Name: Amazon Kinesis
Price: 0.0085 USD
Rating: 9.6 (24 reviews)
Author: Amazon AWS

Help Desk

Live Chat

Web and Video Conferencing

Customer Support

Flowchart

Development

Association Management

Business Intelligence (BI)

Collaboration

Enterprise Legal Management

Risk Management

Streaming Analytics

Workflow

Enterprise

Accounting

Accounts Receivable

Crypto Asset Management

Crypto Tax

Subscription Management

Finance and Accounting

Applicant Tracking

Corporate Learning Management

Diversity, Equity, and Inclusion (DEI)

HR Management

Payroll

Talent Intelligence

Talent Management

Workforce Analytics

Workforce Management

Human Resources

.Net Development

AI Governance

Cloud Storage

Custom Software Development Services

Data Center Virtualization

Data Fabric

Fraud Detection

IT Service Providers

Identity Verification

Integration Platform as a Service (iPaaS)

Intranet

Load Testing

Managed File Transfer (MFT)

Managed Private Cloud

Network Performance Monitoring

SaaS Management

Software-Defined WAN

System Monitoring

Time Series Databases

User Activity Monitoring

Web Portal

Information Technology

A/B Testing

Ad Serving & Retargeting

All-in-One Marketing

Audio Editing

Content Management

Contest Management

Email Marketing

Gamification

Marketing Automation

Personalization Engines

Press Release Management

Product Sampling

Social Media Management

Survey & Forms Building

Video Editing

Web Analytics

Marketing

Project Management

Professional Services

Customer Relationship Management (CRM)

Lead-to-Account Matching and Routing

Product Configuration

Sales

Architecture

Construction Workforce Management

Inventory Management

Last Mile Delivery

Loan Servicing

Moving Software

Oil and Gas

Physical Therapy

Vertical-Specific

Find top rated software and services based on in-depth reviews from verified users. 400+ software categories including PaaS, NoSQL, BI, HR, and more.

Apache Kafka

Apache

Azure Stream Analytics

Microsoft

Amazon EMR (Elastic MapReduce)

Amazon AWS

Amazon Redshift

Google Analytics

Google

Google Cloud Dataflow

Confluent

AWS IoT Core

Firebase

Apache Spark Streaming

### Streaming Analytics
Users consistently highlight Amazon Kinesis's streaming analytics as a key strength, praising its ability to handle real-time data streams efficiently. The platform's ease of use, scalability, and integration with other AWS services are frequently commended. While some users mention minor concerns such as pricing and documentation clarity, the overall sentiment leans towards the effectiveness and reliability of Amazon Kinesis's streaming analytics capabilities.

### Real-Time Data Processing
Users generally praise Amazon Kinesis for its robust real-time data processing capabilities. The consensus among reviewers is that Amazon Kinesis excels in handling large volumes of data in real-time, allowing for immediate analysis and processing. While some users acknowledge the complexity of setup and maintenance, the overall sentiment leans towards the effectiveness of Amazon Kinesis in enabling real-time data streaming and processing. The service's ability to integrate with various analytic tools and AWS microservices further enhances its appeal for businesses seeking efficient real-time data processing solutions.

### Performance and Latency
Opinions on Amazon Kinesis's performance and latency capabilities vary among users. While some users praise its low latency and ease of integration with services like Lambda, others express concerns about the complexity of setup and maintenance, as well as the potential latency issues in data processing. The mixed feedback suggests that while Amazon Kinesis offers benefits such as scalability and real-time processing, some users may encounter challenges related to performance and latency that could impact their overall experience with the platform.

### AWS Integration
Users generally praise Amazon Kinesis for its strong integration with AWS services, particularly AWS Lambda and AWS IoT. The ease of setting up and managing integrations with other AWS resources like S3, SQS, and Kinesis Firehose is highlighted as a major advantage. While some users find the initial setup complex and the pricing slightly on the higher side, the overall consensus is positive regarding the seamless integration capabilities of Amazon Kinesis with the AWS ecosystem. The ability to connect and stream data effortlessly between different AWS services is a key factor that users appreciate, despite some minor drawbacks mentioned by a few reviewers.

### Application and Service Integration
Users generally praise Amazon Kinesis for its robust application and service integration capabilities. The platform's ease of integration with various AWS services, such as Lambda, S3, and Firehose, is highlighted as a significant advantage. Many users appreciate the scalability and reliability of Amazon Kinesis when it comes to integrating with multiple servers and handling streaming data efficiently. While some users find the initial setup complex and the UI less intuitive, the overall sentiment leans towards the positive side, with users acknowledging the platform's ability to streamline data integration processes effectively.

