Welcome back, data folk, to our 3-part series on managing and analyzing data with SQL, Python and pandas. In our first post, we went into the differences, similarities, and relative advantages of using SQL vs. pandas for data analysis.One of…
Continue reading How to Read a SQL Query Into a Pandas Dataframe (2024 Updated)
Poorly designed databases can cause many problems, including a waste of resources, difficult maintenance, and faulty performance. That’s why having a great database schema design is a crucial part of effective data management. “Great database schema design” is certainly easier said…
Continue reading Database Schema Design Examples, Principles & Best Practices
This article will cover how to work with time series/datetime data in Redshift.Time series data holds the date and/or time information and is different from string or numeric data since it has components like day, month, year, hours, etc., that we…
Continue reading Manipulating Time Series/Datetime Data with SQL in Redshift
Power BI increases its adoption among the most experienced users each day. Thanks to being a user-friendly tool with various data sources to connect to, it helps analyze distinct data sources within the same console.In this post, I’ll show you…
HubSpot is a leading platform for inbound marketing, sales, and customer relationship management. With HubSpot, you can get exhaustive data on your marketing efforts. But that same data can be exhausting, whether due to the sheer volume of information you have…
Continue reading How to Analyze HubSpot Email Campaigns with SQL
If you use HubSpot, you know it captures a ton of data about your customers, marketing campaigns, and so much more. And while native HubSpot reporting is a great way to get started, you’re likely to find yourself thirsty for…
Continue reading HubSpot & SQL: A New Way to Understand Your Revenue
MongoDB’s data flexibility is a breeze for developers but can be a major headache for analysts and data scientists. Here’s how to wrangle your non-relational MongoDB data into a Tableau-friendly shape using JSON exports, the MongoDB Connector for Business Intelligence,…
For the final entry in our SQL and pandas series, we’re going to be talking today about closing the loop. We’ve talked about the difference between pandas and SQL, how to fit each of them into your workflow, and how…
Continue reading How to Load Pandas Dataframes into SQL Database as a Table
SQL and pandas are both invaluable tools for any analyst or data scientist. They tend to be mentioned in the same breath when talking about data manipulation and analysis—and can do a lot of the same things—but they have pretty…
Continue reading SQL and pandas: A Rosetta Stone for Analysts
This tutorial was authored by Ahmed Shaaban, a MeasureMatch partner.In this tutorial we are going to walk through:This is a big debate in any business environment: why do I need to replicate my MySQL data?Continue reading MySQL Replication for Analytics…
Continue reading MySQL Replication for Analytics and Visualization: A Panoply Tutorial
At Panoply, we get a lot of customers who are looking to use Grafana to analyze data stored in DynamoDB instances. If you’ve ever looked into this, you’ve probably seen that Grafana doesn’t have any native support for DynamoDB, and…
Continue reading How to Analyze and Connect DynamoDB Data in Grafana
DATEDIFF is a super useful function that returns the difference between date parts (or dateparts) of two date or time expressions. For this post we will use data from: Hubspot Lead Analytics in SQL, Beyond CRM Reporting and will use…
Continue reading Creating Elapsed Time Histograms in SQL Using DATEDIFF
