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Amazon Kinesis vs Elasticsearch: What are the differences?
Introduction
Amazon Kinesis and Elasticsearch are two popular data processing and analysis tools used in various industries. While both offer capabilities to handle large amounts of data, there are several key differences between them.
1. Scalability and Data Storage
Amazon Kinesis is designed for real-time data streaming and processing. It allows you to collect, process, and analyze streaming data from various sources in real-time. Kinesis offers seamless scalability, automatically adjusting resources based on data volume. It also provides durable storage for data streams, allowing you to retain data for up to 365 days.
On the other hand, Elasticsearch is a distributed, highly scalable search and analytics engine. It is primarily used for indexing, searching, and analyzing data. Elasticsearch uses a distributed architecture, making it capable of handling large amounts of data and scaling horizontally across multiple nodes. However, Elasticsearch does not have built-in storage capabilities like Kinesis, and it relies on external storage systems.
2. Data Processing and Analytics
Amazon Kinesis provides real-time data processing capabilities. It allows you to perform data transformations, aggregations, and filtering on the streaming data using AWS Lambda, AWS Glue, or other compatible services. Kinesis also integrates well with other AWS services such as Amazon S3, Redshift, and EMR for further data processing and analysis.
Elasticsearch, on the other hand, excels in full-text search and advanced analytics. It provides powerful search capabilities, including fuzzy matching, phrase matching, and relevance scoring. Elasticsearch also supports aggregations, allowing you to summarize and extract insights from your data. With Elasticsearch's robust query DSL (Domain-Specific Language), you can easily craft complex queries and perform advanced analytics on your data.
3. Data Visualization and User Interface
Amazon Kinesis does not offer a built-in user interface for data visualization. However, it integrates well with AWS services like Amazon QuickSight and Kibana, which can be used to visualize and analyze the data collected by Kinesis in real-time.
On the other hand, Elasticsearch comes with Kibana, a powerful data visualization and exploration tool. Kibana provides a user-friendly interface for creating visualizations, dashboards, and reports based on Elasticsearch data. It offers a wide range of visualization options, including bar charts, line charts, maps, and more.
4. Data Retention and Archive
In terms of data retention and archive, Amazon Kinesis provides long-term storage for data streams. You can choose to retain the data for up to 365 days, which can be useful for compliance, audit, or historical analysis purposes. Kinesis also allows you to archive the data to Amazon S3 for cost-effective, long-term storage.
On the other hand, Elasticsearch does not have built-in capabilities for long-term data retention or archiving. It is designed more for real-time and near-real-time analysis and search use cases. If you need to retain the data for longer periods or archive it for compliance purposes, you would need to implement external solutions for data storage and archiving.
5. Pricing Model and Cost
Amazon Kinesis pricing is based on the number of shards, amount of data ingested, and data egress. It offers different pricing tiers based on the desired level of data processing and retention. The pricing can vary depending on the specific features and capabilities you choose to use.
Elasticsearch is open-source, but if you choose to use Amazon Elasticsearch Service, it is a managed service and has its own pricing model. The pricing is based on factors like instance type, storage, data transfer, and additional services like Kibana. It is important to carefully consider the cost implications of using Elasticsearch, especially if you have large amounts of data or require high levels of scalability.
6. Managed Service vs. Open-source
Amazon Kinesis is a fully managed service provided by Amazon Web Services (AWS). This means that AWS takes care of the underlying infrastructure, maintenance, and operational tasks, allowing you to focus on using the service and analyzing your data. This makes it easier to get started with Kinesis and reduces the operational burden.
On the other hand, Elasticsearch is open-source software that can be self-hosted or managed through a third-party service. If you choose to self-host, you are responsible for managing the infrastructure, scaling, and maintenance of Elasticsearch. If you opt for a managed Elasticsearch service like Amazon Elasticsearch Service, some of the operational tasks are taken care of by the service provider, but you still have more control and responsibility compared to a fully managed service like Kinesis.
In Summary, Amazon Kinesis is a scalable, real-time data streaming and processing service with built-in data storage capabilities, while Elasticsearch is a distributed search and analytics engine primarily used for indexing and searching data. Kinesis excels in real-time data processing, integrates well with other AWS services, and provides long-term data retention options. Elasticsearch is powerful for full-text search, advanced analytics, and data visualization with its bundled Kibana tool. Kinesis is a fully managed service, while Elasticsearch can be self-hosted or managed through a third-party service.
Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?
(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.
Thank you!
Hi Rana, good question! From my Firebase experience, 3 million records is not too big at all, as long as the cost is within reason for you. With Firebase you will be able to access the data from anywhere, including an android app, and implement fine-grained security with JSON rules. The real-time-ness works perfectly. As a fully managed database, Firebase really takes care of everything. The only thing to watch out for is if you need complex query patterns - Firestore (also in the Firebase family) can be a better fit there.
To answer question 2: the right answer will depend on what's most important to you. Algolia is like Firebase is that it is fully-managed, very easy to set up, and has great SDKs for Android. Algolia is really a full-stack search solution in this case, and it is easy to connect with your Firebase data. Bear in mind that Algolia does cost money, so you'll want to make sure the cost is okay for you, but you will save a lot of engineering time and never have to worry about scale. The search-as-you-type performance with Algolia is flawless, as that is a primary aspect of its design. Elasticsearch can store tons of data and has all the flexibility, is hosted for cheap by many cloud services, and has many users. If you haven't done a lot with search before, the learning curve is higher than Algolia for getting the results ranked properly, and there is another learning curve if you want to do the DevOps part yourself. Both are very good platforms for search, Algolia shines when buliding your app is the most important and you don't want to spend many engineering hours, Elasticsearch shines when you have a lot of data and don't mind learning how to run and optimize it.
Rana - we use Cloud Firestore at our startup. It handles many million records without any issues. It provides you the same set of features that the Firebase Realtime Database provides on top of the indexing and security trims. The only thing to watch out for is to make sure your Cloud Functions have proper exception handling and there are no infinite loop in the code. This will be too costly if not caught quickly.
For search; Algolia is a great option, but cost is a real consideration. Indexing large number of records can be cost prohibitive for most projects. Elasticsearch is a solid alternative, but requires a little additional work to configure and maintain if you want to self-host.
Hope this helps.
Pros of Amazon Kinesis
- Scalable9
Pros of Elasticsearch
- Powerful api328
- Great search engine315
- Open source231
- Restful214
- Near real-time search200
- Free98
- Search everything85
- Easy to get started54
- Analytics45
- Distributed26
- Fast search6
- More than a search engine5
- Great docs4
- Awesome, great tool4
- Highly Available3
- Easy to scale3
- Potato2
- Document Store2
- Great customer support2
- Intuitive API2
- Nosql DB2
- Great piece of software2
- Reliable2
- Fast2
- Easy setup2
- Open1
- Easy to get hot data1
- Github1
- Elaticsearch1
- Actively developing1
- Responsive maintainers on GitHub1
- Ecosystem1
- Not stable1
- Scalability1
- Community0
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Cons of Amazon Kinesis
- Cost3
Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4
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