As an analyst or data scientist, it’s becoming increasingly advantageous to have a deep understanding of multiple analytical programming languages. There's a growing number of people using SQL and Python in a hybrid-fashion for data analysis. But, the dialogue around the usage of these two languages tends to portray them as complementary, but functionally discrete.

We here at Mode Analytics have been Amazon Redshift users for about 4 years. Redshift enables and optimizes complex analytical SQL queries, all while being linearly scalable and fully-managed within our existing AWS ecosystem. In our early searches for a data warehouse, these factors made choosing Redshift a no-brainer. Redshift has mostly satisfied the majority of our analytical needs for the past few years, but recently, we began to notice a looming issue. Certain data sources being stored in our Redshift cluster were growing at an unsustainable rate, and we were consistently running out of storage resources.

Just in time to make good on New Year's resolutions, today we're releasing a major upgrade to Mode's Python Notebooks: support for over 20 new Python libraries. We're also updating our existing libraries, from NumPy to pandas to matplotlib (and more). All together, this adds up to 35 new and improved Python libraries. Enough to make a wintry day brighter.

In this day and age, every company needs an analytical decision-making culture. Every company needs to be data-driven. And not someday. Every company needs to make analytical decisions every day, starting from day one.

Customer success teams are a central pillar of recurring-revenue businesses. Great customer success teams help users get the highest possible value out of a product or service, leading to more valuable accounts and happy, long-term customers. Tracking a customer success team's ability to deliver on these goals can help identify opportunities for improvement.