Doug Cutting, the creator of Apache Hadoop and Lucene, said one day: “You know, people today think that search and big data are separate but in two or three years, everyone will wonder why we ever thought that.”
Hope, his prediction came true and today every company having a big data project has a search opportunity packed.
3 reasons why your Big Data needs a search engine
- You or your customers need to find the relevant info.
No matter whether you use big data sets (or even not big) for internal company’s purposes or run a product/service for customers, it’s always necessary to extract the relevant information. Search engines are able to provide results from any sort of indexed data in a fraction of a second.
- You or your customers need analytics.
Search engines perform searches for business reports, product/service dashboards, different kinds of analysis and even visualize all this data by charts, graphs or diagrams (if powered with this feature, of course). This is called business intelligence
- You want to know what’s inside your data lake.
Companies always want more data: they collect it to a data lake and then don’t know how to manage all of it. Let search engine index it and enjoy the results: now you are able to find any useful info in a moment.
4 advantages of big data search engines
- Speed: most of the existing solutions are designed to achieve high speeds for indexing and searching within a scalable, high concurrency architecture. New data can be indexed and searched in real time, and on multiple servers, delivering value to your customers.
- Volume: search engines for big data sets are engineered to process the really huge volume of information in a fraction of a second.
- Variety: big data search engine can process almost any type of data – web content, email, social networks, databases, any user-generated content etc. and perform searches within milliseconds in this variety of structured and unstructured information.
- Flexibility: if you have a custom solution for your big data search, then most probably it has flexible architecture. This high-customization feature allows a company to accommodate its current requirements without stuck at just one way of searching.
Existing solutions on the market
Best enterprise search engine solutions:
- Amazon CloudSearch
- Apache Solr
- Google Search Appliance
- Microsoft Azure Search
- Sphinx Search server
Top open source solutions:
- Apache Solr
- Apache Lucene Core
It’s obvious: if you have a big data project, you need a search engine!
Another clear thing: no matter which solution you choose for your business needs (enterprise or open source), you still need assistance in implementing and adapting it to your existing ecosystem. Meaning, most probably you’ll have to hire a dedicated professional for this project or buy an extended support from a solution vendor.
Here comes the alternative way out of this situation: develop a custom big data search engine suitable for company’s current needs and goals, flexible and customizable.
Integrating search into your big data projects can help avoid many issues. So, consider applying a search engine on a planning stage of your project.