If you’re under 1 TB of data, Postgres will give you a good price to performance ratio. These aren’t strict limitations and each can handle more or less data depending on various factors - but we’ve found each to excel within these bounds. Here’s a handy chart to help you figure out which option is right for you. If you're dealing with large volumes of data, then it's more helpful to have a non-relational database because it won’t impose restraints on incoming data, allowing you to write faster and with scalability in mind. The next question to ask yourself is how much data you’re dealing with. If you’re doing a large amount of text mining, language processing, or image processing, you will likely need to use non-relational data stores. Classic examples of semi-structured data are texts like email, books, and social media, audio/visual data, and geographical data. Non-relational databases excel with extremely large amounts of data points (think millions) of semi-structured data. On the other hand, if your data fits better on a sheet of paper, you should look into a non-relational (NoSQL) database like Hadoop or Mongo. User traits like names, emails, and billing plans fit nicely into a table as do user events and their properties. For most types of user analysis, relational databases work well. These structured, relational databases are great when you know exactly what kind of data you’re going to receive and how it links together - basically how rows and columns relate. If you answered Excel, a relational database like Postgres, MySQL, Amazon Redshift or BigQuery will fit your needs. Does it fit nicely into rows and columns, like a ginormous Excel spreadsheet? Or would it make more sense if you dumped it into a Word Doc? Think about the data you want to analyze. They are typically part of a broader data warehouse. Analytics databases are read-only systems that specialize in quickly returning queries and are more easily scalable. Working with lots of customers to get their DB up and running, we’ve found that the most important criteria to consider are:Īn analytics database, also called an analytical database, is a data management platform that stores and organizes data for the purpose of business intelligence and analytics. Whichever set up you choose, you can make tradeoffs along the way to improve the performance from what we discuss here. In this post, we’ll go over suggestions and best practices for the average company that’s just getting started. You need a separate kind of database for analysis. You might also accidentally delete important info if you have data analysts or engineers poking around in there. It’s not a good idea to write scripts to query your production database, because you could reorder the data and likely slow down your app. When your analytics questions run into the edges of out-of-the-box tools, it’s probably time for you to choose a database for analytics.
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