Table of contents
Real-time data processing
Amazon Kinesis allows for real-time processing of data streams, enabling rapid ingestion and analysis of data as it arrives.
Scalability
Kinesis is highly scalable and can handle massive volumes of streaming data, expanding automatically to meet your needs.
Fully managed service
As a fully managed service, Kinesis handles infrastructure maintenance, provisioning, and scaling, reducing operational overhead.
Integration with AWS ecosystem
Kinesis integrates seamlessly with other AWS services such as Lambda, Redshift, S3, and Elasticsearch, facilitating comprehensive data workflows.
Multiple data stream applications
The service supports different types of data stream applications including data delivery, analytics, and real-time processing, making it versatile.
Security
Offers robust security through integration with AWS Identity and Access Management (IAM), encryption at rest with AWS Key Management Service (KMS), and in-transit encryption.
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Yes, Amazon Kinesis is a good option for organizations that need to process and analyze large streams of data in real-time. Its scalability, ease of integration with existing AWS infrastructure, and advanced features make it a preferred choice for many enterprise-level applications.
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Check the traffic stats of Amazon Kinesis on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Amazon Kinesis on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Amazon Kinesis's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Amazon Kinesis on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Amazon Kinesis on Reddit. This can help you find out how popualr the product is and what people think about it.
High-throughput Kinesis or Kafka consumers where processing latency compounds. - Source: dev.to / 3 months ago
Step 4 Examine the compute usage and identify suitable services and workloads. Services like EKS, OpenSearch, CloudWatch, Kinesis, and Firehose suggest stateless/fault-tolerant/bath-oriented workloads suitable for Spot Instances. Therefore EKS worker nodes, data processing jobs, CI/CD workloads or OpenSearch indexing tasks can be migrated to Spot. - Source: dev.to / about 1 year ago
Real-Time Processing โ With Amazon Kinesis and Amazon DynamoDB, fintech firms can analyze transactions instantly, identify fraud before it happens. - Source: dev.to / over 1 year ago
Amazon Kinesis is a fully managed real-time data streaming service by AWS, designed for large-scale data ingestion and processing. - Source: dev.to / almost 2 years ago
Https://aws.amazon.com/kinesis/ > Amazon Kinesis Data Streams is a serverless streaming data service that simplifies the capture, processing, and storage of data streams at any scale. I'd never heard of that one. - Source: Hacker News / about 2 years ago
Event Consumers: Services that actively listen for events and respond accordingly. These consumers can be easily implemented using microservices, AWS Lambda or Amazon Kinesis (for ingesting, processing, and analyzing streaming data in real-time). - Source: dev.to / over 2 years ago
When you see Amazon Kinesis as an option, this becomes the ideal option to process data in real time. Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit... - Source: dev.to / over 2 years ago
RisingWave is an open-source streaming database that has built-in fully-managed CDC source connectors for various databases, also it can collect data from other sources such Kafka, Pulsar, Kinesis, or Redpanda and it allows you to query real-time streams using SQL. You can get a materialized view that is always up-to-date. - Source: dev.to / over 3 years ago
For example, RisingWave is one of the fastest-growing open-source streaming databases that can ingest data from Apache Kafka, Apache Pulsar, Amazon Kinesis, Redpanda, and databases via native Change data capture connections or using Debezium connectors to MySQL and PostgreSQL sources. Previously, I wrote a blog post about how to choose the right streaming database that discusses some key factors that you should... - Source: dev.to / over 3 years ago
RisingWave is an open-source distributed SQL database for stream processing. RisingWave accepts data from sources like Apache Kafka, Apache Pulsar, Amazon Kinesis, Redpanda, and databases via native Change data capture connections to MySQL and PostgreSQL sources. It uses the concept of materialized view that involves caching the outcome of your query operations and it is quite efficient for long-running stream... - Source: dev.to / over 3 years ago
You can ingest data from different data sources such as message brokers Kafka, Redpanda, Kinesis, Pulsar, or databases MySQL or PostgreSQL using their Change Data Capture (CDC) which is the process of identifying and capturing data changes. - Source: dev.to / over 3 years ago
Amazon Kinesis is a platform for real-time data processing, ingestion, and analysis. Kinesis Data Streams is a serverless streaming data service (part of the Kinesis streaming data platform, along with Kinesis Data Firehose, Kinesis Video Streams, and Kinesis Data Analytics) that enables developers to collect, process, and analyze large amounts of data in real-time from various sources such as social media, IoT... - Source: dev.to / over 3 years ago
Amazon Kinesis (analytics) Amazon Kinesis is a platform for streaming data on AWS. Kinesis offers services that simplify the loading and analysis of streaming data. Https://aws.amazon.com/kinesis/. - Source: dev.to / over 3 years ago
For real-time streaming, we have other frameworks and tools like Apache Kafka, ActiveMQ, and AWS Kinesis. - Source: dev.to / over 3 years ago
