How do you handle big data? If you have endless time and your data doesn’t change frequently, you could try to organize it to the hilt. Alternately, you could relax and let enterprise search offer immediate access. Enterprise search is the force that cannot be stopped if Big Data is the unmovable item.
Enterprise search allows you to conduct a thorough analysis of your own Big Data, as opposed to a search engine that simply scans the web, like Google. Enterprise search must index the data first before being able to quickly search terabytes. The index is merely an internal reference that pre-tabulates distinctive words and numbers in the data and their precise locations, including those that are spread across many data repositories and locations.
A technical introduction to indexing. Start by examining what the indexer *doesn’t* do. It *does not* relocate, copy, remove, or otherwise change the original files. The same way you would evaluate a Microsoft Word document in Word or a PDF in Adobe Acrobat Reader, it *doesn’t* pull up files in their related apps. Such a course of action would take far too long.
So what does the indexer actually do? All files’ binary formats are immediately accessed by the indexer. If you looked at a binary format, you would see a jumble of binary codes, making it challenging to decipher individual words, let alone full phrases. But because search engines have document filters built in, they can handle binary formats.
Each binary format must first be correctly parsed by the document filters before being indexed. The parsing requirements for several file types, and occasionally even variants of the same file type, can run into the hundreds of pages. Parsing the text of a binary format will swiftly come to a halt in the absence of the proper parsing definition.
The indexer, unleashed. Unleashed was the indexer. You might anticipate that indexing would be time-consuming given the focus on precision parsing. You only need to point to the folders, email repositories, etc. to cover; the indexer will take care of the rest. Document filters have a tough job ahead of them in the data recognition department. The parsing specification to use for each binary format can be determined by the indexer on its own. (A search engine should examine the binary format rather than the file extension to make this judgement. It’s far too simple to save a PDF with a.DOCX extension or an Access database with a.ONE extension.)
The indexer can examine the data considerably more thoroughly than a human could by simply looking at files in the related apps. For instance:
- When it comes to indexing a binary format, “invisible” text such as black writing on a black background or white writing on a white background inside a related application view is just plain text.
- Binary-formatted metadata is readily available and may be found without having to do a lot of navigating around in an associated program.
- The search engine can easily navigate through multi-layered file formats, such as an email attachment that is a ZIP or RAR file containing a PowerPoint presentation and an Excel spreadsheet hidden inside the PowerPoint.
- Numerous languages can be present in a single file thanks to Unicode’s automatic support for hundreds of different languages around the world.
Unstoppable power. Indexing completed, start your search. Here are just a few factors that make indexed search a powerful force:
- There can be any number of active indexed search threads simultaneously. Scalability is unrestricted for online search due to the index structure’s flexibility to allow each search thread to operate in a stateless fashion.
- Over twenty full-text and metadata search options are made available by the index structure. Free-form natural language, precise word and phrase Boolean (and/or/not), and proximity search requests are all included.
- Typographical errors that could be present in files like emails or OCR’ed text can be sorted through using options like fuzzy searching.
Beyond words, the search engine can also locate number and numeric patterns, including numeric ranges, date and date ranges across several date formats, and number and numeric patterns. Additional items that might have unintentionally entered the Big Data repository, such as credit card numbers, can be flagged by the search engine.
Last but not least, automatic index updates can take care of reindexing the new things, eliminating the deleted ones, etc. whilst concurrent searching continues uninterrupted.
