Holistics Blog

There was a time when being a power Excel user gave you an edge in your resume – it implied that you can perform your tasks more efficiently than your peers. Excel remains one of the most commonly used tool for analyzing data today and can be applied in various scenarios such as financial modeling, data analytics, or even invoicing. However, it is running into a wall of limitations as the nature and use of data become more complex

Excel remains a powerful tool for data analysis today. But...

As engineers we refactor and abstract our code regularly, this is part of process to improve our code quality and scale up our codebase. In my previous job, we refactor our code religiously - “Leave the codebase a better place than you found it” so we said, that was the main reason to help us build big, complex system with just a handful of engineers!

When we started Holistics, we naturally asked - how can we enable the same refactoring approach for data/SQL analysts?

Writing Reusable...

Holistics featured at Grab Engineering’s presentation at AWS Summit Singapore 2017, alongside with Tableau

Holistics gives you more flexibility and control over Tableau for data extraction and preparation.

1. Ask Better Questions

Tableau is good for reporting organized data (where you already have the data in your columns), but it falls short for business and operational reporting where you need more specific reports across different tables.

While not impossible, it is challenging to write...

Being an analytics/reporting product company, we’ve worked with a fair amount of customers who run Postgres/Redshift for their analytics, and the same fair amount who uses MySQL.

And from our experience with both, I’d prefer our new customers to use Postgres all the time. For reporting/analytics/data-warehouse purpose, Postgres wins over MySQL hands-down.

In this post I lay down a few practical reasons why working with Postgres is so much better than MySQL from a data analyst perspective...

We’ve worked with a lot of customers who writes SQL on a regular basis. And from time to time they come to us with SQL question - that we thought would be interesting to share with others!

Today, one of our customers came to us with a question - They were having problems unnesting a string/array into rows in Amazon Redshift.

Question - Splitting comma-delimited string into rows in Redshift

Suppose we have a books table in Amazon Redshift that has these data:

tags (varchar)
-----
A
A, B
C...