Think of them as preformed queries macros and modules

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Think of them as preformed queries. Macros and Modules Macros and modules let you add functionality to your database. For example, if you want to do a monthly cleanup of all records older than a certain date, you can automate that by creating a module. For anything that you would have to run manually, you can probably create a macro to make the process faster. Non-relational Databases Relational databases are great for holding structured data, but not all data fits into a nice, clean structure with rows and columns. Two other types of data that people need are nonstructured and semi- structured. These types of data can be housed in non-relational databases. Before diving into the types of non-relational databases, it’s important to understand the key features of non-structured and semi-structured data. Non-structured Data Most data in the world—80 percent by some professional estimates —is non-structured data. Examples of non-structured data include pictures, videos, web pages, emails, documents, texts, and social media. Realize that this data has structure within itself. For example, emails have a sender and receiver, a subject line, and a message body. The data is considered unstructured because it doesn’t fit neatly into a database. Here’s a good analogy to help think about dealing with unstructured data: Imagine you have a file folder on your computer. In that folder, you’ve placed photos from a vacation, emails, work history, 515
school transcripts, text messages, all of your social media content, and X-ray images from a medical procedure you underwent a few years ago. That folder is now your “database,” and the content in it is your data. You can see that the data has no structure, but there may be interesting links between different pieces of it. It will be quite challenging to find those links, though, considering all of the different data types included. Non-structured data is often referred to as unstructured data. Semi-structured Data As its name implies, semi-structured data fits somewhere in between structured and nonstructured data. Specifically, semi- structured data is generally thought of as unstructured data that has been tagged with metadata. Metadata is literally data about data. Take the vacation photos and X-ray images from the unstructured data folder, for example. Both of them are images, which are just collections of pixels—how would they be included in a search? In a semi-structured world, tags of metadata could be associated with the pictures to provide some context (maybe the date they were taken, the location, or even some more specific information such as it included a hand). Granted, the hand picture could be either from a vacation or an X-ray, but at least you’ve got something to go on. Emails can be tagged too by listing the sender and the recipient, time, and date. Going back to the messy unstructured data folder example, you can probably think of ways to include metadata for each type of data mentioned.

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