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Amazon Kinesis VS Amazon Elasticsearch Service

Compare Amazon Kinesis VS Amazon Elasticsearch Service and see what are their differences

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Contents:
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  2. » Videos
  3. » Reviews
  4. » Alternatives

Amazon Kinesis logo Amazon Kinesis

Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

Amazon Elasticsearch Service logo Amazon Elasticsearch Service

Amazon Elasticsearch Service is a managed service that makes it easy to deploy, operate, and scale Elasticsearch in the AWS Cloud.
  • Amazon Kinesis Landing page
    Landing page //
    2022-01-28
  • Amazon Elasticsearch Service Landing page
    Landing page //
    2023-03-13

Amazon Kinesis features and specs

  • 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.

Possible disadvantages of Amazon Kinesis

  • Cost
    While pricing is scalable, costs can escalate quickly with high data throughput and storage requirements, potentially becoming expensive for large-scale implementations.
  • Complex setup and management
    Despite being a managed service, the initial setup and tuning of Kinesis can be complex and may require specialized knowledge.
  • Latency
    Although designed for real-time data processing, there can be minor latency involved that might not fit ultra-low latency requirements.
  • Limited data retention
    Kinesis typically supports up to 7 days of data retention in streams, which might be insufficient for use cases requiring longer retention periods without extra storage solutions.
  • API Rate Limits
    API access to Kinesis is subject to rate limits, which could impact applications requiring high-frequency data ingestion and retrieval.
  • Dependence on AWS services
    Tight integration with AWS services can pose a challenge for organizations looking for a multi-cloud or cloud-agnostic strategy.

Amazon Elasticsearch Service features and specs

  • Scalability
    Amazon OpenSearch Service allows for easy scalability of clusters based on demand, without the need to manually manage infrastructure.
  • Managed Service
    The service is fully managed by AWS, including automatic backups, monitoring, and updates, reducing operational overhead for users.
  • Integration
    Seamless integration with other AWS services such as Amazon VPC, AWS Lambda, and Amazon S3 for streamlined workflows and enhanced data analysis capabilities.
  • Security
    Built-in security features such as VPC support, IAM policies, and data encryption at rest and in transit ensure data is well-protected.
  • High Availability
    The service offers multiple availability zones for high availability and durability of data.
  • Cost Efficiency
    Comes with a pay-as-you-go pricing model which allows users to efficiently manage costs and scale resources according to budget and usage.

Possible disadvantages of Amazon Elasticsearch Service

  • Vendor Lock-in
    Relying on Amazon for Elasticsearch service can lead to vendor lock-in, making it hard to transition to other services or platforms without significant effort.
  • Cost
    While the pricing model is flexible, the costs can accumulate quickly with large-scale deployments, potentially leading to high expenses.
  • Limited Customization
    Being a managed service, it offers less flexibility and customization options compared to self-managed solutions.
  • Version Lag
    The service may not always be in sync with the latest releases and may lag behind the open-source Elasticsearch in terms of new features.
  • Complexity
    Setting up and optimizing Amazon OpenSearch Service can be complex, requiring a good understanding of both the service and underlying Elasticsearch technology.

Analysis of Amazon Kinesis

Overall verdict

  • 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.

Why this product is good

  • Amazon Kinesis is generally considered a robust choice for real-time data processing because it can ingest, buffer, and process streaming data at scale. It offers features like durable storage, the ability to handle high throughput with low latency, and seamless integration with other AWS services. This makes it particularly well-suited for applications that require real-time analytics, data lake integrations, or reacting to changing data streams with minimal delay.

Recommended for

  • Organizations dealing with large quantities of streaming data
  • Businesses needing real-time data analytics and processing
  • Developers looking for seamless integration with AWS services
  • Teams wanting to build real-time machine learning models
  • Companies implementing IoT solutions requiring data streaming

Amazon Kinesis videos

+ Add

AWS Big Data - Amazon Kinesis Analytics Introduction and Demonstration

More videos:

  • Review - Analyzing Data Streams in Real Time with Amazon Kinesis: PNNL's Serverless Data Lake Ingestion

Amazon Elasticsearch Service videos

+ Add

Amazon Elasticsearch Service Deep Dive - AWS Online Tech Talks

More videos:

  • Review - Moving From Self-Managed Elasticsearch to Amazon Elasticsearch Service - AWS Online Tech Talks

Category Popularity

0-100% (relative to Amazon Kinesis and Amazon Elasticsearch Service)

