Data Analytics Made Easier With Elasticsearch
Data Analytics Made Easier With Elasticsearch
Elasticsearch is popular for analytics because it's easy to install, scales out to hundreds of nodes with no additional software, and is easy to work with due to its built-in REST API.
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Companies all across the globe have cashed in on collecting as much data as possible in order to have better insights. The mindset is quite straightforward when it comes to leveraging heaps of data to drive business through better decision making.
Collecting data is good and collecting big data is better, but the process of assessing and analyzing big data not so easy. It requires knowledge of enterprise search engines for making content from different sources like enterprise databases, social media, sensor data, etc. searchable to a defined audience. Elasticsearch, Apache Solr, and Sphinx are some of the free and open-source enterprise search software.
Before we dive in, let's go through the basics of Elasticsearch. It is the main product of a company called Elastic. It is a very useful tool for indexing of documents coupled with full text-based search. The domain-specific query language (JSON-based) is simplistic yet highly formidable, which makes it the default standard when it comes to search integration. Elasticsearch is mainly used for web search, log analysis, and big data analytics. Often compared with Apache Solr, both depend on Apache Lucene for low-level indexing and analysis. Elasticsearch is more popular because it is easy to install, scales out to hundreds of nodes with no additional software, and is easy to work with due to its built-in REST API.
Advantages of Implementing Elasticsearch
1. Developer-Friendly API
Elasticsearch is API-driven. Almost any action can be performed using a simple RESTful API using JSON over HTTP. Client libraries are available for many programming languages. It has clean and easily navigatable documentation, increasing the quality and user experience of independently created applications on your platform. It can be integrated with Hadoop for fast query results. Klout, a website that measures social media influence, uses this technique and has a scale from 100 million to 400 million users while reducing the database update time from one day down to four hours and delivering query results to business analysts in seconds rather than minutes.
2. Real-Time Analytics
Real-time analytics provide updated results of customer events such as page views, website navigation, shopping cart use, or any other kind of online or digital activity. This data is extremely important for businesses conducting dynamic analysis and reporting in order to quickly respond to trends in user behavior. Using Elasticsearch data is immediately available for search and analytics. Elasticsearch combines the speed of search instances with the power of analytics for better decision-making. It gives insights that make your business streamlined and improves your products through interactive search and other analyzing features.
3. Ease of Data Indexing
Data indexing is a way of sorting a number of records on multiple fields. Elasticsearch is schema-free and document-oriented. It stores complex real-world entities in Elasticsearch as structured JSON documents. Simply index a JSON document and it will automatically detect the data structure and types, create an index, and make your data searchable. You also have full control to customize how your data is indexed. It simplifies the analytics process by improving the speed of data retrieval process on a database table.
4. Full-Text Search
With full-text search, a search engine examines all of the words in every stored document as it tries to match search criteria. Elasticsearch builds distributed capabilities on top of Apache Lucene to provide the most powerful full-text search capabilities available in any open-source product. Its powerful, developer-friendly query API supports multilingual search, geolocation, contextual did-you-mean suggestions, autocomplete, and result-snippets.
5. Resilient Clusters
Elasticsearch clusters are resilient: they will detect new or failed nodes. It will also reorganize and rebalance data automatically to ensure that your data is safe and accessible. A cluster may contain multiple indices that can be queried independently or as a group. Index aliases allow filtered views of an index and may be updated transparently to your application.
Some of the core benefits highlighting how Elasticsearch can be useful for business include:
- Managing huge amounts of data in a quick and seamless manner compared to traditional SQL database management systems.
- Quick access to documents as they are stored in close proximity to corresponding metadata in the index, thereby reducing the number of data reads and faster search result response.
- Scalability: Enables to scale up to thousands of servers and accommodates petabytes of data.
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