Amazon Kinesis Firehose聽vs聽DodgerCMS聽vs聽Elasticsearch

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Amazon Kinesis Firehose
Amazon Kinesis Firehose

117
53
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DodgerCMS
DodgerCMS

4
9
+ 1
0
Elasticsearch
Elasticsearch

11.3K
8.1K
+ 1
1.6K
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What is Amazon Kinesis Firehose?

Amazon Kinesis Firehose is the easiest way to load streaming data into AWS. It can capture and automatically load streaming data into Amazon S3 and Amazon Redshift, enabling near real-time analytics with existing business intelligence tools and dashboards you鈥檙e already using today.

What is DodgerCMS?

DodgerCMS is a static markdown CMS built on top of Amazon S3. It is a clean and simple alternative to heavy content management systems. There are no databases to manage, deployments to monitor, or massive configuration files. Just focus on writing your content and the results are live immediately.

What is Elasticsearch?

Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
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Why do developers choose Amazon Kinesis Firehose?
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Why do developers choose Elasticsearch?
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            What are some alternatives to Amazon Kinesis Firehose, DodgerCMS, and Elasticsearch?
            Stream
            Stream allows you to build scalable feeds, activity streams, and chat. Stream鈥檚 simple, yet powerful API鈥檚 and SDKs are used by some of the largest and most popular applications for feeds and chat. SDKs available for most popular languages.
            Kafka
            Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
            Amazon Kinesis
            Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.
            Google Cloud Dataflow
            Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.
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            Decisions about Amazon Kinesis Firehose, DodgerCMS, and Elasticsearch
            Tim Specht
            Tim Specht
            鈥嶤o-Founder and CTO at Dubsmash | 16 upvotes 140.5K views
            atDubsmashDubsmash
            Elasticsearch
            Elasticsearch
            Algolia
            Algolia
            Memcached
            Memcached
            #SearchAsAService

            Although we were using Elasticsearch in the beginning to power our in-app search, we moved this part of our processing over to Algolia a couple of months ago; this has proven to be a fantastic choice, letting us build search-related features with more confidence and speed.

            Elasticsearch is only used for searching in internal tooling nowadays; hosting and running it reliably has been a task that took up too much time for us in the past and fine-tuning the results to reach a great user-experience was also never an easy task for us. With Algolia we can flexibly change ranking methods on the fly and can instead focus our time on fine-tuning the experience within our app.

            Memcached is used in front of most of the API endpoints to cache responses in order to speed up response times and reduce server-costs on our side.

            #SearchAsAService

            See more
            Julien DeFrance
            Julien DeFrance
            Principal Software Engineer at Tophatter | 16 upvotes 1.1M views
            atSmartZipSmartZip
            Rails
            Rails
            Rails API
            Rails API
            AWS Elastic Beanstalk
            AWS Elastic Beanstalk
            Capistrano
            Capistrano
            Docker
            Docker
            Amazon S3
            Amazon S3
            Amazon RDS
            Amazon RDS
            MySQL
            MySQL
            Amazon RDS for Aurora
            Amazon RDS for Aurora
            Amazon ElastiCache
            Amazon ElastiCache
            Memcached
            Memcached
            Amazon CloudFront
            Amazon CloudFront
            Segment
            Segment
            Zapier
            Zapier
            Amazon Redshift
            Amazon Redshift
            Amazon Quicksight
            Amazon Quicksight
            Superset
            Superset
            Elasticsearch
            Elasticsearch
            Amazon Elasticsearch Service
            Amazon Elasticsearch Service
            New Relic
            New Relic
            AWS Lambda
            AWS Lambda
            Node.js
            Node.js
            Ruby
            Ruby
            Amazon DynamoDB
            Amazon DynamoDB
            Algolia
            Algolia

            Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

            I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

            For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

            Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

            Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

            Future improvements / technology decisions included:

            Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

            As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

            One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

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            Praveen Mooli
            Praveen Mooli
            Engineering Manager at Taylor and Francis | 12 upvotes 541.6K views
            MongoDB Atlas
            MongoDB Atlas
            Java
            Java
            Spring Boot
            Spring Boot
            Node.js
            Node.js
            ExpressJS
            ExpressJS
            Python
            Python
            Flask
            Flask
            Amazon Kinesis
            Amazon Kinesis
            Amazon Kinesis Firehose
            Amazon Kinesis Firehose
            Amazon SNS
            Amazon SNS
            Amazon SQS
            Amazon SQS
            AWS Lambda
            AWS Lambda
            Angular 2
            Angular 2
            RxJS
            RxJS
            GitHub
            GitHub
            Travis CI
            Travis CI
            Terraform
            Terraform
            Docker
            Docker
            Serverless
            Serverless
            Amazon RDS
            Amazon RDS
            Amazon DynamoDB
            Amazon DynamoDB
            Amazon S3
            Amazon S3
            #Backend
            #Microservices
            #Eventsourcingframework
            #Webapps
            #Devops
            #Data

            We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

            To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

            To build #Webapps we decided to use Angular 2 with RxJS

            #Devops - GitHub , Travis CI , Terraform , Docker , Serverless

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            Reviews of Amazon Kinesis Firehose, DodgerCMS, and Elasticsearch
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            How developers use Amazon Kinesis Firehose, DodgerCMS, and Elasticsearch
            Avatar of imgur
            imgur uses ElasticsearchElasticsearch

            Elasticsearch is the engine that powers search on the site. From a high level perspective, it鈥檚 a Lucene wrapper that exposes Lucene鈥檚 features via a RESTful API. It handles the distribution of data and simplifies scaling, among other things.

            Given that we are on AWS, we use an AWS cloud plugin for Elasticsearch that makes it easy to work in the cloud. It allows us to add nodes without much hassle. It will take care of figuring out if a new node has joined the cluster, and, if so, Elasticsearch will proceed to move data to that new node. It works the same way when a node goes down. It will remove that node based on the AWS cluster configuration.

            Avatar of Instacart
            Instacart uses ElasticsearchElasticsearch

            The very first version of the search was just a Postgres database query. It wasn鈥檛 terribly efficient, and then at some point, we moved over to ElasticSearch, and then since then, Andrew just did a lot of work with it, so ElasticSearch is amazing, but out of the box, it doesn鈥檛 come configured with all the nice things that are there, but you spend a lot of time figuring out how to put it all together to add stemming, auto suggestions, all kinds of different things, like even spelling adjustments and tomato/tomatoes, that would return different results, so Andrew did a ton of work to make it really, really nice and build a very simple Ruby gem called SearchKick.

            Avatar of AngeloR
            AngeloR uses ElasticsearchElasticsearch

            We use ElasticSearch for

            • Session Logs
            • Analytics
            • Leaderboards

            We originally self managed the ElasticSearch clusters, but due to our small ops team size we opt to move things to managed AWS services where possible.

            The managed servers, however, do not allow us to manage our own backups and a restore actually requires us to open a support ticket with them. We ended up setting up our own nightly backup since we do per day indexes for the logs/analytics.

            Avatar of Brandon Adams
            Brandon Adams uses ElasticsearchElasticsearch

            Elasticsearch has good tooling and supports a large api that makes it ideal for denormalizing data. It has a simple to use aggregations api that tends to encompass most of what I need a BI tool to do, especially in the early going (when paired with Kibana). It's also handy when you just want to search some text.

            Avatar of Ana Phi Sancho
            Ana Phi Sancho uses ElasticsearchElasticsearch

            Self taught : acquired knowledge or skill on one's own initiative. Open Source Search & Analytics. -time search and analytics engine. Search engine based on Lucene. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.

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