Data Engineering

Data Engineering Scenarios

In line with the data-engineering-scenarios Github organization that I created (https://github.com/data-engineering-scenarios), this page will contain notes on different scenarios - both finished and planned.

These scenarios focus on different technologies available via Google Cloud or Amazon Web Services. Roughly, they can be grouped as follows:

Dataproc

This is the "classic" big data technology - distributed computing on clusters.

• Dataproc - Google Cloud version, allocate a cluster and run jobs through it

• Hadoop - the big data technology that started it all; processing data in parallel on nodes using MapReduce framework

• Spark - similar to Hadoop, but more focused on efficient computation
• PySpark - Python bindings for Spark (Java)
• SparkSQL - allows SQL queries in Spark programs, e.g., running an SQL query on Hive, and passing the results to Spark computations

• Pig - works with Hadoop; higher-level scripting language that shortens Hadoop jobs

• Hive - data warehouse that sits on Hadoop (or Pig); gives SQL-like interface to query data. (SQL queries are implemented in MapReduce)
• HBase - Java software for non-relational databases, analogous to Google's BigTable; runs on Hadoop, can serve as source/sink for MapReduce queries, is a column-based key store; no SQL queries - MapReduce only
• Phoenix - turns HBase (non-relational, non-SQL database) into an SQL-like data store
• Parquet - column-based table storage that sits on Hadoop

GCDEC

Working through the Google Cloud Data Engineer certification course... See GCDEC for pages related to that.