• Big Data
  • 4 min read

How ElasticSearch And Big Data Will Get Along

how elasticsearch and big data will get along
If I have to differentiate between good and better then I would say that collecting data is good but Big Data is far better than that. Once you collect a certain amount of big data, analyzing it comes in second place. But analyzing big data is not that easy. You will have to make the different data sources for different platforms considering Social media, databases, sensor data, etc. Because of the data that goes on social media, interpretation will get tougher. 
 
There is some free and open-source software like ElasticSearch, Apache Solr, and Sphinx, they offer good software for your enterprise to search for certain things. ElasticSearch has reached machine learning but in particular, this article is kept in mind that it sticks to only big data analytics.
 
Grab a thorough knowledge of elastic search Machine learning in elastic search is very new, on the other hand, big data analytics has its roots already in ElasticSearch. 
 
Now that you know what is an ElasticSearch, let’s move to the next topic and let’s cover all the benefits that ElasticSearch offers.
 

Benefits of ElasticSearch 

What are those things for which we use ElasticSearch? Umm, Websearch, log analysis, and big data analytics. Elastic search is gaining more popularity because of its ease in installation, it also scales out hundred of nodes without any type of additional software. Also, its built-in REST API will give you ease and enhancement while working. 
 
Here are overviewed applications and benefits of ElasticSearch 

1. Real-time analytics 

Basically, the real-time analysis provides you the updated customer reviews and events such as website navigation, page view, and shopping cart view. Big data analytics is useful for those industries which are interested in dynamic analysis and reporting. Dynamic analysis is a quick responding trend to user behavior. ElasticSearch is available for real-time analytics it provides a speed to search and power to analytics. 

2. Full-text search 

Full-text searches involve a search engine looking through every word of every document it has in its database for matches to search terms. If you really want to deliver the most potent full-text search capabilities of any open source solution, Elasticsearch constructs distributed capabilities on top of Apache Lucene. Multilingual search, geolocation, contextual did-you-mean suggestions, autocomplete, and result snippets are all supported by the robust, developer-friendly Query API.
 

3. Resilient clusters 

The clusters in ElasticSearch will detect new or failed nodes, that’s why they are called resilient. It also rebalances the amount of data you have entered and ensures that is it even safe and accessible for certain searches. Index aliases let you browse the index using filters, and they may be modified without affecting your app's functionality.

4. Data Indexing 

You must be wondering what is data indexing now. You now know about big data, data indexing is a way of sorting a number of records in different fields. When you use Elastic Search you will get schema-free and document-oriented results. If you have complex entities in the real-world ElasticSearch will convert them into structured JSON documents. 
 
This JSON document will detect the data structure type and create an index that eventually makes your data searchable. It improves the speed of the data retrieval process on a database table. This makes the analytics process very simple. 

5. Developer Friendly API

Last but not least, developer-friendly API is the most appreciated thing in the Elastic Search domain. You may perform almost any action in ElasticSearch using a simple RESTful API using JSON over HTTP. There are certain different languages that have client libraries available for them. 
 
The quality and convenience of independently developed apps on your platform are enhanced by clear and concise documentation. Integration with Hadoop allows for quick responses to queries. With this method, Klout, a website that measures social media influence, was able to increase its user base from 100 million to 400 million, cut database update time in half from one day to four hours, and provide query results to business analysts in seconds rather than minutes.
 

What are the things you should use Elastic-Search for?

1. Log monitoring 

Provides fast and scalable logging that will not ever quit. 

2. Infrastructure monitoring 

Manage your metrics whenever you want wherever you want. 

3. APM

Gives insight into your application performance. 

4. Synthetic monitoring 

It monitors availability issues and also reacts to them. 

5. SIEM

Interact investigation and automated threat detection

6. Maps

Let’s explore the location data in real time. 

7. Enterprise Search

Elastic-Search provides you with the discovery and search experience for any use case.
 

Conclusion 

The use of ElasticSearch will reduce in average query response time in the portal. ElasticSearch is a whole new architecture and it will definitely enhance the scalability of the data search. It is considered the most important benefit of ElasticSearch. ElasticSearch adds up new data nodes in the future also it is easy to scale the infrastructure of ElasticSearch, linearly.

Also Read

Harnil Oza is a CEO of HData Systems - Data Science Company & Hyperlink InfoSystem a top mobile app development company in Canada, USA, UK, and India having a team of best app developers who deliver best mobile solutions mainly on Android and iOS platform and also listed as one of the top app development companies by leading research platform.

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