence of data records in a stream.
Streams are made of shards and are the base throughput unit of a Kinesis stream, as pricing is per shard basis.
Each shard supports up to 5 transactions per second for reads, up to a maximum total data read rate of 2 MB per second, and up to 1,000 records per second for writes, up to a maximum total data write rate of 1 MB per second (including partition keys)
Each shard provides a fixed unit of capacity. If the limits are exceeded, either by data throughput or the number of PUT records, the put data call will be rejected with a ProvisionedThroughputExceeded exception.
This can be handled by
Implementing a retry on the data producer side, if this is due to a temporary rise of the stream’s input data rate
Dynamically scaling the number of shared (resharding) to provide enough capacity for the put data calls to consistently succeed
Capacity Mode
A data stream capacity mode determines the pricing and how the capacity is managed
Kinesis Data Streams currently supports an on-demand mode and a provisioned mode
On-demand mode,
KDS automatically manages the shards in order to provide the necessary throughput.
You are charged only for the actual throughput used and KDS automatically accommodates the workloads’ throughput needs as they ramp up or down.
Provisioned mode
Number of shards for the data stream must be specified.
Total capacity of a data stream is the sum of the capacities of its shards.
Shards can be increased or decreased in a data stream as needed and you are charged for the number of shards at an hourly rate.
Retention Period
All data is stored for 24 hours, by default, and can be increased to 8760 hours (365 days) 168 hours (7 days) maximum.
Producers
A producer puts data records into Kinesis data streams.
To put data into the stream, the name of the stream, a partition key, and the data blob to be added to the stream should be specified.
Partition key is used to determine which shard in the stream the data record is added to.
Consumers
A consumer is an application built to read and process data records from Kinesis data streams.
supports encryption in transit using HTTPS endpoints.
supports Interface VPC endpoint to keep traffic between VPC and Kinesis Data Streams from leaving the Amazon network. Interface VPC endpoints don’t require an IGW, NAT device, VPN connection, or Direct Connect.
integrated with IAM to control access to Kinesis Data Streams resources.
integrated with CloudTrail, which provides a record of actions taken by a user, role, or an AWS service in Kinesis Data Streams.
PutRecord & PutRecords operations are synchronous operation that sends single/multiple records to the stream per HTTP request.
use PutRecords to achieve a higher throughput per data producer
helps manage many aspects of Kinesis Data Streams (including creating streams, resharding, and putting and getting records)
Kinesis Agent
is a pre-built Java application that offers an easy way to collect and send data to the Kinesis stream.
can be installed on Linux-based server environments such as web servers, log servers, and database servers
can be configured to monitor certain files on the disk and then continuously send new data to the Kinesis stream
Kinesis Producer Library (KPL)
is an easy-to-use and highly configurable library that helps to put data into a Kinesis stream.
provides a layer of abstraction specifically for ingesting data
presents a simple, asynchronous, and reliable interface that helps achieve high producer throughput with minimal client resources.
batches messages, as it aggregates records to increase payload size and improve throughput.
Collects records and uses PutRecords to write multiple records to multiple shards per request
Writes to one or more Kinesis data streams with an automatic and configurable retry mechanism.
Integrates seamlessly with the Kinesis Client Library (KCL) to de-aggregate batched records on the consumer
Submits CloudWatch metrics to provide visibility into performance
Third Party and Open source
Log4j appender
Apache Kafka
Flume, fluentd, etc.
Kinesis Consumers
Kinesis Application is a data consumer that reads and processes data from a Kinesis Data Stream and can be built using either Kinesis API or Kinesis Client Library (KCL)
Shards in a stream provide 2 MB/sec of read throughput per shard, by default, which is shared by all the consumers reading from a given shard.
