Are you looking to process real-time streaming data in the cloud? If so, AWS Kinesis could be the solution you need. In this comprehensive guide, I‘ll walk you through everything Kinesis has to offer so you can evaluate if it‘s the right fit for your use case.
I‘ll explain what Kinesis is, its capabilities, use cases, pricing, competitors, and more. My goal is to provide you with detailed yet easy to understand information so you can make an informed decision. Sound good? Let‘s get started!
What is AWS Kinesis?
AWS Kinesis is a managed cloud service to easily collect, process, and analyze real-time streaming data at massive scale.
With Kinesis, you can build powerful applications that give you immediate insights from data as it‘s generated. This enables innovative use cases like real-time analytics, monitoring, fraud detection, and more.
Some key facts about Kinesis:
- Fully managed service – no servers to provision
- Real-time processing within milliseconds
- Scales to handle terabytes per second
- Durable storage across 3 availability zones
- Integrates with AWS analytics and ML services
- Handles security, encryption, access controls
- Pay only for the resources used
In a nutshell, Kinesis takes care of the complex infrastructure so you can focus on consuming and analyzing real-time data streams to drive value for your business.
Kinesis Components and Capabilities
The Kinesis platform includes several components to support different streaming data scenarios:
Kinesis Data Streams
Kinesis Data Streams allows you to build custom applications to consume and process data streams in real time.
It can capture and store terabytes of data per hour from hundreds of thousands of sources. The data is available immediately so your app can process it as it arrives.
Some key capabilities:
- Collect and process streaming data at massive scale
- Millisecond data delivery
- Multiple consumers can read the same stream
- Integrates with EC2, Lambda, and ML
- Configurable 1-7 day data retention
- Replay data for reprocessing
Kinesis Data Streams empowers you to build innovative real-time apps like analyzing user clickstreams, monitoring IT systems, processing financial transactions, running ETL jobs, and more.
Kinesis Data Firehose
Kinesis Data Firehose is a serverless service that delivers real-time data to preferred destinations like S3, Redshift, Elasticsearch, and Splunk.
It takes care of all the complexity of scaling delivery across varying workloads. It also optimizes the format of the data for each destination.
Key capabilities:
- Fully managed – no servers to manage
- Real-time data transformation
- Automatic scaling to match throughput needs
- Batching and compression for cost savings
- Reliable delivery monitoring
- Seamless integration with data lakes and warehouses
Firehose allows focusing just on streams and destinations without any infrastructure admin. Use cases include streaming ETL, log collection, and data lake ingestion.
Kinesis Video Streams
Kinesis Video Streams makes it easy to securely stream video content for processing and analysis.
It can ingest millions of streams from devices and then apply AI and ML services like Rekognition to gain real-time insights.
Key features:
- Scalable video ingestion from devices
- Integrates with Rekognition, SageMaker, etc
- Real-time video analytics
- Securely stores, encodes and indexes video
- Playback video for viewing
Video Streams opens up possibilities like smart security, machine monitoring, automated vehicle telemetry, and more.
Kinesis Data Analytics
Kinesis Data Analytics allows you to perform real-time analytics on streams via SQL or Java code.
The service scales automatically so you can focus just on the analytics. It integrates with visualization tools like Quicksight too.
Key capabilities:
- Analyze data streams using standard SQL
- Process and filter records in real-time
- Scale stream processing automatically
- Integrate with BI and reporting tools
- No infrastructure to manage
KDA makes it simple to generate aggregated metrics, dashboards, and alerts from your data streams.
Kinesis Client Library (KCL)
The Kinesis Client Library (KCL) simplifies creating applications that consume streams.
It handles tricky aspects like checkpointing, automatic restarts, batching, and load balancing across shards.
KCL enables maximum flexibility for building stream processing apps in Java, Node, Python, and .NET.
Kinesis Producer Library (KPL)
The Kinesis Producer Library (KPL) helps securely and reliably send data to Kinesis.
It batches records to optimize throughput, retries on failures, and logs detailed metrics.
KPL ensures high performance ingestion from any data source into Kinesis.
As you can see, the components provide a full-stack streaming platform tailored for real-time workloads at any scale.
Kinesis Use Cases
Kinesis powers a wide variety of streaming data use cases. Here are some examples:
Real-time Analytics
Kinesis enables running analytics on data immediately as it arrives to surface timely insights.
For example, Expedia uses Kinesis to analyze customer clickstreams in real time to recommend hotels and flights. By acting on data quickly, they boost conversions.
Data Processing and ETL
Kinesis provides a resilient foundation for streaming ETL jobs and data pipelines.
McDonald‘s built their next-gen data streaming platform on Kinesis and it handles millions of events per day with sub-second latency.
Application Monitoring
Streaming application logs, metrics, and clickstream data to Kinesis allows real-time monitoring and alerting.
