What Is Elasticsearch? (And Should It Be Used For Embedded Dashboards and Reports)

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Elasticsearch is a free and open-source search and analytics engine that allows users to search for and analyze almost any type of data quickly and efficiently in any dashboard.

This article will answer the question: what is Elasticsearch, and should it be used for embedded dashboards and reports?

What Is Elasticsearch?

Elasticsearch is a free and open-source, distributed search and analytics engine for all types of data. It’s the main component of the Elastic Stack, a set of free and open-source tools used in data ingestion, storage, analysis, and visualization. It is seen as an effective visualization tool.

The Apache Lucene library developed using Java allows users to store, search, and analyze vast amounts of data quickly. It can do this because it searches an index instead of searching the text directly and forgoes a structure based on tables and schemas for one based on documents.

It does this by storing data as JSON documents and JSON files in JSON format. Each of these documents correlates with a set of keys with their corresponding values. These documents are then stored in an inverted index during the indexing process, which makes them searchable in almost real-time.

Ultimately, users can use Elasticsearch to search all kinds of documents in a scalable way with almost real-time results.

Why Pick Elasticsearch Over Other Technologies?

So, considering its distributed, scalable design and the fact that people can use it for all types of data like textual, numerical, structured, and unstructured data, why should businesses consider using it?

Well, for one, Elasticsearch is:

Fast, which makes it ideal for time-sensitive applications.

Distributed, which makes it extremely scalable.

Feature-rich, which makes storing and searching data more efficient.

Able to simplify data ingestion, visualization, and reporting.

Considering the above, it’s suitable for several use cases:

Application search. It’s often used in applications that rely heavily on a search platform for access, retrieval, and reporting of data in the application.

Website search. Websites that contain a lot of content and data need accurate and effective searches. Here, Elasticsearch is very useful and is gaining popularity in the site search domain.

Enterprise search. By using Elasticsearch, companies can perform enterprise-wide searches, including people searches, blog searches, document searches, and e-commerce product searches. Anyone can use it across the enterprise to perform almost any type of search.

Logging. Because it can ingest and analyze log data in almost real-time while also being scalable, it’s commonly used for log analytics. These analytics provide businesses with important operational insights that allow them to make better decisions.

Infrastructure metrics. Many companies use the Elastic Stack to gather data across several performance parameters and analyze various metrics.

Security analytics. Users can analyze access and security logs with the Elastic Stack, which provides a complete view of what’s going on in a company’s systems in real-time.

Application performance monitoring. Application performance monitoring allows companies to monitor software services and applications in real-time and gets detailed performance information. In this way, they can identify and fix performance issues quickly.

Should It Be Used for Business Intelligence, Reports, and Dashboards?

Now that we’ve seen some popular use cases for Elasticsearch, the question is whether users can use it for business intelligence, reports, and dashboards. Although Elasticsearch can be used as a business analytics tool, it doesn’t mean it’s ideal.

For one, it has a steep learning curve for implementing it, especially where companies have multiple data sources. This makes it cumbersome to implement and use for business intelligence, reporting, and dashboards.

The other, bigger problem relates to how it handles data. At its core, Elasticsearch is a JSON document store. In other words, to search the data, it processes JSON requests and returns JSON data. To do this, it indexes documents. This doesn’t necessarily make it such an effective data store.

In contrast, a typical SQL database can store data and also index it. Keep in mind, though, that Elasticsearch is not meant to be a primary datastore. As such, Elasticsearch is not as capable of discrete record retrieval as an SQL database is. It’s also unable to join document types like a typical SQL database can with tables.

In simple terms, for different document types, Elasticsearch would need to run the queries one after the other. In addition, it provides an ODBC connection and many other ways to connect to query the data. This is not ideal for complex queries.

Ultimately, it’s necessary to flatten the Elasticsearch data into a relational database for business intelligence, reporting, and dashboarding purposes. So, while people can certainly use Elasticsearch for this purpose, it’s not ideal.

How Can Yurbi Help With Elasticsearch Based Dashboards and Reports?

If you are trying to generate reports/dashboards to be included in your SaaS/Business app, you should flatten out the Elasticsearch data into a relational database for querying.

However, Yurbi can query Elasticsearch live via the ODBC driver they provide, and in the upcoming release of Yurbi, that supports API endpoint querying to consume JSON data results. We will be able to directly connect to the Elasticsearch API to fetch and store data, however, we still do not recommend using that for embedded analytics except in very specific use cases.

We are just a phone call away if you think Yurbi is the software to help you with your Elasticsearch based dashboards and reports. Feel free to schedule a live demo with us or contact us to discuss the best approaches and solutions for your concerns. We are more than happy to help you.

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