Udacity Data Engineering Nanodegree Review (2026): AWS vs Azure Compared

Last updated: 13 October 2026 (full refresh; syllabus, prices and exam details re-checked 12 October 2026). Written by Josh Hutcheson, OnlineCourseing editor. See our review methodology.

Some links on this page are affiliate links: if you enroll through them we may earn a commission at no extra cost to you. It does not change our verdict, and several of the alternatives we name pay us nothing.

QUICK VERDICT

See Data Engineering with AWS →

BY THE NUMBERS (CHECKED 12 OCTOBER 2026)

• 4.6 out of 5 from 1,294 learner reviews on Udacity’s Data Engineering with AWS page; most were written before the October 2026 rebuild. The Azure page does not display a rating.
• 4 courses, 16 lessons and 4 projects in the AWS program; 22 lessons and 4 projects in the Azure program.
• 66 hours (AWS, Intermediate) and 56 hours (Azure, Advanced), by Udacity’s estimate.
• Cloud market share in Q2 2026: Amazon 28%, Microsoft 20%, Google 15%, in a market that grew 43% year on year (Synergy Research Group).

Prefer Azure? See the Azure Data Engineering program.

AWS or Azure? Choose your variant in 30 seconds

The two programs teach the same job, data engineering, on different clouds and with different tools. They are separate Nanodegrees with separate syllabi, instructors and projects, so pick the cloud first and the program follows.

A quick rule of thumb: read ten data engineer job postings in your market. If seven or more list AWS services, take AWS. If seven or more list Azure services, take Azure. If it is mixed, take AWS: it is the larger market, and its program is the more current of the two. The modeling, SQL and Spark skills transfer between clouds either way; what does not transfer is the console and service names, which is what interviewers screen on.

What is the Udacity Data Engineering Nanodegree?

Udacity’s Data Engineering Nanodegrees are project-based programs that teach you to build the systems analysts and data scientists rely on: data models, warehouses, lakehouses and scheduled pipelines. Each program ends in four portfolio projects, and every submission is reviewed by a Udacity reviewer who either passes it or sends it back with notes on what to fix.

The format is self-paced: written and video lessons, hands-on exercises in the browser and in the cloud console, then a project at the end of each course. There are no live classes and no fixed start dates. Both programs are marked as eligible for credit toward Udacity’s own master’s degrees, which matters only if you might continue into one.

What it costs, and how the discounts work

Udacity charges $249 a month, $846 for a four-month bundle (about $212 a month, 15% less than paying monthly, after which it converts to month-to-month), or $999 to buy a single program outright. A subscription covers the whole catalog of 500+ courses and programs. Sources: the nd027 and nd0277 program pages, read 12 October 2026.

Udacity runs percentage-off promotions most months; on 12 October 2026 its program pages advertised 40% off any plan for new users. Our links pass the best discount we can verify to the checkout automatically, so you do not need to type a code. Confirm the final price on the checkout page before you pay.

Which plan is cheapest depends on how fast you work. At 10 hours a week the AWS program’s 66 hours take about seven weeks of lessons, plus project resubmissions, so two months on the monthly plan ($498 at list price) undercuts the $846 bundle. The bundle wins if you expect to need three months or more, or want a second program on the same subscription. The $999 one-time price only makes sense if you need more than four months and want no renewal.

What Udacity’s own outcome and salary numbers mean

The AWS program page shows two numbers worth reading carefully. First, “96% of Udacity graduates reported a positive career outcome”, from Udacity’s Q2 2026 Learner Outcome Survey: that is a self-reported survey of graduates across all programs, not a placement rate for this one. Second, U.S. data engineer pay of $115K at entry level, $230K mid-career and $510K for experienced engineers, sourced to Built In, Glassdoor and Levels.fyi and labeled total compensation. Total compensation includes bonuses and stock, and those sources lean toward large tech employers, so treat the entry figure as an upper bound for a first data engineering job, not an expectation.