Dow Jones monitors millions of data points a day via Kinesis to watch their systems and detect issues.
IoT and Sensor Data
Kinesis can efficiently collect and process high-volume telemetry data from IoT devices.
Amazon‘s IoT applications ingest sensor data from devices using Kinesis to enable automation.
Fraud Detection
By streaming transactions to Kinesis as they occur, you can identify fraudulent activity instantly.
Coinbase leverages Kinesis to check cryptocurrency transactions and pinpoint theft attempts.
Gaming Leaderboards
Kinesis empowers keeping gaming leaderboards updated in real-time as scores change.
Epic Games uses Kinesis to power the Fortnite leaderboards with minimal delay.
Predictive Scaling
Tracking traffic and usage metrics allows forecasting future demand so you can proactively scale resources.
Pinterest uses Kinesis plus machine learning for automated predictive scaling of their platform.
This is just a sample of the innovations Kinesis enables by unlocking the value in real-time data.
Kinesis Benefits
Let‘s explore some of the key benefits of using Kinesis:
Fully Managed Infrastructure
With Kinesis, you don‘t need to provision any servers or manage infrastructure. The service handles the hard work of ensuring high availability, scalability, and durability behind the scenes.
This streamlines development and reduces operational overhead. You also don‘t pay for excess capacity you aren‘t using.
Real-Time Stream Processing
Kinesis enables consuming, processing, and analyzing data streams in real-time so you can act on insights immediately.
This unlocks reactive use cases and delivers tangible business value from streaming data. Kinesis brings millisecond latency to the types of workloads that used to be limited to batch.
Massive Scalability
Kinesis offers virtually unlimited throughput and storage. Streams can scale to support up to gigabytes of incoming data per second from hundreds of thousands of sources.
This flexible scalability means you can start small projects and grow them seamlessly over time without service limits.
Durable Data Storage
Kinesis replicates data across 3 AWS availability zones for maximum resilience. Your data is stored durably even if an entire AZ goes down.
You configure data retention from 24 hours up to 7 days. This allows replaying and reprocessing streams when needed.
Tight Integration with AWS
Kinesis interoperates closely with a broad range of AWS analytics, ML, storage, and visualization services.
For instance, you can stream data from Kinesis into S3, Redshift, OpenSearch and more. This enables full end-to-end solutions on AWS.
Flexible Pricing
Kinesis offers pay-as-you-go pricing without upfront fees or minimum commitments. You only pay based on the throughput capacity and number of shards you provision.
Volume discounts can reduce shard pricing by up to 65% at scale. There are no data transfer fees too.
Enterprise-Grade Security
Encryption, VPC support, access controls, and compliance certifications safeguard your streaming data. Kinesis integrates with AWS security services like CloudTrail.
These protections allow using Kinesis for sensitive data like financial transactions, healthcare records, and personal info.
The combination of real-time processing, scalability, durability and tight AWS integration make Kinesis a powerful choice for streaming workloads.
Kinesis Limitations
While Kinesis has many advantages, there are some limitations worth noting:
Complexity
Kinesis is a complex service with many components. There is a steep learning curve to use it effectively. It requires engineering expertise to implement, optimize, and manage over time.
Cost at Scale
The pay-as-you-go model is cost efficient for small to mid-size streams. But for high volume production workloads with many shards, cost can add up. Large scale streaming on Kinesis runs $1000s per month.
Vendor Lock-In
Since Kinesis is proprietary to AWS, it can create vendor lock-in. Migrating large streaming applications off of Kinesis is difficult due to tight integration with AWS services.
Mutable Streams
Kinesis shards are immutable, so updating or deleting existing records in a stream is not possible. You need an external database for mutable data.
No Query Capabilities
Kinesis itself does not support querying data like a database. You need separate analytics and visualization tools for this capability.
Limited Data Retention
The max data retention in Kinesis streams is 7 days. This limits how far back you can replay and reprocess historical data.
Keep these tradeoffs in mind when evaluating options for your streaming application needs.
Kinesis Pricing and TCO
There are no upfront fees or minimum commitments required to use Kinesis. You pay only for the resources consumed.
Here is a breakdown of the Kinesis pricing model:
Shards
- $0.015 per shard provisioned per hour
You provision shards based on your peak throughput needs. Each shard can ingest 1MB/sec or 1000 events/sec.
PUT Payload Units
- $0.025 per 1 million payload units put into a shard
A payload unit is a 1KB chunk of data. So 5 KB = 5 payload units.
GET Payload Units
- $0.015 per 1 million payload units retrieved from a shard
Data Transfer
- $0.00 per GB for data transfer in/out
There are no data transfer fees which helps lower costs.
Kinesis Firehose
- Starts at $0.024 per GB for the first 1 TB per month. Tiers down from there.
Kinesis Video Streams
- $0.025 per GB video ingested
- $0.16 per million Video GetMedia calls
Kinesis offers volume discounts that can reduce shard hourly prices by up to 65% at scale.