Kinesis Client Library (KCL)
is a pre-built library with multiple language support
delivers all records for a given partition key to same record processor
makes it easier to build multiple applications reading from the same stream for e.g. to perform counting, aggregation, and filtering
handles complex issues such as adapting to changes in stream volume, load-balancing streaming data, coordinating distributed services, and processing data with fault-tolerance
uses a unique DynamoDB table to keep track of the application’s state, so if the Kinesis Data Streams application receives provisioned-throughput exceptions, increase the provisioned throughput for the DynamoDB table
AWS Lambda, Kinesis Data Firehose, and Kinesis Data Analytics also act as consumers for Kinesis Data Streams
Kinesis Enhanced fan-out
allows customers to scale the number of consumers reading from a data stream in parallel, while maintaining high performance and without contending for read throughput with other consumers.
provides logical 2 MB/sec throughput pipes between consumers and shards for Kinesis Data Streams Consumers.
Kinesis Data Streams Sharding
Resharding helps to increase or decrease the number of shards in a stream in order to adapt to changes in the rate of data flowing through the stream.
Resharding operations support shard split and shard merge.
Shard split helps divide a single shard into two shards. It increases the capacity and the cost.
Shard merge helps combine two shards into a single shard. It reduces the capacity and the cost.
Resharding is always pairwise and always involves two shards.
The shard or pair of shards that the resharding operation acts on are referred to as parent shards. The shard or pair of shards that result from the resharding operation are referred to as child shards.
Kinesis Client Library tracks the shards in the stream using a DynamoDB table and discovers the new shards and populates new rows in the table.
KCL ensures that any data that existed in shards prior to the resharding is processed before the data from the new shards, thereby, preserving the order in which data records were added to the stream for a particular partition key.
Data records in the parent shard are accessible from the time they are added to the stream to the current retention period.
Questions are collected from Internet and the answers are marked as per my knowledge and understanding (which might differ with yours).
AWS services are updated everyday and both the answers and questions might be outdated soon, so research accordingly.
AWS exam questions are not updated to keep up the pace with AWS updates, so even if the underlying feature has changed the question might not be updated
Open to further feedback, discussion and correction.
Questions are collected from Internet and the answers are marked as per my knowledge and understanding (which might differ with yours).
AWS services are updated everyday and both the answers and questions might be outdated soon, so research accordingly.
AWS exam questions are not updated to keep up the pace with AWS updates, so even if the underlying feature has changed the question might not be updated
Open to further feedback, discussion and correction.
You are deploying an application to track GPS coordinates of delivery trucks in the United States. Coordinates are transmitted from each delivery truck once every three seconds. You need to design an architecture that will enable real-time processing of these coordinates from multiple consumers. Which service should you use to implement data ingestion?
Amazon Kinesis
AWS Data Pipeline
Amazon AppStream
Amazon Simple Queue Service
You are deploying an application to collect votes for a very popular television show. Millions of users will submit votes using mobile devices. The votes must be collected into a durable, scalable, and highly available data store for real-time public tabulation. Which service should you use?
Amazon DynamoDB
Amazon Redshift
Amazon Kinesis
Amazon Simple Queue Service
Your company is in the process of developing a next generation pet collar that collects biometric information to assist families with promoting healthy lifestyles for their pets. Each collar will push 30kb of biometric data In JSON format every 2 seconds to a collection platform that will process and analyze the data providing health trending information back to the pet owners and veterinarians via a web portal Management has tasked you to architect the collection platform ensuring the following requirements are met. Provide the ability for real-time analytics of the inbound biometric data Ensure processing of the biometric data is highly durable, elastic and parallel. The results of the analytic processing should be persisted for data mining. Which architecture outlined below will meet the initial requirements for the collection platform?
Utilize S3 to collect the inbound sensor data analyze the data from S3 with a daily scheduled Data Pipeline and save the results to a Redshift Cluster.
Utilize Amazon Kinesis to collect the inbound sensor data, analyze the data with Kinesis clients and save the results to a Redshift cluster using EMR. (refer link)
Utilize SQS to collect the inbound sensor data analyze the data from SQS with Amazon Kinesis and save the results to a Microsoft SQL Server RDS instance.