AWS variant: Data Engineering with AWS (nd027)

The AWS program is rated Intermediate and estimated at 66 hours. Udacity updated it on October 7, 2026, and the current syllabus is built around a lakehouse on AWS rather than the Cassandra-and-Spark course many older reviews describe. Its four instructors are Koosha Totonchi (Principal Data Engineer), Chester Ismay (AI and data science educator and consultant), Eduardo Mota (Senior Cloud Data Architect) and Jo-L Collins (Senior Lead Data Scientist). Source: the nd027 syllabus, read 12 October 2026.

Course 1: Data Modeling with AWS (18 hours)

Relational, document and graph modeling side by side. You design normalized PostgreSQL schemas (with JSONB columns and star schemas), model MongoDB collections with embedding and referencing, and build Neo4j graphs queried with Cypher. The course also covers OLTP versus OLAP workloads, hot and cold data, and polyglot persistence: when one application should use more than one kind of database.

Lessons: Introduction to Data Modeling; Data Modeling for Relational Databases; Data Modeling for Document Stores and NoSQL; Data Modeling for Graph Databases; Project: Data Modeling for ACME.

Project: design a normalized PostgreSQL schema, flexible MongoDB collections and a Neo4j recommendation graph for a growing e-commerce company, and explain the trade-offs between them.

Course 2: Data Warehouses on AWS (13 hours)

Dimensional modeling (fact grain, surrogate keys, star and snowflake schemas), then Python ETL that pulls from PostgreSQL, Cassandra and Neo4j, cleans and conforms the data, and loads it into Amazon Redshift through S3 with the COPY command. You write Redshift DDL with distribution styles, sort keys and compression, build materialized views and run data quality checks.

Lessons: Introduction to Data Warehousing; Dimensional Modeling for Analytics; Extracting and Transforming Source Data; Loading, Optimization, and Validation in Redshift; Project: Build a Multi-Source E-commerce Analytics Warehouse in Redshift.

Project: a star-schema warehouse in Redshift that integrates three source systems with a validated load.

Course 3: Data Lakes and Lakehouses on AWS (15 hours)

S3 as the lake, change data capture from PostgreSQL through AWS DMS, the Glue Data Catalog, Athena queries and Lake Formation governance. You process bronze, silver and gold layers with PySpark (with partitioning and caching), then build Apache Iceberg tables and S3 Tables with ACID MERGE for CDC upserts and time-travel queries.

Lessons: Introduction to Data Lakes and Lakehouses; Building Data Lakes on AWS; Processing Data in the Lake with Spark; Implementing Lakehouse Tables; Project: Exastore Data Lakehouse on AWS.

Project: a full lakehouse for a fictional retailer, Exastore, with CDC and batch ingestion, Spark ETL, medallion layers and lakehouse tables.

Course 4: AWS Data Pipelines and Orchestration with Airflow (20 hours)

Apache Airflow from DAG basics (tasks, XCom, Jinja templating, branching, runtime parameters) to production patterns: single-responsibility tasks, data intervals and catchup, asset-driven scheduling and dynamic task mapping. You build ETL and ELT pipelines through S3 into Redshift with data constraint checks, then a lakehouse workflow with Glue crawlers, Iceberg and Athena, and deploy to Amazon MWAA.

Lessons: Introduction to Data Pipelines and Airflow; Data Lineage and Orchestration; Orchestrating Warehouse Workflows with Amazon Redshift; Orchestrating Lakehouse Workflows with AWS Glue and Athena; Project: AWS Data Lakehouse Pipeline for Sparkify.

Project: three asset-triggered DAGs that ingest, transform and promote data through raw, transaction and analytics layers.

What changed in the rebuild: the old program centered on Cassandra, a Spark data lake and an open-ended capstone. The new one replaces the capstone with a fourth guided project, adds MongoDB and Neo4j to the modeling course, and moves the lake work to Iceberg tables, S3 Tables and Lake Formation, which is closer to what AWS teams build now. Cassandra survives only as one of the three source systems in the warehouse project.