### Microservices and Serverless Architecture
Users consistently highlight Amazon Kinesis's microservices and serverless architecture as key strengths, emphasizing the platform's ease of use, flexibility, and close integration with other AWS services. The ability to handle real-time streaming data efficiently and the support for building comprehensive infrastructures within the AWS ecosystem are frequently cited as major advantages. Additionally, users appreciate the scalability and the continuous improvements made to enhance the overall experience of utilizing microservices and serverless capabilities within Amazon Kinesis.

### IoT Data Handling
Users generally find Amazon Kinesis's IoT data handling capabilities to be a strong point, with positive feedback highlighting its efficiency in processing real-time data from IoT devices. The ability to route data to different destinations and integrate with various AWS resources like Kinesis for reliable and fast data streaming has been well-received. Some users have expressed a desire for more extensive support for ETL processes and additional sample projects to aid in getting started with IoT data handling using Kinesis. Despite these minor concerns, the overall sentiment leans towards Amazon Kinesis being a valuable tool for managing IoT data effectively.

### Data Storage and Transfer
Users generally praise Amazon Kinesis for its efficient data storage and transfer capabilities. They appreciate the ease of scaling, processing, and managing data without the need for internal servers. The integration with AWS services like Lambda and S3 is highlighted as a major advantage, allowing for reliable and cost-effective data transfer between external systems and AWS accounts. While some users find the service to be slightly expensive for large data transfers and mention a learning curve for configuration, overall, Amazon Kinesis is lauded for its ability to handle high throughput data transfer and storage effectively.

### Event and Log Management
Users consistently highlight Amazon Kinesis's event and log management capabilities as a key strength of the platform. They appreciate the ease of integrating Kinesis with various AWS resources to efficiently process and analyze logs in real-time. Despite some minor delays in log synchronization when integrating with other tools, users find Kinesis to be a reliable and scalable solution for capturing, processing, and forwarding logs from multiple sources. The ability to aggregate logs from different instances into a centralized location and derive real-time metrics is particularly valued by users. Additionally, the flexibility of Kinesis in supporting multiple integrations, such as forwarding ECS service data to Coralogix, and its scalability during peak hours without data drop issues, further enhance its appeal for event and log management tasks.

### Cost Management and Optimization
Users generally have mixed opinions about Amazon Kinesis's cost effectiveness and pricing strategies. Some users appreciate the pay-as-you-go model and the ability to scale effortlessly to handle high message volumes, which they find cost-effective. However, others express concerns about the high costs associated with scaling up data volumes and the complexity of understanding pricing structures, especially when compared to open-source alternatives like Kafka. Proper management of sharding is highlighted as a key factor in reducing costs effectively. It is recommended for users to have a clear understanding of data volume requirements and sharding strategies to accurately estimate the cost of using Amazon Kinesis.

### Scalability Solutions
Users generally praise Amazon Kinesis for its exceptional scalability solutions, highlighting its ability to handle large volumes of data efficiently and grow alongside their needs. While some users mention challenges with advanced features like sharding and horizontal scaling, overall, the consensus leans towards the platform's strong scalability capabilities. The ease of integration with other AWS services and the platform's reliability in managing real-time streaming data are also frequently commended by users. Despite some minor complexities, Amazon Kinesis's scalability solutions are widely regarded as a valuable asset for businesses seeking robust data processing and analysis capabilities.

Amazon Kinesis is a streaming analytics suite for data intake from video or other disparate sources and applying analytics for machine learning (ML) and business intelligence.

Amazon Kinesis Data Streams

Amazon Kinesis Data Firehose

Amazon Kinesis Data Analytics

Amazon Kinesis Video Streams

Microsoft offers Azure Stream Analytics for IoT and connected devices,  supporting real-time analytics and reporting.

Standard

Dedicated

Amazon Kinesis

Streaming Analytics is performing analytic computations on data streaming from devices, sensors, websites, social media, applications, infrastructure systems, and more. This category of tools is an evolution of Complex Event Processing (CEP) software, designed specifically for the big data era.

Likelihood to Recommend

Support Rating

Real-Time Data Analysis

Data Ingestion from Multiple Data Sources

Low Latency

Integrated Development Tools

Data wrangling and preparation

Linear Scale-Out

Data Enrichment

Amazon strives to be Earth's most customer-centric company where people can find and discover virtually anything they want to buy online. By giving customers more of what they want - low prices, vast selection, and convenience - Amazon continues to grow and evolve as a world-class e-commerce platform.

