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Data Engineering on Google Cloud platform

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Онлайн-курс

Обучение платное

Not set

10 часов курса

Курс от Udemy

$ 12.99

Data Engineering on Google Cloud platform

$ 12.99

Продолжительность: 10 ч
Уровень: Средний
Язык: Not set
Заинтересовано: 514
Сертификат: Да

Чему вы научились?

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

О курсе

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 .

P.S : 88BA1461141F3A2A6E2D for half price .

Требования

Basic Python Skills
Comfortable with basic Linux/Bash commands
Basic understanding of spark (python) and how hadoop works
A Google cloud compute account / if not sign up for a free trial account
Comfortable with setting up Google SDKs regardless of the operating system
Should have the desire to learn and eagerness to explore more about the relevant topics

Лекторы

Siddharth Raghunath

Data Engineer / Cloud Data Engineer / Passionate Techie

Платформа

Курсы Udemy подойдут для профессионального развития. Платформа устроена таким образом, что эксперты сами запускают курсы. Все материалы передаются в пожизненный доступ. На этой платформе можно найти курс, без преувеличений, на любую тему – начиная от тьюториала по какой-то камере и заканчивая теоретическим курсом по управлению финансовыми рисками. Язык и формат обучения устанавливается преподавателем, поэтому стоит внимательно изучить информацию о курсе перед покупкой.

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