View the nd027 syllabus →

Azure variant: Data Engineering with Microsoft Azure (nd0277)

The Azure program is rated Advanced and estimated at 56 hours across 22 lessons. Udacity last updated it on September 6, 2026, and its page does not display a learner rating. Instructors: Vishnu (Lucky) Pamula (Senior Cloud Solution Architect at Microsoft), Amanda Moran and Matt Swaffer, PhD. Source: the nd0277 syllabus, read 12 October 2026.

Data Modeling (9 hours)

Relational models in PostgreSQL and non-relational models in Apache Cassandra, including when each fits the data consumers’ needs. Project: model event data for a music streaming app in Cassandra and build its ETL pipeline.

Cloud Data Warehouses with Azure (14 hours)

Data warehouse concepts and OLAP cubes, ETL versus ELT, cloud warehouse technology in general and Azure’s in particular, implemented in Azure Synapse Analytics. Project: a Synapse data warehouse for bike share analytics.

Data Lakes and Lakehouses with Spark and Azure Databricks (14 hours)

The big data ecosystem, Spark for wrangling, debugging and optimizing jobs, then Spark clusters, data lakes and lakehouse architecture on Azure Databricks. Project: a Databricks lakehouse for the same bike share data.

Data Pipelines with Azure (18 hours)

Building, transforming, monitoring and productionizing pipelines in Azure Data Factory and Synapse pipelines, including data quality. Project: data integration pipelines for a New York City payroll analytics platform.

What the Azure program does not cover: Microsoft Fabric, which is now the focus of Microsoft’s data engineering certification, DP-700 (Fabric Data Engineer Associate). If that certification is your goal, pair the program with Microsoft Learn’s DP-700 material. See our Azure Data Engineer certification guide.

View the nd0277 syllabus →

What you actually build (the portfolio that gets you hired)

Each program produces four reviewed projects you can put on GitHub and talk through in an interview. The AWS set reads like a modern data platform built in stages:

• Multi-database data model: the same business modeled in PostgreSQL, MongoDB and Neo4j, with the trade-offs written up.
• Redshift analytics warehouse: three source systems integrated into a star schema with a validated load.
• AWS data lakehouse: CDC ingestion, Spark ETL across bronze, silver and gold layers, and Iceberg tables.
• Airflow lakehouse pipeline: event-driven DAGs that move data through S3, Glue, Iceberg and Athena, deployed on MWAA.

The Azure set is a Cassandra data model, a Synapse warehouse, a Databricks lakehouse and Data Factory pipelines. It covers the same ground, but with older modeling tools and without Fabric.

A portfolio like this is the main reason to pay Udacity’s price over a cheaper video course. The projects are guided scenarios rather than your own data, so every graduate has the same four. Add one project of your own afterward if you want something no other applicant has.

How to present the projects to employers

• One repository per project, each with a README that states the business problem, an architecture diagram, the AWS services used and why, and how to run it.
• Show the trade-offs. The modeling project asks you to compare relational, document and graph designs; put that comparison in the README, because it is the part interviewers ask about.
• Show data quality. The warehouse and pipeline projects include validation checks. Say what each check catches.
• Write your own solution. Former students’ repositories for older versions of this program are public. Copying one defeats the review and leaves you unable to answer follow-up questions.
• Add a fifth project on your own data, ideally from the industry you want to work in, built with the same stack.

Prerequisites: this is NOT a beginner program (either variant)

Udacity rates the AWS program Intermediate and the Azure program Advanced, and lists nine prerequisites for each. For the AWS program they are relational data models, command line basics, intermediate Python, relational database basics, basic GitHub and intermediate SQL, among others. In practice you should be able to:

• Python: write functions, loop over files and parse JSON without looking up syntax.
• SQL: write joins, subqueries and GROUP BY queries; window functions help in the warehouse course.
• Command line and Git: move around a terminal, run scripts and push to GitHub.
• For Azure: be comfortable with distributed processing ideas before you start; Advanced is an honest label.

