Best Data Engineering Courses 2022(Free & Paid)

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Best Data Engineering Courses. Data engineering is a process that starts with data acquisition and ends with data preparation for analysis. The process starts by acquiring the right data, and then ensuring that the data is prepared for analysis in a way that allows for accurate decision making. Data engineering also involves creating tools and processes to help manage and analyze the data.

Data engineering is a process of extracting insights and understanding from data. It is a critical skill for today’s businesses. In this article, we will discuss the top 10 data engineering courses that you can take to learn data engineering.

What is Data Engineering?

Data engineering is the process of transforming data into useful forms for business purposes. It involves understanding how data is collected, structured, and transformed to meet the needs of business users. Data engineers work with a variety of data sources, including raw data, relational databases, streaming data, and big data. They use a variety of tools and techniques to extract insights from data and make it usable for business decisions.

Data exploration: A variety of tools can be used to explore data and understand it better, such as Excel, Tableau, and R.

Data modeling: This involves creating structures that make data easier to understand and use for business decisions. Modeling can be done using tools such as SQL or ERD modeling tools.

Data analysis: This involves using data to identify patterns and trends, which can help make better business decisions. Various analytical techniques can be used, such as regression analysis or decision trees.

Why is Data Engineering Important?

Data engineering is important because it helps organizations make better decisions by understanding the data they have. Data engineering can help organizations by cleansing, transforming, and loading data into a database; managing and monitoring data pipelines; and creating reports. Additionally, data engineering can help improve the speed of data processing and enable more efficient decision making.

Data preparation: This involves transforming data into a form that is easier to use for business purposes. This can involve cleansing the data, transforming it into a different format, and adding metadata.

Data integration: This involves integrating different data sources into a cohesive system. Data integration can be done using various tools, such as connectors or APIs.

Data quality assurance: This involves ensuring that the data is accurate and reliable before it is used in business decisions. Various techniques can be used to check the accuracy of data, such as manual checks or automated checks using machine learning algorithms.

How to Choose the Right Data Engineering Course for You?

In order to be a successful data engineer, you need to have a strong foundation in mathematics and computer science. However, there is no one-size-fits-all answer when it comes to choosing the right data engineering course for you.

Some factors you should consider include your experience and interests in data engineering, the level of difficulty of the course, and whether or not you want to attend an online or on-campus program. Additionally, make sure to research which schools offer accredited data engineering degrees. Once you have determined which course is right for you, make sure to research the program and find a placement agency that can help you get started in your new career.

Some data engineering courses you may want to consider include:

  1. Data integration using connectors and APIs
  2. Data quality assurance
  3. Advanced data mining and machine learning
  4. Advanced data visualization
  5. Credible Instructor with years of experience in the data engineering field
  6. User-friendly platform
  7. Excellent course materials
  8. Offer hands-on experience
  9. Provide excellent value for money
  10. Positive reviews from real students
  11. Data engineering for big data

Best Data Engineering Courses for Beginner

There are many data engineering courses that are perfect for beginners. However, it is important to choose a course that is right for your skills and interests. For example, if you are new to programming, a course that focuses on programming languages may be more appropriate than one that covers more general data engineering concepts.

1.Data Engineering Foundations Specialization

The goal of data engineering is to make quality data available for fact-finding and data-driven decision-making. This course from IBM will help you learn the fundamental skills needed to get started in this field, regardless of your experience level. With the help of this course, you will gain practical experience and skills.

You’ll develop your understanding of data engineering so that you can apply it to a career in data science or technology. And you’ll build the foundation of your career in data engineering by completing this Specialization coursework.

You Will Data engineering Tool Like MySQL, PostgresSQL, IBM Db2, PhpMyAdmin, pgAdmin, IBM Cloud, Python, Jupyter notebooks, Watson Studio, etc.

You Will Learn

  • Information Engineering
  • Python Programming
  • Extraction, Transformation And Loading (ETL)
  • Relational Database Management System (RDBMS)
  • SQL
  • Data Science
  • Database (DBMS)
  • NoSQL
  • Data Analysis
  • Pandas
  • Numpy
  • Web Scraping

Feature of Course

  • 500K Student already enrolled
  • 4.7 Rating with 504 student reviews
  • Affordable Price

2. IBM Data Engineering Professional Certificate

This Coursera Professional Course is designed for absolute beginners who want to learn data engineering. You will be taught by more than a dozen top-notch IBM experts who have many years of experience in the field.

Course Content

The program consists of 13 minor courses as follows:

1. Introduction to Data Engineering –You will Learn the data engineering ecosystem and its lifecycle.

2. Python for Data Science, AI & Development – You will learn to code in Python and understand programming concepts. This course is useful for data science and machine learning.

