Day 1 Example
postgres json example and its limitation
The need of a NoSQL database in some cases
Day 2 Example
MongoDB data types
Basic MongoDB CRUD Operatons (will be continued in Day3)
Day 3 Example
Design and implement more complex queries - focusing on query and projection operators.
Day 4 Example
Design and create aggregation pipeline.
Create and drop index to improve query performance.
Day 6 Example
Load data from S3 and preprocess using Pyspark.
Store created aggregates into MongoDB.
Day 7 Example
Creating DataFrames (2 ways)
DataFrame API Basics
Day 8 Example
Apply SQL functions with DataFrame - Scalar/Aggregate/Window/UDF/Join
Registering DataFrame in the Table Catalog
Loading/Writing Data using SparkSQL - csv, parquet, json
Day 9 Example
Load/Write data to S3/MongoDB locally.
Apply logistic regression to adult.dat using Spark ML.
Day 10 Example
Apply decision tree/Random Forest/ and KMeans using penbased.dat
Create Spark ML pipeline.
Day 12 Example
Create H2O dataframes.
Apply H2O AutoML and evaluate/explain models.