Data Engineering on Google Cloud platform

Rating 4.19 out of 5 (413 ratings in Udemy)

What you'll learn

Pyspark for ETL/Batch Processing on GCP using Bigquery as data warehousing component
Automate and orchestrate SparkSql batch jobs using Apache Airflow and Google Workflows
Sqoop for Data ingestion from CloudSql and using Airflow to automate the batch ETL
Difference between Event-time data transformations and process-time transformations
Pyspark Structured Streaming - Real Time Data streaming and transformations
Save real time …

Rating 4.19 out of 5 (413 ratings in Udemy)

What you'll learn

Pyspark for ETL/Batch Processing on GCP using Bigquery as data warehousing component
Automate and orchestrate SparkSql batch jobs using Apache Airflow and Google Workflows
Sqoop for Data ingestion from CloudSql and using Airflow to automate the batch ETL
Difference between Event-time data transformations and process-time transformations
Pyspark Structured Streaming - Real Time Data streaming and transformations
Save real time streaming raw data as external hive tables on Dataproc and perform ad-hoc queries using HiveSql
Run Hive-SparkSql jobs on Dataproc and automate micro-batching and transformations using Airflow
Pyspark Structured Streaming - Handling Late Data using watermarking and Event-time data processing
Using different file formats - AVRO and Parquet . Different scenarios in which to use the file formats

Description

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 ETLjobs 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 GCPAccount by themselves on their local machines because the local operating system does not matter in order to succeed in this course .

P.S : 88BA1461141F3A2A6E2D for half price .

Duration 10 Hours 58 Minutes

Paid

Self paced

Intermediate Level

English (US)

3607

Rating 4.19 out of 5 (413 ratings in Udemy)

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