Course Overview
Any data pipeline needs both data lakes and warehouses, which are its two essential components. The use cases for each form of storage are highlighted in this course, which also delves deeply into the technical aspects of the Google Cloud data lake and warehouse solutions. The function of a data engineer is also covered in this course, along with the advantages of a strong data pipeline for company operations. It looks at the benefits of performing data engineering in a cloud setting.
The first lesson in the Data Engineering on Google Cloud series is this one. Take a part in the Building Batch Data Pipelines on the Google Cloud course after finishing this one.
The emblem that is shown above can be yours if you've finished this course! Visit your profile page to see all the badges you have earned.
Prerequisites
To get full advantage from this course, professionals should have completed the course on "Google Cloud Big Data and Machine Learning Fundamentals" or have equal expertise.
The professional ought to possess the following, as well:
basic knowledge of a common query language, such as SQL.
data modelling and ETL (extract, transform, load) activities experience
knowledge of creating applications with a popular programming language like Python.
familiarity with statistics and machine learning
Audience Profile
Developers who are in charge of querying datasets, displaying query results, and producing reports should take this course. Data Engineer, Data Analyst, Database Administrator, and Big Data Architect are examples of specific employment roles.
Learning Objectives
- Differentiate between data lakes and data warehouses.
- Explore the Google Cloud's data lake and warehouse solutions, as well as the use cases for each form of storage.
- Talk about the responsibilities of a data engineer and how successful data pipelines may assist corporate operations.
- Consider the benefits of performing data engineering in a cloud setting.
Content Outline
This session introduces the Modernizing Data Lakes and Data Warehouses with Google Cloud course and the Data Engineering on Google Cloud source series.
The purpose of this module is to define the job of a data engineer and to support the argument that data engineering should be performed in the cloud.
In this module, we describe a data lake and how to utilize Cloud Storage as your data lake on Google Cloud.
This module discusses BigQuery as a data warehousing option on Google Cloud.
A summary of the key learning points
FAQs
A: Google BigQuery is a cloud-based enterprise data warehouse that offers rapid SQL queries and interactive analysis of massive datasets. BigQuery was designed on Google's Dremel technology to process read-only data.
A: Large amounts of organised, semi-structured, and unstructured data can be stored, processed, and secured in a data lake, which is a centralised repository. Without regard to size restrictions, it may process any type of data and store it in its original format. Learn more about updating your Google Cloud data lake.
A: A data lake contains all an organization's data in a raw, unstructured form and can store the data indefinitely — for immediate or future use.
A data warehouse contains structured data that has been cleaned and processed, ready for strategic analysis based on predefined business needs.
A: Radiant has highly intensive selection criteria for Technology Trainers & Consultants who deliver training programs. Our trainers & consultants undergo a rigorous technical and behavioral interview and assessment process before they are onboarded in the company.
Our Technology experts/trainers & consultants carry deep-dive knowledge in the technical subject & are certified by the OEM.
Our training programs are practically oriented with 70% – 80% hands-on training technology tools. Our training program focuses on one-on-one interaction with each participant, the latest content in the curriculum, real-time projects, and case studies during the training program.
Our faculty will quickly provide you with the knowledge of each course from the fundamental level, and you are free to ask your doubts at any time from your respective faculty.
Our trainers have the patience and ability to explain complex concepts simplistically with depth and width of knowledge.
To ensure quality learning, we provide support sessions even after the training program.
A: To attend the training session, you should have operational Desktops or Laptops with the required specification and a good internet connection to access the labs.
A: We recommend you attend the live session to practice & clarify the doubts instantly and get more value from your investment. However, if, due to some contingency, you have to skip the class, Radiant Techlearning will help you with the recorded session of that particular day. However, those recorded sessions are not meant only for personal consumption and NOT for distribution or any commercial use.
A: Radiant Techlearning has a data center containing a Virtual Training environment for participantshands-on practicece.
Participants can easily access these labs over Cloud with the help of a remote desktop connection.
Radiant virtual labs allow you to learn from anywhere and in any time zone.
A: The learners will be enthralled as we engage them in the natural world and Oriented industry projects during the training program. These projects will improve your skills and knowledge and give you a better experience. These real-time projects will help you a lot in your future tasks and assignments.