Are you planning to enroll in Udacity Data Engineering Nanodegree? If yes, then wait and read this Udacity Data Engineering Nanodegree Review and then decide whether to enroll or not. In this udacity data engineering Nanodegree review, I tried to cover all the important points you need to know before paying to the Udacity Data Engineering Nanodegree. So give your few minutes and get your answer to this question- Is Udacity Data Engineering Nanodegree Worth It?
So, without any delay, let’s get started-
Udacity Data Engineering Nanodegree Review
Before enrolling in Udacity Data Engineering Nanodegree, the first thing you need to know is the eligibility criteria or the Prerequisites.
Because without having prerequisite knowledge, you will feel lost in the Nanodegree program and you may think that you have wasted your money with the wrong program. That’s why this is the first parameter I always consider before selecting any course.
So, let’s see what are the prerequisites for Udacity Data Engineering Nanodegree–
Prerequisites
The first thing, I would like to mention is that Udacity Data Engineering Nanodegree is not a beginner-level program. This is really the only intermediate-advanced data engineering course out there. This Nanodegree program requires the following skills before enrolling in the program-
Python–
If you are a beginner in Python, then don’t directly jump to this program. To get 100% from Udacity Data Engineering Nanodegree Program, you need to know-
- Strings, numbers, and variables; statements, operators, and expressions;
- Lists, tuples, and dictionaries; Conditions, loops;
- Procedures, objects, modules, and libraries;
- Troubleshooting and debugging; Research & documentation;
- Problem-solving; Algorithms and data structures
2. SQL-
Along with Python knowledge, you should be familiar with SQL programmings such as Joins, Aggregations, Subqueries, Table definition, and manipulation (Create, Update, Insert, Alter).
If you meet the following prerequisites, then I would say you are ready for the Udacity Data Engineering Nanodegree. If not, then I would say first learn Python and SQL.
The next important thing you need to know is- Who will teach you? and What are their qualifications? So let’s see the information of the Instructors-
Instructors
- Amanda Moran– She is a developer advocate at DATASTAX.
- Ben Goldberg– Staff Engineer at SPOTHERO
- Sameh El-Ansary– CEO of Novelari & Assistant professor at Nile University
- Olli Livonen– Data Engineer at WOLT
- David Drummond- VP of Engineering at Insight
- Judit Lantos- Data Engineer at Split
- Juno Lee- Instructor
Amazing…Right?
Learning from such experienced and knowledgeable instructors is really amazing and helpful. This is the reason, I personally love Udacity.
Udacity also has its forums, where you can ask instructors your doubt, and they will answer your query.
Now the next important thing you need to know is the Structure of the Course. So, let’s have a look at the course structure.
Course Structure
The Udacity data engineering Nanodegree has 5 courses and each course has separate 1–2 Course Projects. You need to submit these guided projects after completing the course. And the contractor hired by Udacity reviews your projects.
Due to its practical approach, you will get to learn various new things. Because when you implement by yourself, your understanding becomes stronger.
Now let’s see the details of the courses-
- Data Modeling
- Cloud Data Warehouses
- Spark and Data Lakes
- Automate Data Pipelines
- Capstone Project
1. Data Modeling
This is the first course where you will learn how to create NoSQL and relational data models to fill the needs of data consumers. You will also learn how to choose the appropriate data model for a given situation. Each course has some lessons. There are three lessons in the first course.
In the first lesson, you will learn the fundamentals of data modeling and how to create a table in Postgres and Apache Cassandra.
In the second lesson, concepts of normalization and denormalization will be introduced with hands-on projects. And you will also know the difference between OLAP and OLTP databases.
The third lesson of this course will teach you when to use NoSQL databases and how they differ from relational databases. You will also learn how to create a NoSQL database in Apache Cassandra.
There are two projects in the first lesson- Data Modeling with Postgres and Data Modeling with Apache Cassandra.
In these projects, you have to model user activity data for a music streaming app called Sparkify. For this, you have to create a database and ETL pipeline, in both Postgres and Apache Cassandra, designed to optimize queries for understanding what songs users are listening to.
2. Cloud Data Warehouses
The second lesson is focused on data warehousing, specifically on AWS. You will also learn various techniques such as Kimball, Inmon, Hybrid, OLAP vs OLTP, Data Marts, etc. Some of the AWS tools that you’ll be using here are IAM, S3, EC2, and RDS instances.
There are three lessons in this course. In the first lesson, you will understand Data Warehousing architecture, how to run an ETL process to denormalize a database (3NF to Star), how to create an OLAP cube from facts and dimensions, etc.
The second lesson will help you to understand cloud computing and teach you how to create an AWS account and understand their services, how to set up Amazon S3, IAM, VPC, EC2, RDS PostgreSQL.
In the third lesson, you will learn how to implement Data Warehouses on AWS. You will also learn how to identify components of the Redshift architecture, how to run the ETL process to extract data from S3 into Redshift, and how to set up AWS infrastructure using Infrastructure as Code (IaC).
In this course, there is one project where you have to build a cloud data warehouse to find insights into what songs their users are listening to.
3. Spark and Data Lakes
This course provides an introduction to Apache Spark and Data Lakes. In this course, you will learn how to use Spark to work with massive datasets and how to store big data in a data lake and query it with Spark. You will also learn concepts such as distributed processing, storage, schema flexibility, different file formats.