A four-week warm-up if you are close but not ready

If you are missing Python or SQL entirely, start with our Programming for Data Science with Python review, which covers Udacity’s own prerequisite path, or a cheaper beginner course from our data engineering courses guide.

Cloud costs: what Udacity covers and what you might pay

Older reviews of this program, including an earlier version of this page, warned of $100 or more a month in cloud bills on top of the subscription. That is not how the AWS program works today.

AWS variant cloud costs

For AWS-based programs Udacity supplies the AWS account: you open the AWS console from the lesson’s Cloud Resources tab (Udacity Support), and Udacity sets a fixed budget or credits for each student’s AWS Cloud Labs (AWS Cloud Resource Best Practices). The practical risk is running that budget down with a resource you forgot to delete, not a surprise bill on your own card. Udacity’s own guidance names Redshift clusters specifically: do not leave them running overnight. Delete clusters, Glue jobs, DMS replication instances and S3 buckets when a lesson is done so the budget lasts through the final project, and keep buckets private to avoid data transfer charges.

Azure variant cloud costs

The Azure program’s public syllabus does not say whether Udacity supplies Azure access. Udacity’s Azure Cloud Architect program does include a lesson on using Udacity-provided Azure credentials, so check your classroom for the same before you spend anything. If you use your own account, Azure lets new customers try it free for up to 30 days with free amounts of popular services for 12 months. Synapse dedicated SQL pools and Databricks clusters bill while they run, so pause or delete them between sessions and set a budget alert.

Practicing on your own AWS account

If you want to keep practicing after the program, a new AWS Free Tier account now gives you $100 in credits at sign-up and up to $100 more as you try key services: up to $200 over six months on the Free plan, after which the account closes unless you upgrade to a paid plan. Redshift Serverless, Glue and MWAA can use credits quickly, so set an AWS Budgets alert on day one.

Comparison: Udacity vs DataCamp vs Coursera IBM Data Engineering

If your goal is to get hired as a data engineer and you already have the prerequisites, Udacity’s AWS program gives you the most realistic portfolio of the three. If your goal is to learn the concepts as cheaply as possible, DataCamp’s Python for Data Engineering track or the IBM Data Engineering Professional Certificate on Coursera cost far less, and IBM’s is the better starting point for a true beginner. For a wider view, see our data engineering courses guide.

Where the AWS Data Engineer Associate exam fits

Many learners pair the AWS program with the AWS Certified Data Engineer Associate (DEA-C01): 65 questions in 130 minutes for $150, with a pass mark of 720 on a 100 to 1,000 scale (AWS). Here is how the exam guide’s domains line up with the Udacity courses:

The program gives you the hands-on base; a dedicated DEA practice course and timed practice tests cover the gaps. Book the exam once you score comfortably above the pass mark on questions you have not seen before.

Alumni perspective: what taking the program is actually like

Alberto’s background: a Computer Science Engineering degree, internships in data science, then a Data Engineer role at a biotechnology company. He met Airflow, Spark and Apache Cassandra at work and realized he had gaps in data modeling and processing engines, topics the Udacity program covered directly.

On the pricing reality: “At the time when I wanted to enroll, there was no valid offer or discount available. That’s the reason why I paid the full price for the course for 5 months subscription. You have access to paid services, but I don’t recommend paying such a high price. If you get a discount of 50% or even 70%, go for it: the outcome will be worth it.” (In practice: wait for one of Udacity’s recurring promotions.)

On the cloud warehouse project: “This project was groundbreaking for me because at this stage I was not afraid of building my cloud data warehouse for my projects. I was not aware of the technologies like Spark and Hadoop. It was great to know about them and the state of art technologies that should be known in the Data Science Industry.”

