Scalable and efficient data pipelines are as important for the success of analytics, data science, and machine learning as reliable supply lines are for winning a war. — For deploying big-data analytics, data science, and machine learning (ML) applications in the real world, analytics-tuning and model-training is only around 25% of the work. Approximately 50% of the effort goes into making data ready for analytics and ML. The remaining 25% effort goes into making insights and model inferences…

Difference between SQL and NoSQL databases. Deep dive, decision tree, and cheatsheet to choose the best for your data type and use case from 12 database types. — How do you choose a database? Maybe, you assess whether the use case needs a Relational database. Depending on the answer, you pick your favorite SQL or NoSQL datastore, and make it work. It is a prudent tactic: a known devil is better than an unknown angel.

What if programming languages were stocks? And you had to make a portfolio to fetch good returns in 2022? — You probably have seen various surveys and analyses listing the most popular programming languages. Those cover the universe of programming languages, like the S&P 500 does for the stock market. What would be the best portfolio for you to be successful and outperform the rest? Of course, it depends on…

Systematic way of collecting data and extracting actionable insights using the Drivetrain Approach. — “Let’s collect all data we can, and we will fish for insights later.” Have you heard this before? That approach seldom works. On rare occasions when it does work a little, the RoI is very low w.r.t. the cost of collecting, processing, and storing volumes of data. …

How to profile performance and balance it with ease of use — Applying a function to all rows in a Pandas DataFrame is one of the most common operations during data wrangling. Pandas DataFrame apply function is the most obvious choice for doing it. It takes a function as an argument and applies it along an axis of the DataFrame. …