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Big Data Processing on Google Dataproc
Introduction
At present, about 2.5 quintillion bytes (2500 PetaBytes) of data is produced by humans every day (Source: Social Media Today). Processing this much quantity of data is a headache. This is where Big Data Processing comes into the play and acts as a painkiller for this headache.
There are many tools and technologies already available in the market — such as Hadoop, Spark, Hive, and many more. But installing, configuring, and managing all these technologies on premise is quite a strenuous task for Data Engineers. This whole process becomes effortless because of Google Dataproc, which lets you work with Hadoop and Spark Frameworks on Cloud.
What will you Learn?
- What is Google Dataproc?
- Why use Dataproc?
- Getting started with Dataproc
- Running Spark, Hive Commands, JupyterLab and HBase
What is Google Dataproc?
It is a Hadoop and Spark managed service which lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. It helps you to create and manage the clusters. Even with less time, Dataproc lets you focus on the jobs/query without worrying about the money spent on administration.
Why use Dataproc?
Dataproc has a number of advantages such as:
