For decades, if you wanted to combine datasets, the only way to do it was with data joins. But data joins have always been a complicated language to understand and are usually best left to developers to build and execute. They were meant as a way for IT departments to prepare and store data, not for end users to analyze it. Enter Data Links, a new tool for your analytic toolbox that’s only available on the Qrvey platform.

Traditional data joins are great tools for preparing data, particularly when working with structured, transactional data that needs to be normalized. However, data joins come with many limitations. Joins are both rigid and inflexible, and best used with predictable, structured datasets. If you don’t know exactly how to structure your data join, bad things can happen fairly quickly.

What is a Data Join

A data join is a process that combines data from two or more tables based on a common field or condition. There are different types of data joins, such as inner join, left join, right join, full join, self join, and cross join. Each type of join can be useful for analytics data preparation, depending on the desired result and the structure of the data.

What are the types of data joins

Inner join:

This join returns only the rows that match the condition in both tables. It is useful for filtering out irrelevant or missing data and finding the intersection of two data sets.

Left join:

This join returns all the rows from the left table and the matching rows from the right table. If there is no match, the right table columns are filled with null values. It is useful for preserving the data from the left table and adding additional information from the right table if available.

Right join:

This join is similar to the left join, but it returns all the rows from the right table and the matching rows from the left table. It is useful for preserving the data from the right table and adding additional information from the left table if available.

Full join:

This join returns all the rows from both tables, regardless of whether they match or not. If there is no match, the missing columns are filled with null values. It is useful for finding the union of two data sets and identifying the differences between them.

Self join:

This join is a special case of an inner join, where the same table is joined to itself using an alias. It is useful for finding relationships within the same table, such as hierarchical or recursive data.

Cross join:

This join returns the Cartesian product of two tables, which means every row from the first table is paired with every row from the second table. It is useful for generating combinations of data or testing scenarios.

Get a demo of Qrvey

When it comes to analytics, and in particular embedded analytics, you never know exactly which combinations of data sources, fields and information your users will need. And these combinations are constantly changing, both from user to user and from day to day. This new use case requires more than just a data join. It requires Data Links.

Data Links allow everyday users, not just programmers, to combine data from any data set, whether its structured, semi- or unstructured, quickly and easily using a simple, no-code interface. These datasets don’t even need to reside in the same location. Links can be made between local, cloud and even third-party systems as needed.

Data links can be added, updated, and removed with just a click, making them perfect for data discovery, exploration and analysis.

And when it comes to performance, Qrvey’s Data Links really excel. They’re built using the power and flexibility of the modern cloud and Elasticsearch, allowing them to instantly scale to meet your company’s demanding requirements.

By combining a multi-tenant data lake capable of joining data, linking data and creating custom datasets, Qrvey’s embedded analytics software lays the foundation for SaaS applications to significantly improve their reporting features.

Get a demo of Qrvey

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