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In businesses all around the world, primary data stores are the main source of truth and hold the most up-to-date and accurate data that allow senior leaders to make informed decisions. If you’re working in a development or data team, you’ll frequently encounter stakeholders requesting targeted data insights to support strategic decision making. Or on the flip side, maybe you’re the person reaching out to your technical team to get the lowdown on the latest trends.
Let’s say your organization is using a MongoDB database to store data. Left to your own devices, how would you go about preparing and extracting your data for analysis? The answer is, you’d need to write an aggregation pipeline. The pipeline is similar to an assembly line in a factory, where each station on the line performs a specific task on the raw materials to transform them into the final product.
So what exactly are MongoDB aggregation pipelines?
A MongoDB aggregation pipeline is a series of data processing stages. It’s essentially a list of stages that sequentially process the data which is stored in documents in the database. Documents are like rows in relational databases and are grouped into collections, similar to tables.
The capabilities of an aggregation pipeline go beyond the simple query. The different types of stages enable you to:
- Filter documents to match specific criteria so that you select only the relevant data.
- Group and summarize data (for example, totals or averages).
- Sort results into ascending or descending order.
- Combine or join data from other collections in the database.
- Modify data (for example, adding new fields).
- Define the fields to include in the results.
Each stage builds upon the outputs of the previous one allowing you to carry out complex analysis on your data.
Aggregation pipelines are a key part of the MongoDB Aggregation Framework, which helps your business process large amounts of raw data to achieve greater efficiency and faster decision making.
Until recently, you’d need to know the ins and outs of the stage syntax to get started with creating an aggregation pipeline and, if you’re interested, there are many good examples of MongoDB aggregation syntax online.
Natural language querying and aggregations to the rescue!
Technology has moved on with the rapid introduction of AI into your daily workflows. Natural language querying redefines the accessibility of data, moving away from writing code syntax towards a conversation where you ask different questions and experiment with data exploration.
Say you’re a marketing manager and you’re looking for insights about product performance so that you can tailor your product offerings to target different customer segments. You might ask the following question:
“Analyze customer transactions by product type, group the remaining documents by customer segment, and calculate the total transaction value and average transaction frequency.”
Let’s break down the elements:
- “Analyze customer transactions by product type”
This puts the focus on specific product categories and helps understanding of product performance across the different types of products.
- “Group the remaining documents by customer segment”
This categorizes customers into segments (for example, age group or income level) and enables analysis of how different customer groups interact with the products.
- “Calculate the total transaction value and average transaction frequency.”
This provides insights into the revenue contribution of each customer segment for the selected product type.
Ready to try out your own AI prompts?
Taking advantage of natural language querying and AI technologies is key to deriving your own insights from your business data to assist you with effective strategy planning and decision making.
AI-powered tools let you interact with the data, allowing you to ask questions and quickly generate aggregation pipelines. Their flexibility provides the agility to easily adjust the pipelines to answer different different business scenarios, giving you the competitive edge.