AWS Lambda is a serverless computing platform that lets you run your code in response to events, such as changes to data in an S3 bucket or a new line of a stream in a Kinesis stream. It's a great way to build scalable and cost-effective applications, and it's a must-know tool for any serverless developer. - Source: dev.to / over 3 years ago
Have you explored Kinesis handing the streaming with events to Lambda to handle storage on the cloud-side? Source: almost 4 years ago
Like many applications, our infrastructure relies on queues to decouple various components. In our system we use AWS Kinesis as a data stream, consumed by Broadway consumers for some critical parts of our infrastructure. We have found that sometimes our Broadway consumers for AWS Kinesis fail in ways that do not gracefully recover when they crash. For example, each Kinesis shard has its own supervision tree... - Source: dev.to / about 4 years ago
Amazon Kinesis is the real-time stream processing service of AWS. Whether you got video, audio, or IoT streaming data to handle, Kinesis is the way to go. - Source: dev.to / about 4 years ago
If youโre using a tool like Datadog, you might forward your CloudWatch logs and metrics to Datadog using a Forwarder Lambda function. If youโre also using Kinesis in your tech stack to quickly process streaming data, you can use their Firehose delivery stream to forward logs to Datadog as well. - Source: dev.to / over 4 years ago
AWS Lambda functions recently announced an enhancement with event-triggers for DynamoDB, Amazon SQS, Amazon Kinesis as event sources which makes it easier for event based Lambda function triggers to get invoked only based on the filter expression. You can read about the official announcement from AWS Blog post. - Source: dev.to / over 4 years ago
Right now I'm just using Axis Companion with edge storage. I only have Mac computers and don't want to run a VMS so I'm working on some code to stream recordings and analytics metadata to AWS Kinesis. In my personal opinion, edge analytics with cloud storage is the way of the future. I have a basic prototype working but it's not without frustrations. The analytics metadata stream is only available through ONVIF... Source: over 4 years ago
Amazon Kinesis, a fully managed real-time data streaming service by AWS, continues to establish itself as a pivotal player in the fields of data analytics, big data management, and stream processing. The platform is widely recognized for handling massive data influx with ease, facilitating real-time processing and swift data analysis. Kinesis is favorably positioned alongside competitors like Apache Kafka, Google Cloud Dataflow, and Azure Stream Analytics, offering scalability, flexibility, and reliability.
Amazon Kinesis is esteemed for its capability to manage streaming data without the burdens usually associated with additional infrastructure management. This attributes to its popularity as a viable alternative to Apache Kafka. With its ability to process data from disparate sourcesโranging from IoT sensors to social mediaโAmazon Kinesis is embraced by various industries that demand real-time data insights, including fintech and video streaming sectors.
Scalability and Flexibility: Kinesis's serverless architecture allows it to capture, process, and manage data streams of vast scale efficiently, promoting cost-effective solutions for businesses. This architectural choice also supports ease of integration across AWS services such as AWS Lambda, which is instrumental in developing serverless applications.
Ease of Use and Integration: The ease with which Kinesis can integrate with other AWS services, especially for event-driven architectures or serverless computing, is cited as a significant advantage. This integration streamlines the deployment of bespoke data solutions and quickens the time-to-insight for real-time applications.
Real-Time Data Processing: The platform is lauded for real-time data ingestion and processing capabilities, offering quick and actionable insights. This feature proves beneficial in scenarios like fraud detection in fintech domains, emphasizing Kinesis's role in enhancing proactive business strategies.
Kinesis serves a broad spectrum of applications, including collecting and analyzing logs, video, and audio data in real-time. The platform's utility spans industry verticals, enabling applications that leverage geospatial data, engage in social network analysis, and support Internet of Things (IoT) infrastructures.
While Amazon Kinesis is acclaimed for its capabilities, users have noted some challenges, particularly involving the maintenance of consumer components when dealing with application integrations. For instance, users encountered issues concerning the resilience of shard consumers, suggesting room for improvement in fault recovery processes.
Event-Driven and Stream Processing Ecosystem: Within the ecosystem of event-driven architectures and stream processing solutions, Kinesis provides robust CDC (Change Data Capture) capabilities. However, as competitive technologies evolve, keeping up with enhancements within the broader streaming database landscape becomes a strategic consideration.
Amazon Kinesis is a cornerstone in the real-time data streaming space, offering robust scalability, flexibility, and an array of features that align with modern data processing needs. As businesses increasingly pivot toward data-driven strategies, Kinesis remains a formidable contender, particularly for enterprises already embedded within the AWS ecosystem. Nonetheless, like any sophisticated platform, ongoing assessment and adaptation to emerging challenges and competition in streaming technologies will further cement its relevance and efficacy in dynamic tech landscapes.
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