Search engines exist to help you find information as efficiently as possible and to let you research all kinds of topics. But not all search engines are created equal–and not all of them are suitable for the same types of searches.
So, when it comes to data science browsing, it’s very important to note that Elasticsearch Engine is precisely the kind of search engine you’re after. We’ll cover the basics relating to Elasticsearch, and then we’ll give you the top reasons why it’s the best choice for anyone interested in data science browsing.
What is Elasticsearch Engine?
Originally an open-source search engine (and analytics engine) created using Apache Lucene, Elasticsearch Engine is a data solution that lets you work with giant sets of data.
You can use it to do more than search, too. With Elasticsearch, it’s easy to both store and analyze data, making it an excellent solution for anyone planning to work with bigger quantities of data.
How does it work?
Elasticsearch can handle the large quantities of data it works with because it keeps them meticulously organized. That organization relies on grouping all documents by their characteristics. These groups are called indices, which Elasticsearch Engine then uses to conduct its searches.
So, when the engine receives a query, it finds the appropriate index (or indices) with the right information. On the user’s end, the concept is very simple. You enter a search term–say, ‘hosted phone system’–and the engine returns all the data it has on the subject. In the specific case of our example, it would show you any relevant data that connects with hosted phone systems.
What can you find?
As long as you’re looking for something based on data, you can use Elasticsearch.
Whether you’re searching for information on video annotation, data analytics for businesses in your sector, or a specific piece of data on a data-rich website, Elastic’s search engine can locate it for you.
Elasticsearch is also well-suited for security analytics. That means that anyone looking to improve their company’s security protocols can get a lot of valuable research done with this search engine.
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Why is Elasticsearch perfect for data science browsing?
Next, we’ll walk you through some of the main reasons why Elasticsearch is the best engine for data scientists to use when they’re browsing. The following list is not exhaustive because there are loads of reasons why Elasticsearch is excellent.
1. It’s very, very quick
Anyone working in data science knows that it can be a very fast-paced environment. When you’re able to gather large quantities of data in little time, it’s easy to get overwhelmed by just how many things you can do with all that information.
Thankfully, Elasticsearch is more than quick enough to keep up with that demand.
Its index-based search system lets it generate reliable results in milliseconds. That means you’ll never have to waste time waiting for data to load or worrying about whether it’s accurate or relevant to your search terms.
So, if you’re looking for information on call center technology, you can have the data you need at your fingertips in the literal blink of an eye.
2. The breadth of data available
Elasticsearch works with giant data sets. It won’t be slowed down by loading big chunks of data or by storing it.
As mentioned above, data science is a fast-growing, ever-evolving field. Because of the easily accessible database and the high quality of available data, Elasticsearch is the perfect tool for data scientists. Plus, with more data at the ready, data scientists can browse for as long as they’d like without having to risk running out of valuable data to peruse.
3. Analytics
Data science relies on more than just accessing stores of information. It’s also focused on processing, synthesizing, and analyzing the information you gather–which is precisely why Elasticsearch’s analytics capabilities are so useful to data scientists.
If you’re brand-new to the world of data science or if you’re interested in getting involved, it can be incredibly helpful to be able to see existing analytics. You can learn from closely examining them. You can also see the kinds of analytics that tend to be useful to draw up.
Analytics are also useful for experienced data scientists. In addition to showing them new ways to process data, analytics can give them valuable insights into what types of data analysis are popular at the moment.
4. Logging
Often, data science involves logging all your data in one application, then using others to process that information. This can be helpful if both applications are perfectly suited to their respective purposes, but it is time-consuming. Elasticsearch lets you do both in one solution.
It’s perfect for both logging and analytics, which means you can keep all your work in one place. That’s especially convenient if your existing solutions aren’t always perfectly compatible or if you want to be able to work from home without having to install a bunch of software. You can also take advantage of Elasticsearch’s native searching features to easily sort your data and scan for what you need.
5. Website search functionality
We’ve alluded to this before, but Elasticsearch lets you browse all the data belonging to a data-rich website. This makes it much easier to find what you need when you’re only sure of which website the data will be on.
For example, let’s say you’re looking to browse a digital archive that’s full of all kinds of how-to guides and explanations. The more guides there are, the longer it would take to manually sift through them in search of the exact one you need.
When you use Elasticsearch Engine, you can instead filter through all that data in a fraction of a second. If you’re looking for an email to fax guide, you’d have that ready immediately with this search engine.
6. It fosters better security measures
With Elasticsearch, you can easily analyze access logs and other security analytics. This helps you ensure your data stays completely safe.
Also, Elasticsearch’s capacity for drawing up analytics on security data lets you evaluate and assess the security protocols you’ve got in place. For example, it could tell you whether your firewalls are effectively keeping malicious outsiders out–and to what extent you’re being targeted by those outsiders.
This allows data scientists to draw up fact-backed recommendations for security improvements.
7. Native APIs
Built specifically to be used with the solution, Elasticsearch REST API was made with searching and querying Elasticsearch in mind.
The fact that it’s a native API is highly valuable to data science experts. That’s because the two will be perfectly compatible at every turn, eliminating the risk of friction between the interfaces. This also means you don’t have to waste time searching for the source of any incompatibilities.
Also, a native API will often be updated in tandem with any updates to the solution itself. For example, when Elasticsearch went from open to closed source, the REST API was able to make the shift with it. This saves further time and effort on the data scientists’ end.
8. User friendliness
As far as data search engines go, Elasticsearch is very user-friendly. With the right tips, it’s fully accessible for both beginners and experienced data scientists.
This removes a barrier to entry for any data scientists with less experience or know-how. That, in turn, makes the field of data science more accessible to a wider talent pool, which leads to the potential for better and more sustainable growth within the field.
Plus, it saves data scientists a lot of effort they’d otherwise have to spend trying to learn the ropes of a complicated piece of software.
9. Data can be edited and deleted easily
Storing data is just one part of a data scientist’s daily responsibilities. It’s also important that they’re able to amend or remove data–or entire sets of data–just as quickly.
Thankfully, Elasticsearch is built for this.
Removing data works via a simple process that only takes a matter of minutes. This means that it’s well worth browsing through your own old data, so you can check whether it all still needs to be up and whether any of it needs to be changed or updated.
Conclusion
Elasticsearch Engine is built with data scientists and their needs in mind. That’s the main reason why it suits them so well and why it’s so great for anyone browsing data science.
From the fact that it can handle both storage and analytics to its inherent lightning speed, Elasticsearch is the right tool if you’re working with data. It’s not just built for little bite sizes of data either–it can handle huge quantities at a time without slowing down in the process.
This makes Elasticsearch the perfect engine for both new and experienced data scientists.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.
