PySpark SQL Recipes

Chapter 1:  Introduction to PySparkSQL.- Chapter 2:  Some time with Installation.- Chapter 3:  IO in PySparkSQL.- Chapter 4 :  Operations on PySparkSQL DataFrames.- Chapter 5 :  Data Merging and Data Aggregation using PySparkSQL.- Chapter 6: SQL, NoSQL and PySparkSQL.- Chapter 7: Structured Streaming.- Chapter 8 : Optimizing PySparkSQL.- Chapter 9 : GraphFrames.

Raju Kumar Mishra has strong interests in data science and systems that have the capability of handling large amounts of data and operating complex mathematical models through computational programming. He was inspired to pursue an M. Tech in computational sciences from Indian Institute of Science in Bangalore, India. Raju primarily works in the areas of data science and its different applications. Working as a corporate trainer he has developed unique insights that help him in teaching and explaining complex ideas with ease. Raju is also a data science consultant solving complex industrial problems. He works on programming tools such as R, Python, scikit-learn, Statsmodels, Hadoop, Hive, Pig, Spark, and many others. His venture Walsoul Private Ltd provides training in data science, programming, and big data.
Sundar Rajan Raman is an artificial intelligence practitioner currently working at Bank of America. He holds a Bachelor of Technology degree from the National Institute of Technology, India. Being a seasoned Java and J2EE programmer he has worked on critical applications for companies such as AT&T, Singtel, and Deutsche Bank. He is also a seasoned big data architect. His current focus is on artificial intelligence space including machine learning and deep learning.