Google Cloud platform is catching up and a lot of companies have already started moving their infrastructure to GCP . This course provides the most practical solutions to real world use cases in terms of data engineering on Cloud . This course is designed keeping in mind end to end lifecycle of a typical Big data ETL project both batch processing and real time streaming and analytics .
Considering the most important components of any batch processing or streaming jobs , this course covers
Writing ETL jobs using Pyspark from scratch
Storage components on GCP (GCS & Dataproc HDFS)
Loading Data into Data–warehousing tool on GCP (BigQuery)
Handling/Writing Data Orchestration and dependencies using Apache Airflow(Google Composer) in Python from scratch
Batch Data ingestion using Sqoop , CloudSql and Apache Airflow
Real Time data streaming and analytics using the latest API , Spark Structured Streaming with Python
Micro batching using PySpark streaming & Hive on Dataproc
The coding tutorials and the problem statements in this course are extremely comprehensive and will surely give one enough confidence to take up new challenges in the Big Data / Hadoop Ecosystem on cloud and start approaching problem statements & job interviews without inhibition .
Most importantly , this course makes use of Linux Ubuntu 18.02 as a local operating system.Though most of the codes are run and triggered on Cloud , this course expects one to be experienced enough to be able to set up Google SDKs , python and a GCP Account by themselves on their local machines because the local operating system does not matter in order to succeed in this course .
Courses : 1
Specification: Data Engineering on Google Cloud platform
5 reviews for Data Engineering on Google Cloud platform