Utilize EMR to collect the inbound sensor data, analyze the data from EUR with Amazon Kinesis and save me results to DynamoDB.
Your customer is willing to consolidate their log streams (access logs, application logs, security logs etc.) in one single system. Once consolidated, the customer wants to analyze these logs in real time based on heuristics. From time to time, the customer needs to validate heuristics, which requires going back to data samples extracted from the last 12 hours? What is the best approach to meet your customer’s requirements?
Send all the log events to Amazon SQS. Setup an Auto Scaling group of EC2 servers to consume the logs and apply the heuristics.
Send all the log events to Amazon Kinesis develop a client process to apply heuristics on the logs (Can perform real time analysis and stores data for 24 hours which can be extended to 7 days)
Configure Amazon CloudTrail to receive custom logs, use EMR to apply heuristics the logs (CloudTrail is only for auditing)
Setup an Auto Scaling group of EC2 syslogd servers, store the logs on S3 use EMR to apply heuristics on the logs (EMR is for batch analysis)
You require the ability to analyze a customer’s clickstream data on a website so they can do behavioral analysis. Your customer needs to know what sequence of pages and ads their customer clicked on. This data will be used in real time to modify the page layouts as customers click through the site to increase stickiness and advertising click-through. Which option meets the requirements for captioning and analyzing this data?
Log clicks in weblogs by URL store to Amazon S3, and then analyze with Elastic MapReduce
Push web clicks by session to Amazon Kinesis and analyze behavior using Kinesis workers
Write click events directly to Amazon Redshift and then analyze with SQL
Publish web clicks by session to an Amazon SQS queue men periodically drain these events to Amazon RDS and analyze with SQL
Your social media monitoring application uses a Python app running on AWS Elastic Beanstalk to inject tweets, Facebook updates and RSS feeds into an Amazon Kinesis stream. A second AWS Elastic Beanstalk app generates key performance indicators into an Amazon DynamoDB table and powers a dashboard application. What is the most efficient option to prevent any data loss for this application?
Use AWS Data Pipeline to replicate your DynamoDB tables into another region.
Use the second AWS Elastic Beanstalk app to store a backup of Kinesis data onto Amazon Elastic Block Store (EBS), and then create snapshots from your Amazon EBS volumes.
Add a second Amazon Kinesis stream in another Availability Zone and use AWS data pipeline to replicate data across Kinesis streams.
Add a third AWS Elastic Beanstalk app that uses the Amazon Kinesis S3 connector to archive data from Amazon Kinesis into Amazon S3.
You need to replicate API calls across two systems in real time. What tool should you use as a buffer and transport mechanism for API call events?
AWS SQS
AWS Lambda
AWS Kinesis (AWS Kinesis is an event stream service. Streams can act as buffers and transport across systems for in-order programmatic events, making it ideal for replicating API calls across systems)
AWS SNS
You need to perform ad-hoc business analytics queries on well-structured data. Data comes in constantly at a high velocity. Your business intelligence team can understand SQL. What AWS service(s) should you look to first?
Kinesis Firehose + RDS
Kinesis Firehose + RedShift (Kinesis Firehose provides a managed service for aggregating streaming data and inserting it into RedShift. RedShift also supports ad-hoc queries over well-structured data using a SQL-compliant wire protocol, so the business team should be able to adopt this system easily. Refer link)
Kinesis acts as a highly available conduit to stream messages between data producers and data consumers.
Data producers can be almost any source of data: system or web log data, social network data, financial trading information, geospatial data, mobile app data, or telemetry from connected IoT devices.
Data consumers will typically fall into the category of data processing and storage applications such as Apache Hadoop, Apache Storm, S3, and ElasticSearch.
Purpose
Kinesis data streams is highly customizable and best suited for developers building custom applications or streaming data for specialized needs.
Kinesis Data Firehose handles loading data streams directly into AWS products for processing. Firehose also allows for streaming to S3, OpenSearch Service, or Redshift, where data can be copied for processing through additional services.