3. Python Project for Data Engineering – In This Course, You Will Learn projects that will b useful for future work.

4. Introduction to Relational Database (RDBMS) – You will learn fundamental concepts of RDBMS and data models. 

5. Databases and SQL for Data Science with Python –  You will Learn how data engineers use it to communicate and extract data from databases.

6. Introduction to NoSQL Databases – You will Learn NoSQL Database in this complete course.

7. Introduction to Big Data with Spark and Hadoop –   This course will cover the basics of big data and its application in data analytics.

8. Data Engineering and Machine Learning Using Spark – The eighth course will provide an overview of using Apache Spark in data engineering and machine learning applications. Then work with Spark MLlib to perform tasks such as ETL.

9. Hands-on Introduction to Linux Commands and Shell Scripting – In This course You will learn how to use Linux shell commands and shell scripting. 

10. Relational Database Administration (DBA) – In this course, you will learn how to manage databases effectively. You will understand methods and best practices for configuring, upgrading, monitoring, maintaining, and securing your database.

11. ETL and Data Pipelines with Shell, Airflow, and Kafka – You will Get more extra features related to data and database.

12. Getting Started with Data Warehousing and BI Analytics – The twelfth course will focus on data repositories, specifically data warehouses. After that, you will learn about business intelligence analytics and gain experience using IBM Cognos.

13. Capstone Project – The final course in the data engineering program provides you with an opportunity to use all of the knowledge you have acquired throughout the program and assume a role as a data engineer for a virtual organization.

You will Learn

  • Relational Database Management Syste (RDBMS)
  • ETL & Data Pipelines
  • NoSQL and Big Data
  • Apache Spark
  • SQL
  • Data Science
  • Database (DBMS)
  • NoSQL
  • Python Programming
  • Data Analysis
  • Pandas
  • Numpy

Feature Of Course

  • 13,089 student already enrolled
  • 4.6 Rating with 1,046 student reviews
  • Affordable Price

3. Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate

This professional course incorporates Qwiklabs platform labs that allow you to apply the skills you learn. Projects incorporate Google Cloud Platform products used within Qwiklabs, which will give you practical hands-on experience with the concepts explained throughout the modules.

This course provides an opportunity to learn by doing. Projects will incorporate topics such as Google BigQuery, which are used and configured within Qwiklabs. You can expect to gain practical experience with the concepts explained throughout the modules.

Course Content

There are 6 Courses in this Professional Certificate

  1. Google Cloud Big Data and Machine Learning Fundamentals
  2. Modernizing Data Lakes and Data Warehouses with Google Cloud
  3. Building Batch Data Pipelines on GCP
  4. Building Resilient Streaming Analytics Systems on Google Cloud
  5. Smart Analytics, Machine Learning, and AI on GCP
  6. Preparing for the Google Cloud Professional Data Engineer Exam

You will Learn

  • Information Engineering
  • Google Cloud
  • Bigquery
  • Tensorflow
  • Cloud Computing
  • Google Cloud Platform

Feature of Course

  • 63,331 student already enrolled
  • 4.6 Rating with 1,046 student reviews
  • Affordable Price

4. Data Engineering Essentials using SQL, Python, and PySpark

There are many data engineering courses available to students. However, some of the best courses to give students a good foundation in data engineering include SQL and Python. Additionally, courses like Spark Core provide a great way to get started with data engineering. This course will teach you the basics of data engineering, including using SQL, Python for Hadoop, Hive or Spark SQL; understanding the development and deployment cycle of Python applications; and reviewing Spark Jobs.

This course is hands-on with thousands of tasks, you should practice as you go through the course. You will learn Data Engineering using Spark SQL (PySpark and Spark SQL). Understand how to write high quality Spark SQL queries using SELECT, WHERE, GROUP BY, ORDER BY, ETC.

This course is designed by a 20+ year veteran with experience in data engineering and big data. He has several certifications and has taught hundreds of thousands of IT professionals about data engineering and big data.

Feature of Course

  • Popular Course on Udemy
  • You will Data Engineering Programming Completely
  • Learn Data Engineering using Spark 
  • 33,611 students Already Enrolled
  • 4.3 Rating with 786 Student Reviews
  • 30 Day Money Back Guarantee
  • Suitable Price for Everyone
  • This course give value to money

5. Data Engineering using AWS Analytics Services

Data Engineering course covers the basics of building data pipelines with AWS Analytics Stack. This includes services such as Glue, Elastic Map Reduce (EMR), Lambda Functions, Athena, QuickSight, and many more. In this course, we will walk you through how to build a pipeline using these tools and make sure that your data is ready for downstream analysis.