On time to complete: “It took me a bit longer than expected to complete this Data Engineering Nanodegree. I enrolled in October 2020, but because of work, I was not able to focus on the course until January. In finishing the Nanodegree in March, I paid for one extra month with an offer + 80€. Honestly, it took me around 2 to 3 months of focused work to complete the Nanodegree. After my experience, I think that if someone is willing to go 100% for the course then he should choose a monthly payment plan instead of a 5-month subscription. If you are studying this Nanodegree in parallel to anything else then I strongly recommend you to choose the 5-month subscription; speedrunning the projects will only make you struggle on project requirements.”

On mentorship: “I did not approach a mentor directly for advice. But I had a chance of submitting my GitHub repository and LinkedIn profile for review and I don’t think that it is something really special.”

On the capstone: “For this project I decided to create a report with agricultural data. This project covered an entire closed-loop scenario.” (That open-ended, bring-your-own-data capstone belonged to the earlier program.)

Two lessons carry over to today’s program. His pace, two to three months of focused work, matches the 66-hour estimate plus resubmissions. And his plan advice still holds: pay monthly if you can study full time, take the bundle if you are fitting this around a job. Today’s AWS program ends with an Airflow lakehouse project rather than a bring-your-own-data capstone, so plan a project of your own after you finish if you want one no one else has.

Pros

• Current stack (AWS): Iceberg, S3 Tables, Glue, Athena, Lake Formation and Airflow on MWAA, rebuilt in October 2026.
• Reviewed projects: four portfolio projects, each checked by a Udacity reviewer before it passes.
• Cloud access included (AWS): Udacity supplies AWS Cloud Labs with a fixed budget, so you do not need your own AWS account.
• Breadth of modeling: relational, document and graph databases in the first course, which most data engineering courses skip.
• Learner rating: 4.6 from 1,294 reviews on the AWS program page.
• Whole-catalog subscription: the same plan covers Udacity’s other AWS programs if you continue.

Cons

• Price: $249 a month is many times a DataCamp or Coursera subscription.
• Prerequisites: nine listed for each program; neither is a first course in Python or SQL.
• New syllabus, old reviews: the 1,294 reviews mostly predate the October 2026 rebuild of the AWS program.
• Azure program lags: it teaches Synapse and Data Factory rather than Microsoft Fabric, which DP-700 now tests.
• No streaming on the syllabus: the AWS program is batch and CDC focused; Kinesis and real-time pipelines are not listed. Udacity teaches streaming in a separate program; see our Data Streaming Nanodegree review (Kafka and Spark Structured Streaming).
• Fixed lab budget: careless clusters can use it up before the last project.
• Self-paced only: no live sessions, so you supply the discipline.

Who should take this Nanodegree

Take the AWS program (nd027) if:

• You have intermediate Python and SQL and want a data engineering portfolio on the most widely used cloud.
• Your target jobs list Redshift, S3, Glue, Athena or Airflow.
• You can study about 10 hours a week for two to four months.
• You plan to sit the AWS Certified Data Engineer Associate exam afterward.

Take the Azure program (nd0277) if:

• Your target employers run Microsoft and list Synapse, Data Factory or Databricks.
• You already understand distributed processing and want an Advanced program.
• You are willing to add Microsoft Learn’s Fabric material for DP-700.

Skip both if:

• You are new to Python or SQL; build those first.
• You only want to learn the concepts cheaply; DataCamp or Coursera cost far less.
• You want live teaching and career services; compare bootcamps instead.

A realistic schedule for the AWS program

Comparing other Udacity programs? Our Data Scientist Nanodegree review covers the analysis side, and the Cloud DevOps Engineer review the infrastructure side.