Amazon Kinesis

Amazon Elasticsearch Service

Stream Processing
83 83%
Stream Processing
17% 17
Custom Search Engine
0 0%
Custom Search Engine
100% 100
Data Management
100 100%
Data Management
0% 0
Custom Search
0 0%
Custom Search
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Amazon Kinesis and Amazon Elasticsearch Service

Amazon Kinesis Reviews

Top 10 AWS ETL Tools and How to Choose the Best One | Visual Flow
Amazon Kinesis was built to handle massive amounts of data, allowing it to be uploaded to a Redshift cluster. After the event stream is read and the data is transformed, it is placed into a table in Amazon SCTS in an Amazon ES domain. Thus, there is no need to use a server (instead, you need to integrate AWS ETL and AWS Lambda).
Source: visual-flow.com
6 Best Kafka Alternatives: 2022’s Must-know List
Kinesis enables streaming applications to be managed without additional infrastructure management. This highly scalable platform can process data from various sources with low latency. Known for its speed, ease of use, reliability, and capability of cross-platform replication, Amazon Kinesis is one of the most popular Kafka Alternatives. It is used for many purposes,...
Source: hevodata.com
Top 15 Kafka Alternatives Popular In 2021
Amazon Kinesis, also known as Kinesis Streams, is a popular alternative to Kafka, for collecting, processing, and analyzing video and data streams in real-time. It offers timely and insightful information, streaming data in a cost-effective manner with complete flexibility and scalability. It is easy to ingest data encompassing audios, videos, app logs, etc. It offers an...
Source: www.spec-india.com
16 Top Big Data Analytics Tools You Should Know About
Amazon Kinesis is a massively scalable, cloud-based analytics service which is designed for real-time applications.
Source: www.analytixlabs.co.in

Amazon Elasticsearch Service Reviews

We have no reviews of Amazon Elasticsearch Service yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Amazon Kinesis should be more popular than Amazon Elasticsearch Service. It has been mentiond 27 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Amazon Kinesis mentions (27)

  • AWS Spot Instances: Business Case Essentials
    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 / 3 months ago
  • FINTECH SCALABILITY
    Real-Time Processing — With Amazon Kinesis and Amazon DynamoDB, fintech firms can analyze transactions instantly, identify fraud before it happens. - Source: dev.to / 7 months ago
  • Top 7 Kafka Alternatives For Real-Time Data Processing
    Amazon Kinesis is a fully managed real-time data streaming service by AWS, designed for large-scale data ingestion and processing. - Source: dev.to / about 1 year ago
  • AWS Operational issue – Multiple services in us-east-1
    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 1 year ago
  • Event-Driven Architecture on AWS
    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 1 year ago
View more

Amazon Elasticsearch Service mentions (12)

  • AWS Spot Instances: Business Case Essentials
    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 / 3 months ago
  • OpenSearch for humans
    This change triggered a response from Amazon Web Services, which offered OpenSearch (data store and search engine) and OpenSearch Dashboards (visualization and user interface) as Apache2.0 licensed open-source projects. - Source: dev.to / over 1 year ago
  • OpenSearch as Vector DB: Supercharge Your LLM
    Amazon OpenSearch Service allows you to deploy a secured OpenSearch cluster in minutes. - Source: dev.to / about 2 years ago
  • Building Serverless Applications with AWS - Data
    If yes to these, then OpenSearch is where you are looking. I rarely ever use OpenSearch on its own but usually pair it with DynamoDB. The performance of DDB and the power of searching with OpenSearch make a nice combination. And as with most things with Serverless, pick the right tool for the job. And when it comes to Data, there are so many choices because each one of these is specific to the problem it solves. - Source: dev.to / about 2 years ago
  • Advice on a simple database architecture
    Have you looked into Amazon OpenSearch Service (https://aws.amazon.com/opensearch-service/)? You should be able to load the log files into that service and then query it there. Should simplify things a lot. Source: over 2 years ago
View more

What are some alternatives?

When comparing Amazon Kinesis and Amazon Elasticsearch Service, you can also consider the following products

Confluent - Confluent offers a real-time data platform built around Apache Kafka.

ElasticSearch - Elasticsearch is an open source, distributed, RESTful search engine.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

PieSync - Seamless two-way sync between your CRM, marketing apps and Google in no time

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

TIBCO Spotfire - TIBCO Spotfire is a Business Intelligence (BI) solution that provides users with executive dashboards, data visualization, data analytics and KPIs push to mobile devices.

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