Exploring the Power of AWS Kendra
What is AWS Kendra?
The Europe (London) region now has access to AWS Kendra, an intelligent search service that aids organisations in effortlessly locating necessary information. It employs natural language processing and machine learning to comprehend the context of a query and deliver relevant outcomes.
AWS Kendra utilises machine learning and natural language processing to understand the context of a search and deliver suitable outcomes from a wide array of data sources. It has a user-friendly interface allowing users to search for information using natural language queries, and the results are tailored to the user’s context. The service is designed to operate with a wide range of data sources, such as databases, web pages, file systems, and S3 buckets. It also supports a broad range of file formats and languages, making it simple to incorporate with existing knowledge repositories.
Benefits of AWS Kendra
Below are some of the benefits AWS Kendra provides.
- Accurate and Relevant Results
AWS Kendra utilises machine learning algorithms to deliver precise and pertinent search results. By employing Natural Language Processing, it can comprehend the context of user inquiries and retrieve information that aligns with their intent. It can identify synonyms, acronyms, and other variations of a term, and even furnish answers to multi-step questions.
2. Easy Integration
AWS Kendra can be easily integrated with your existing knowledge repositories, allowing you to utilise your current technology stack, making it easy to set up without the need for any customisation.
3. Scalability
AWS Kendra is built on top of AWS, which means it can scale up or down depending on your needs. This makes it ideal for requirements of all sizes.
4. Security
AWS Kendra provides robust security features that help protect your data. It uses encryption at rest (KMS) and in transit (HTTPS) to ensure that data is sent securely. It also supports multi-factor authentication to guarantee that only authorised users can access sensitive data.
Drawbacks of AWS Kendra
All services have drawbacks, here are a few for AWS Kendra.
- Limited Customization
Whilst AWS Kendra is easy to integrate with your current knowledge repositories, it has limited customisation options. This means that you may need to incorporate third-party tools or services to fully customise the service to your needs.
2. Relatively New
AWS Kendra is still a relatively new service, which means that it may not have the same level of stability as other enterprise search solutions.
Why use AWS Kendra instead of AWS Elasticsearch?
AWS Elasticsearch is another service offered by AWS that provides enterprise-level search capabilities. While both services offer similar functionality, some key differences set them apart.
- Deployment Options
AWS Elasticsearch can be deployed on-premises or in the cloud, whilst AWS Kendra is a fully managed service that is only available in the cloud.
2. Search Capabilities
While both services offer robust search capabilities, AWS Kendra offers more advanced functionality, including natural language queries and machine learning algorithms for relevance ranking. Elasticsearch uses a JSON-based query language that supports keywords, operators, wildcards, regular expressions, etc.
3. Data Sources
AWS Elasticsearch supports a wider range of data sources, including unstructured data and log files. AWS Kendra focuses primarily on structured data sources, such as databases and web pages. On the other hand, AWS Elastisearch can ingest data from AWS services using built-in integrations, or from other third-party sources using custom applications.
4. Cost
When it comes to cost, AWS Kendra is usually pricier than AWS Elasticsearch because of its complex machine-learning features and completely managed service approach. Amazon Kendra’s pricing is determined by the total number of queries and documents indexed per month, as well as the storage size of the index. On the other hand, Elasticsearch’s cost depends on the compute capacity and storage size of the cluster.
5. Search interface
Amazon Kendra provides a straightforward interface that can be integrated into your applications or web pages. Elasticsearch does not provide a default search interface, but you can use third-party tools such as Kibana or custom applications to create one.
AWS Kendra within Enterprise Architectures.
AWS Kendra can be integrated in various ways to enhance search capabilities within enterprise architectures. Here are a few example use cases:
- Intranet Search
Many organisations have vast amounts of knowledge stored in intranets or internal repositories. Implementing Kendra as the search engine for intranet sites can significantly improve search accuracy and relevance. By connecting Kendra to sources like SharePoint or databases, you can easily find documents and information you need, using natural language queries.
2. Customer Support Portals
Customer support teams often deal with a lot of queries from customers. Integrating Kendra into customer support portals, you can enhance self-service options alongside users finding relevant answers to their questions by typing natural language queries, reducing the need for human intervention. Kendra can understand the intent, providing accurate results which can greatly improve the customer experience.
3. Health Records
Healthcare organisations store massive amounts of patient-related data in electronic health records. Kendra can be integrated into health record systems to enable healthcare staff to quickly find relevant patient information. Whether searching for diagnoses, medications, or treatments, Kendra can streamline the retrieval of critical information and save time for busy medical staff.
4. Compliance and Regulatory Search
Many industries, such as finance and healthcare, face strict compliance and regulatory requirements. Kendra can help organisations ensure they are complying with these rules by implementing search engines specific to compliance and regulatory frameworks. Kendra’s search capabilities can help identify relevant policies, or procedures needed to meet regulatory requirements.
In conclusion, both AWS Kendra and Elasticsearch have their respective benefits and drawbacks. AWS Kendra is an out-of-the-box solution that is easy to set up and provides excellent natural language processing, making it easier for users to find information. Whereas Elasticsearch is a solution that requires more configuration but offers more customisation options, making it ideal for organisations with specific search requirements.
About the Author:
Laura Gardner is an AWS Architect at Version 1.