To estimate overall costs, analyze each component of your workload including:
- Number of shards needed
- Peak PUT/GET throughput
- Data retention period
- Storage and delivery destinations
- Data transfer amounts
Most small to mid-size streaming applications run $15 – $500 per month on Kinesis. Enterprise workloads cost over $1000 month.
By optimizing shards and throughput, you can achieve significant cost efficiencies. The pay-as-you-go model also prevents overpaying for unused capacity upfront.
Alternatives to Kinesis
Let‘s compare Kinesis to some common streaming data alternatives:
Kafka
Kafka is a popular open source stream processing platform. Kinesis tends to be easier to use, monitor, and integrate tightly with AWS services. But Kafka provides more control for advanced users.
Google Cloud Pub/Sub
Google‘s Pub/Sub offers real-time messaging with autoscaling. Kinesis provides more robust stream processing and analytics built-in.
Azure Event Hubs
Microsoft‘s fully managed event streaming. Kinesis offers better analytics integration and lower pricing at high volume.
Amazon Managed Streaming for Apache Kafka (MSK)
Fully managed Kafka on AWS. Kinesis is better for simpler use cases not needing full Kafka capabilities. MSK adds operational overhead.
Confluent Cloud
Fully managed Kafka on public cloud. Kinesis has much deeper AWS integration, especially for real-time analytics.
Amazon MQ
Managed message queuing service. Simpler than Kinesis but lacks real-time processing and analytics.
Amazon SQS
Distributed queuing service. Primarily for job and workload distribution rather than streaming analytics.
For real-time stream processing and analytics, Kinesis provides the most robust and enterprise-ready cloud service overall.
Getting Started with Kinesis
Here is an overview of steps for getting started with Kinesis:
1. Sign Up for AWS
Create an AWS account if you don‘t already have one.
2. Understand Your Data Streaming Needs
Determine use cases, required integrations, and scalability needs. This drives design decisions.
3. Choose Kinesis Components
Select the Kinesis services needed like Data Streams, Firehose, Analytics based on requirements.
4. Create Resources
In the AWS console, create your Kinesis streams, delivery streams, consumer apps.
5. Configure Producers and Consumers
Have data sources use KPL to populate streams. Consumers read using the KCL.
6. Process, Store, and Visualize
Route your streams to other services to transform, analyze, store, and visualize.
7. Monitor and Optimize
Use CloudWatch to monitor usage metrics. Tune shards, capacity, retention as needed.
Start small with a simple proof of concept. Iterate and expand the scope over time once the basics are proven out.
Also leverage the excellent Kinesis documentation, getting started guide, and FAQs for help.
Kinesis Release History
Let‘s look back at some key milestones in the evolution of Kinesis:
-
Nov 2013 – Kinesis launches to allow ingesting and processing real-time streaming data
-
Mar 2015 – Kinesis Firehose released for serverless delivery of streams to S3 and other destinations
-
Apr 2016 – Kinesis Analytics introduced for real-time SQL and Java stream processing
-
Nov 2017 – Kinesis Video Streams added for ingesting and analyzing video feeds
-
Dec 2018 – Kinesis Data Streams gets SubscribeToShard API for easier consumer scaling
-
Jul 2019 – Kinesis Data Analytics SQL upgraded from SELECT-only to full DML
-
Aug 2020 – Automatic stream scaling and shard balancing added
-
May 2022 – On-demand throughput capacity increases added to improve cost efficiency
Since launch, Kinesis has continued rapid innovation to grow into a mature, full-featured streaming platform.
The Future of Kinesis
Kinesis is strategic priority for AWS. Here are some areas we can expect continued innovation:
-
Even higher throughput and lower latency as AWS builds out global infrastructure
-
More built-in real-time analytics capabilities like live dashboards and anomaly detection
-
Tighter integrations with AWS big data and visualization services
-
Advanced security features like field-level encryption and fine-grained access controls
-
Pre-built solutions tailored for telecom, financial services, media, and other verticals
-
Hybrid options to ingest and process data across cloud and on-prem
-
IoT capabilities like out-of-the-box support for MQTT data ingestion
Kinesis aims to be the clear leader in managed real-time streaming. The roadmap indicates Amazon will continue pushing the envelope.
Conclusion
Kinesis provides a powerful platform to build stream processing applications that provide real-time insights. Key strengths include tight integration with AWS, enterprise-grade security, and virtually unlimited scalability.
While Kinesis requires engineering skill to leverage fully, it can enable transformative streaming use cases for businesses. Understanding the capabilities and benefits is the first step in determining if it‘s a good fit.
I hope this guide gave you a comprehensive overview of everything Kinesis offers. Let me know if you have any other questions! I‘m happy to discuss further and help assess if Kinesis meets your specific streaming data needs.