Provisioning & Scaling
Kinesis Data Streams needs you to configure shards, scale, and write your own custom applications for both producers and consumers. KDS requires manual scaling and provisioning.
Kinesis Data Firehose is fully managed and sends data to S3, Redshift, and OpenSearch. Scaling is handled automatically, up to gigabytes per second, and allows for batching, encrypting, and compressing.
Processing Delay
Kinesis Data Streams provides real-time processing with ~200 ms for shared throughput classic single consumer and ~70 ms for the enhanced fan-out consumer.
Kinesis Data Firehose provides near real-time processing with the lowest buffer time of 1 min.
Data Storage
Kinesis Data Streams provide data storage. Data typically is made available in a stream for 24 hours, but for an additional cost, users can gain data availability for up to 365 days.
Kinesis Data Firehose does not provide data storage.
Replay
Kinesis Data Streams supports replay capability
Kinesis Data Firehose does not support replay capability
Producers & Consumers
Kinesis Data Streams & Kinesis Data Firehose support multiple producer options including SDK, KPL, Kinesis Agent, IoT, etc.
Kinesis Data Streams support multiple consumers option including SDK, KCL, and Lambda, and can write data to multiple destinations. However, they have to be coded. Kinesis Data Firehose consumers are close-ended and support destinations like S3, Redshift, OpenSearch, and other third-party tools.
AWS Certification Exam Practice Questions
Questions are collected from Internet and the answers are marked as per my knowledge and understanding (which might differ with yours).
AWS services are updated everyday and both the answers and questions might be outdated soon, so research accordingly.
AWS exam questions are not updated to keep up the pace with AWS updates, so even if the underlying feature has changed the question might not be updated
Open to further feedback, discussion and correction.
Questions are collected from Internet and the answers are marked as per my knowledge and understanding (which might differ with yours).
AWS services are updated everyday and both the answers and questions might be outdated soon, so research accordingly.
AWS exam questions are not updated to keep up the pace with AWS updates, so even if the underlying feature has changed the question might not be updated
Open to further feedback, discussion and correction.
Your organization needs to ingest a big data stream into its 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?
Amazon Kinesis Firehose
Amazon Kinesis Streams
Amazon CloudFront
Amazon SQS
Your organization is looking for a solution that can help the business with streaming data several services will require access to read and process the same stream concurrently. What AWS service meets the business requirements?
Amazon Kinesis Firehose
Amazon Kinesis Streams
Amazon CloudFront
Amazon SQS
Your application generates a 1 KB JSON payload that needs to be queued and delivered to EC2 instances for applications. At the end of the day, the application needs to replay the data for the past 24 hours. In the near future, you also need the ability for other multiple EC2 applications to consume the same stream concurrently. What is the best solution for this?
Amazon Kinesis Data Firehose is a fully managed service for delivering real-time streaming data
Kinesis Data Firehose automatically scales to match the throughput of the data and requires no ongoing administration or need to write applications or manage resources
is a 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 Timeas 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 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 the 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.
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 the S3 destination and 1MB to 100MB for the OpenSearch 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 the destination is falling behind data writing to the 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.
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?
Amazon EC2 and Amazon EBS
Amazon Kinesis Firehose and Amazon S3
Amazon ECS and Amazon RDS
Amazon S3 events and AWS Lambda
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?
Kinesis Firehose and RDS
EMR running Apache Spark
Kinesis Firehose and Redshift
EMR using Hive
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?
Amazon Kinesis Firehose
Amazon Kinesis Streams
Amazon CloudFront
Amazon SQS
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?
AWS Data Pipeline
Amazon Kinesis Firehose
Amazon DynamoDB Streams
Amazon Elasticsearch Service
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?
Use Kinesis Firehose with S3 to take the logs and store them in S3 for further processing.
Launch an Elastic Beanstalk application to take the processing job of the logs.
Launch an EC2 instance with enough EBS volumes to consume the logs which can be used for further processing.
Use CloudTrail to store all the logs which can be analyzed at a later stage.