Founded by Jeff Bezos, the Amazon.com website started in 1995 as a place to buy books because of the unique customer experience the Web could offer book lovers. Bezos believed that only the Internet could offer customers the convenience of browsing a selection of millions of book titles in a single sitting. During the first 30 days of business, Amazon fulfilled orders for customers in 50 states and 45 countries - all shipped from his Seattle-area garage.

Amazon's evolution from Web site to e-commerce partner to development platform is driven by the spirit of innovation that is part of the company's DNA. The world's brightest technology minds come to Amazon.com to research and develop technology that improves the lives of shoppers and sellers around the world.

Streaming Analytics is performing analytic computations on streaming data. Data streams can come from devices, sensors, websites, social media, applications, infrastructure systems, and more.

Visualization Dashboards

Easy-to-understand pictorial illustration of data with graphs charts and dashboards

Ability to ingest data from many sources including Internet of Things (IoT) endpoint data, stock trading data etc, as well as static data

How many milli-seconds or seconds it takes to ingest, analyze and respond to an incoming event or
data-point

Tools to allow developers to rapidly create streaming applications via a graphical user interface and selection of predefined
functions and operators

Tools to rapidly prepare data for analysis by normalization, data cleansing, etc.

Easy to scale out or scale down by visually changing the resource allocation. This allows changes in load or traffic to be handled without interruptions

Machine Learning Automation

Machine learning helps automate predictive scoring on streaming data

Ability to enrich the data stream with static reference data

TrustRadius is a technology and business research firm based in Austin, Texas. The company is known as a review platform for verified B2B technology and software reviews through a proprietary algorithm and human verification.

TrustRadius

Aws lambda

Duplicate records

User friendly

Integration with aws

Process data

Real time

Analyze data

Intuitive to use

Data collected

Easy to integrate

High performance

Change events

Aws ecosystem

Large projects

Home

Streaming Analytics Software

We used Kinesis as the basis for information distribution for a mission-critical business operation. We expected a huge amount of mini events reporting information between a Iot of devices, databases, geo info systems, and web sites, as well as reporting (business intelligence). Kinesis was used as the messaging pipeline (nowadays streaming) that glued everything together.

Integrating with other Amazon services
Scaling requests
Totally serverless platform
Simple management

Extended fan-out is complicated to manage.
Documentation is confusing.
VPC integration

Lower the cost of implementation.
Good scaling up
Simple integration, especially with Lambda

AWS Lambda, AWS Elastic Beanstalk and AWS Glue

AWS Elastic Beanstalk, AWS Lambda, AWS Glue

CEO (Chief Executive Officer)

Amazon Kinesis is being used to stream telemetry data from millions of connected devices on the field - helping to drive command and control use cases and also sensor data for performance monitoring. Our volume is billions of messages per day, real-time, with aggressive requirements on response and processing time. All messages are stored in a big data structure and also two-way communication with a mobile app.

Processing huge loads of data
Integrating well with IoT Platform on Amazon
Integration with overall AWS Ecosystem
Scalability

Improve integration with AWS Lambda
Some duplicate records coming from the stream

Significantly reduced the cost of our IoT platform, compared to other Cloud providers.
Better user experience.
Enabled real "real-time analytics."

Tableau Server, Amazon Redshift, Apache Spark

We currently use Kinesis as a trigger for Amazon Lambda functions, however we're working at switching back to SQS since Lambda now works with SQS directly. We simply used Kinesis as a temporary method of plumbing requests from one Lambda function to another.

We also use it as a byproduct of using DynamoDB Streams, but only in so far as that's used under-the-hood to link changes from DynamoDB to a Lambda trigger.

Link DynamoDB change events directly to Lambda
Fast streaming of events
Easy to "tail" changes/latest events

Not a queue system, so little visibility into "backlog" if there is any
Confusing terminology to make sure events aren't missed
Sometimes didn't seem to trigger Lambda functions, or dropped events when a lot came in

Caused us to need to re-engineer some basic re-try logic
Caused us to drop some content without knowing it
Made monitoring much more difficult
We eventually switched back to SQS because Kinesis is not the same as a Queue system

Amazon Simple Queue Service (SQS), Amazon DynamoDB, AWS Lambda, Amazon API Gateway

Amazon Kinesis

Overview

What is Amazon Kinesis?