user: What are good gifts for a new mom?

assistant: For Baby: 
Baby Bum Brush (don't laugh, this saves your life as a new mom!)
Anything Kyte Baby, I was obsessed with this stuff, its all organic bamboo and SOOOO soft
Muslim Burp Cloths - you go through these like WATER for the first few weeks
The Story of You - someone got one for my friend and she raves about it! Its a DIY photo journal to document the first year(s)
: 

For Mom:
Babysitting coupons: just offering to come over and let her sleep for a few hours for the first few weeks will literally change her life. Truly. This is my #1.
Newspaper framed from the day her baby is born: Ricky did this for me, and it meant so much! Just so cool to have.
A cleaning service for a month (nobody got this for me, but I ended up getting it for myself, I couldn't keep up!)
Uber Eats/Doordash/Instacart Food Delivery
Shower Steamers/Spa Day
If she has a C-Section, you might want to talk to her about if she needs anything to help make her comfortable? I had one unexpectedly and I didn't realize you needed special underwear, ice packs, etc. I was totally unprepared, haha.

user: How is data engineering different from data science?

assistant: Data Engineering occurs before data science in the data lifecycle. Data can be generated from various sources and this raw data may be structured, semi structured or even unstructured. To make sense of this data, data has to be ingested and transformed. This is when data engineering comes into picture. Data engineering includes the process of extracting the raw data, ingesting data into a system like a data lake, transforming the data for business needs and finally loading it into a table. This is commonly known as ELT (Extract-Load-Transform). ETL (Extract-Transform-Load) is also possible where transformation happens before the loading stage.
Data Science and Analytics is the next step in the data lifecycle. Once the data needed for specific business need is available, data scientists use this data to run machine learning models to find the most accurate model. This data is available to the data scientists in the form of tables. Data analysts also use this data to do some exploratory analysis and create dashboards. 
In essence, the data lifecycle would look as follows:
Raw data from different sources -> Data Engineering -> Data Science and Analytics

user: Why has pickleball become so popular?

assistant: Pickleball has indeed become very popular.  Promoters of the game have been calling it the "fastest growing sport" for some time now.

There are a few reasons many people cite for why the sport is so popular:

- It is a quick game to learn.  The rules are pretty simple, and more importantly, the skill needed to hit functional shots in the game is not very difficult to learn.  Compare it to tennis, where it can take multiple lessons from a professional teacher just to get to a point where you can hit a few good shots.  With pickleball, almost anyone of any age or fitness level can pick up a paddle and be playing a game in just minutes.

- Pickleball culture has tended to be very inclusive.  Many pickleball groups make it a point to be very inclusive of all ages, races and backgrounds.  This often creates a sense of community for players, and makes them more likely to keep playing and invite friends along, thus growing the sport.

- It is a fun way to get in a decent workout.  While not as taxing as, say, tennis, pickleball can be a good workout.  Especially since unlike tennis there is very little downtime.  The pace of games is very quick and involves near constant movement.  The workout is not cardio-intensive, but still it is an easy way to have fun while staying active.