There are four lessons. In the first lesson, you will learn more about Spark and understand when to use Spark and when not to use it.
The second course will teach you data wrangling with Spark and how to use Spark for ETL purposes. In the third course, you will learn about debugging and optimization and how to troubleshoot common errors and optimize their code using the Spark WebUI.
The fourth course is all about data lakes and teaches you how to implement data lakes on Amazon S3, EMR, Athena, and Amazon Glue. You will also understand the components and issues of data lakes.
In this course, there is one project where you have to create an ETL pipeline for a data lake, using data stored in AWS S3 in JSON format.
4. Automate Data Pipelines
In the fourth course, you’ll use all the technologies learned in the above 3 courses. This is an exciting course where you will get an introduction to Apache Airflow and how to schedule, automate, and monitor data pipelines using Apache Airflow.
There are three lessons in this course. In the first lesson, you will learn how to create data pipelines with Apache Airflow, how to set up task dependencies, and how to create data connections using hooks.
In the second lesson, you will learn about data quality such as partitioning data to optimize pipelines, writing tests to ensure data quality, tracking data lineage, etc.
There is one project in this course, where you have to build data pipelines with Airflow. Where you work on the music streaming company’s data infrastructure by creating and automating a set of data pipelines.
5. Capstone Project
The last course is a capstone project. Where you will combine all the technologies learned in the entire course and build a data engineering portfolio project.
In this project, you have to gather data from several different data sources; transform, combine, and summarize it; and create a clean database for others to analyze.
Throughout the project guidelines, suggestions, tips, and resources will be provided by Udacity.
Now let’s see the price and duration of the Udacity Data Engineering Nanodegree program-
Price and Duration
According to Udacity, the Udacity Data Engineering Nanodegree program will take 5 months to complete if you spend 5–10 hours per week.
And for 5 months they cost around $800+. But Udacity offers two options- One is either pay the complete amount upfront or you can pay monthly installments of $399/month.
So this is according to Udacity, but here I would like to tell you how you can complete the full Udacity Data Engineering Nanodegree program in less time.
Excited to know…How?
So, let’s see-
How to Complete the Udacity Data Engineering Nanodegree In Less Time?
To complete the Udacity Data Engineering Nanodegree program in less time, you need to manage your time productively.
You need to plan your day before and create a to-do list for each day. And you need to spend a good amount of time daily on the program.
According to Udacity, you need to spend 10 hours per week to complete the whole program in 5 months.
Right…?
That means, daily you need to spend 1.5 hours, but if you double the time and give daily 3 hours, then you can complete the whole Nanodegree program in less than 3 months.
For managing your time and avoiding any distractions, you can use the Pomodoro technique to increase your learning.
As I mentioned earlier, after each course, you have to work on a project. And each project has a set of rubrics. So before starting a section, I would suggest you just study the rubrics of the project. The rubrics will provide you a rough idea about what topics and lectures are important for the project. So that you can make notes while watching these lectures.
And you can also implement the project phases after watching the related lecture. By doing this way, you can save your time of watching one video two times. One at the time of learning and the second at the time of working on the project.
I hope these tips will help you to complete the Udacity Data Engineering Nanodegree program in less time.
Now I would like to mention the Pros and Cons of Udacity Data Engineering Nanodegree.
Pros of Udacity Data Engineering Nanodegree
- The structure of the course is perfect if you are focused on hands-on practice and believe in “how” to do things like ETL and Data Warehousing.
- You will get Technical mentor support and the mentor will guide you from the start of your Nanodegree program until you finish the whole program.
- Provides good background information on data modeling, traditional data schemas.
- The Udacity Data Engineering Nanodegree highlights data quality and data governance how to introduce tests within your data pipeline.
- Udacity provides a great community of help. They have a Stackoverflow-style Q&A forum for people stuck with assignments but it also has a pretty large slack, with channels for individual assignments and nano-degrees.
- Udacity provides a greater flexible learning program. So that you can learn at your own pace and from the comfort of your smartphone.
Cons of Udacity Data Engineering Nanodegree
- All of the projects (except the capstone) were based on the same problem domain (a song streaming startup), with the same data, using the same schema, using different tools. So if you are not good at learning a new API, it will be difficult for you.
- Some of the lectures were not very polished, had very little post-editing, and not rehearsed.
So the next and most important question is-
Is Udacity Data Engineer Nanodegree Worth It?
Yes, it is worth it but It depends upon how much money and time you want to spend on learning data engineering. You will learn a lot of practical things throughout the Nanodegree program which will definitely help you at your work.
Who Should Enroll in Udacity Data Engineer Nanodegree?
This Udacity Data Engineer Nanodegree is for those who have intermediate-level Python and SQL knowledge and who want to work as a data-engineer. The content of the course is of very high quality. So if you don’t have enough Python and SQL knowledge, I would not suggest you enroll in Udacity Data Engineer Nanodegree.
Now it’s time to wrap up this Udacity Data Engineering Nanodegree Review.
Conclusion
I hope this Udacity Data Engineering Nanodegree Review helped you to decide whether to enroll in Udacity Data Engineering Nanodegree or not?
If you found this Udacity Data Engineering Nanodegree Review helpful, you can share it with others. Or if you have any doubt or questions, feel free to ask me in the comment section.
All the Best!
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