It is a managed service which makes the process easy to deploy, operate and scale elasticsearch in the cloud. It is a popular open source search and analytic engine, which is used for cases such as log analytics and real time application monitoring. We setup and configure our elasticsearch domain in minutes from its management console, its service provisions all resources which are useful for our cluster to launch it. It detects and replaces the failed elastic search nodes automatically, also reduces and overhead the associated with self managed infrastructure and elasticsearch software. It allows us to control access to your domain by using its identity and access management to backup our data using automated snapshots. In this blog we are going to cover the required topics regarding aws elasticsearch, such as what is aws elasticsearch, main features of aws elasticsearch, architecture of aws elasticsearch, pros and cons of aws elasticsearch.
Aws elasticsearch is an open-source service which is a restful and distributed search and analytics engine built on Apache Lucene. In short time it became popular and the common one for log analytics, and also for full text search and security intelligence, business analytics and operational intelligence. We can send data by using ingestion tools like log stash, amazon kinesis firehose in the form of JSON documents to elasticsearch. It automatically stores the original documents and adds search-abel references for the documents using elasticsearch. We can use kibana which is an open-source visualization tool with elasticsearch for our data visualization and to build interactive dashboards.
It is a free open-source software that can run elasticsearch on premises on amazon EC2 or on its services, by using amazon EC2 deployments we are responsible for installation of elasticsearch and other necessary software and provisioning infrastructure. Elasticsearch services are fully managed so we don't have to worry about time consuming management tasks like hardware provisioning,backups, failure recovery, monitoring, etc. Managing and scaling can be difficult and it requires expertise in setup and configuration, to make it easy for customers that helps to run elasticsearch.
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It is a fully managed service which makes it easy to deploy, secure and run cost effectively at scale, we can monitor and troubleshoot our applications by using these tools at scale we require. It is very useful by having many benefits, they are.
When we observe the architecture of aws elasticsearch, we can easily get an idea regarding different services which are provided. Its cloud formation template deploys the Amazon es domain, whether it may be hardware, software or the data exposed to elasticsearch endpoints. Those templates can easily launch instances of amazon EC2, which are separately available zones of amazon VPC network. For the main purpose of distributing traffic to proxy servers and to enable automatic recovery to maintain instant availability, highly available designs are used.
Architecture overview: This solution of deploying builds the above environment in the AWS Cloud. This figure analyzes the Text with aws Elasticsearch Service and its Comprehend architecture on AWS. Its CloudFormation template used to deploy an aws API Gateway, which invokes the proxy microservice AWS Lambda function. microservices of aws elasticsearch allows the business logic that is used to manage the configuration of preprocessing, other native search capabilities, native indexing, etc.
The microservice of aws elasticsearch used to interact with aws Comprehend, that is for text analysis, aws CloudWatch Logs for logging, metrics, etc. Amazon Elasticsearch Service, which is for documentation indexing. While the aws API receives an authorized request, then the proxy of aws microservice sends the request for aws Comprehend for text analysis. For Amazon ES a call is provided, that is used for indexing the data, and publishes logs and metrics for the CloudWatch. We can visualize the indexed data on the solution’s pre-configured dashboard of kibana.
[ Related article : KIbana course]
High performance: elasticsearch distributive nature enables it to process large volumes of the data in parallel and in quick findings, which match our queries.
It provides plugins and complementary tooling facility: It comes integrated with kibana which is a popular visualization and reporting tool, which also offers integrations along with beats and logstash, while enabling us to easily transform the source data to load it into the cluster. You may also use different types of open-source plugins, which are like language analyzers and suggesters which add rich functionality to our applications.
Real-time operations: Its operations such as reading or writing data, which is usually taking less time to complete. Which lets you use elasticsearch which is used for near real-time operations, and also use cases like application monitoring and anomaly detection.
Easy development of applications: It provides support for various languages, which includes java, python, ruby, php, etc.
Fast time to value: Aws elasticsearch offers APIs, which are simple and rest based, a simple interface of HTTP, JSON documents which are used as schema free. It was used for us to make it easy to get started and built applications for a variety of use cases.
Frequently asked Elasticsearch Interview Questions
This service is integrated with cloud watch our domain monitoring and cloud trail, which helps us for adding access for our domain. We can easily scale our cluster through a single API call or a few clicks in the management console. It gave us direct access to the elasticsearch API, and the applications which are already using our existing elasticsearch environment, which may work seamlessly. It also provides built in support for kibana and we can easily analyze and visualize our data. The continuous and automatic data loading into our domain are taken care of by its integrations.
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