Some Main Point of this course you will Learn

  • Setup Development Environment
  • Getting Started with AWS
  • Development Life Cycle of Pyspark
  • Overview of Glue Components
  • Setup Spark History Server for Glue Jobs
  • Deep Dive into Glue Catalog
  • Exploring Glue Job APIs
  • Glue Job Bookmarks
  • Data Ingestion using Lambda Functions
  • Streaming Pipeline using Kinesis
  • Consuming Data from s3 using boto3
  • Populating GitHub Data to Dynamodb

Getting Started with AWS

  • Introduction – AWS Getting Started
  • Create s3 Bucket
  • Create IAM Group and User
  • Overview of Roles
  • Create and Attach Custom Policy
  • Configure and Validate AWS CLI

Development Lifecycle for Pyspark

  • Setup Virtual Environment and Install Pyspark
  • Getting Started with Pycharm
  • Passing Run Time Arguments
  • Accessing OS Environment Variables
  • Getting Started with Spark
  • Create Function for Spark Session
  • Setup Sample Data
  • Read data from files
  • Process data using Spark APIs
  • Write data to files
  • Validating Writing Data to Files
  • Productionizing the Code

Overview of Glue Components

  • Introduction – Overview of Glue Components
  • Create Crawler and Catalog Table
  • Analyze Data using Athena
  • Creating S3 Bucket and Role
  • Create and Run the Glue Job
  • Validate using Glue CatalogTable and Athena
  • Create and Run Glue Trigger
  • Create Glue Workflow
  • Run Glue Workflow and Validate

Using Athena to run Serverless Queries

  • Getting Started with Athena
  • Accessing Glue Catalog Tables using Athena
  • Create Athena Tables and Populating data into Athena tables
  • Create Athena Tables using query results using CTAS
  • Amazon Athena Architecture
  • Partitioned Tables in Athena
  • Running Athena Queries and Commands using AWS CLI
  • Running Athena Queries and Commands using Python boto3

Cloud Data Warehouse using AWS Redshift

  • Create Redshift Cluster using Free Tier
  • Setup Databases as part of Redshift Cluster and perform CRUD operations
  • Copy CSV or delimited data from s3 into Redshift Tables using credentials as well as iam_role
  • Copy JSON data from s3 into Redshift Tables using iam_role

Feature of Course

  • Bestseller Course On Udemy
  • 3,641 students Already Enrolled
  • 4.5 Rating with 300 student Reviews
  • 30 Day Money Back Guarantee
  • Suitable Price for Everyone
  • This course give value to money

6. Azure Data Engineering – Build Data Ingestion Engine Project

This course is designed to help you learn the Data Engineering techniques of building metadata-driven frameworks with Azure Data Engineering tools. Data frameworks are now an industry norm, and it is important to be able to visualize, design, plan and implement them. The course’s first objective is to help you get started with the Azure Data Factory platform. Once you have a good understanding of how the platform works, it will be easier to use this same pattern to onboard other data sources and sinks.

Some Basic Point cover in This course

Creating your first Pipeline

What will be covered  is as follows;

1. Introduction to Azure Data Factory

2. Unpack the requirements and technical architecture

3. Create an Azure Data Factory Resource

4. Create an Azure Blob Storage account

5. Create an Azure Data Lake Gen 2 Storage account

6. Learn how to use the Storage Explorer

7. Create Your First Azure Pipeline.

Metadata Driven Ingestion

1. Unpack the theory on Metadata Driven Ingestion

2. Describing the High-Level Plan for building the User

3. Creation of a dedicated Active Directory User and assigning appropriate permissions

4. Using Azure Data Studio

5. Creation of the Metadata Driven Database (Tables and T-SQL Stored Procedure)

6. Applying business naming conventions

7. Creating an email notifications strategy

8. Creation of Reusable utility pipelines

9. Develop a mechanism to log data for every data ingestion pipeline run and also the batch itself

10. Creation of a dynamic data ingestion pipeline

11. Apply the orchestration pipeline

12. Explanation of T-SQL Stored Procedures for the Ingestion Engine

13. Creating an Azure DevOps Repository for the Data Factory Pipelines

Event-Driven Ingestion

1. Enabling the Event Grid Provider

2. Use the Getmetadata Activity

3. Use the Filter Activity

4. Create Event-Based Triggers

5. Create and Merge new DevOps Branches

Feature of Course

  • Highest Rated Course on Udemy
  • 4.6 rating with 200 student reviews
  • 2,073 students Student Already Enrolled
  • 30 Day Money Back Guarantee
  • Suitable Price for Everyone
  • This course give value to money

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Is Data Engineering a good career choice?

Data engineering is a growing field that has a lot of potentials. It can be a good career choice if you have the right skills and knowledge. You need to have strong programming skills, as data engineering projects often involve writing code. You also need to be able to understand data structures and algorithms, as well as have experience working with databases.

What is the salary of a Data Engineer?

Data engineering is a rapidly growing field, with increasing demand for skilled professionals. According to Indeed, the median salary for data engineers was $96,070. However, salaries vary greatly depending on experience and skills. A data engineer typically earns a salary in the range of $75,000 to $110,000 per year. The exact salary depends on a number of factors, including experience and qualifications.

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