What learners report: reviews and Reddit (October 2026)

Udacity scores well overall: 4.5 on Trustpilot from 3,406 reviews and 4.7 on Course Report from 627. We read the 40 most recent Trustpilot reviews (20 September to 12 October 2026). Thirty-five gave four or five stars, mostly short notes posted after finishing a course, and several came from employer-sponsored training. The five low ratings are the useful part, because they describe what can go wrong:

• Slow or stuck project reviews: one learner had a review pending for three weeks and several support tickets open.
• Workspace problems: a browser terminal that would not install Python packages.
• Content quality on newer courses: one reviewer found mistakes that, in their view, no one had checked before release.

Udacity’s own project review timeline says reviews are often done in 48 to 72 hours but can take up to a week at busy times, and that canceling a submission to resubmit only pushes you back in the queue. Plan a week of slack per project, keep a local copy of everything you submit, and start the next lesson while you wait. These are small samples: they show what can happen, not how often it does.

On Reddit, two threads matter for this program. In May 2026 a Data Engineering with AWS learner posted on r/Udacity that project submissions kept failing with a generic “Submission Failed” message even for small files, and that support replied with template troubleshooting steps. In late 2025 a data analyst on r/dataengineering, choosing how to spend an employer training budget, asked whether the program was current, citing older comments that it was outdated and that mentors knew little beyond the syllabus. The October 2026 rebuild answers the first worry; the second is why we suggest leaning on the project rubric and AWS documentation rather than on mentor chat.

If you only need the certification and not the portfolio, 2026 pass reports on r/AWSCertifications most often used Frank Kane and Stephane Maarek’s AWS Certified Data Engineer Associate course on Udemy (4.6 from 14,063 ratings, updated October 2026, two practice exams included) plus Tutorials Dojo practice tests. That route costs far less than Udacity but has no reviewed projects and no lab account.

Frequently Asked Questions

Is the Udacity Data Engineering Nanodegree worth it in 2026?

For learners who already write Python and SQL and want a cloud data engineering portfolio, the AWS program is worth it: Udacity rebuilt it on 7 October 2026 around Redshift, Glue, Athena, Apache Iceberg and Airflow on Amazon MWAA, every project is reviewed, and Udacity supplies the AWS lab account. It is not worth it for beginners or for anyone who only wants the concepts, which DataCamp or Coursera teach for far less.

Should I take the AWS or Azure variant?

Look at ten data engineer job postings in the market you want to work in. If most list AWS services such as Redshift, S3, Glue or Athena, take Data Engineering with AWS. If most list Synapse, Data Factory or Databricks on Azure, take the Azure program. If the postings are mixed, AWS is the safer default: it holds the larger share of the cloud market (28% against Microsoft at 20% in Q2 2026, per Synergy Research) and its program is the more recently rebuilt of the two.

How long does the Udacity Data Engineering Nanodegree take?

Udacity estimates 66 hours of content for the AWS program and 56 hours for the Azure program. At 10 hours a week that is about seven and six weeks of lessons, but each of the four projects is reviewed and often needs a resubmission, so most working learners should plan on two to four months.

How much does the Udacity Data Engineering Nanodegree cost?

Udacity charges $249 a month, $846 for a four-month bundle, or $999 to buy the program outright. Udacity runs percentage-off promotions most months; on 12 October 2026 its pages advertised 40% off for new users, and our links pass the best discount we can verify to checkout. Check the final price at checkout before you pay.

What is in the Data Engineering with AWS syllabus?

Four courses: Data Modeling with AWS (PostgreSQL, MongoDB, Neo4j), Data Warehouses on AWS (Amazon Redshift), Data Lakes and Lakehouses on AWS (S3, DMS, Glue, Athena, Lake Formation, PySpark, Apache Iceberg and S3 Tables) and AWS Data Pipelines and Orchestration with Airflow (including Amazon MWAA). There are 16 lessons and four projects in total.

Do I have to pay for AWS or Azure on top of Udacity?

For the AWS program, Udacity provides the AWS account: you launch the console from the classroom, and Udacity sets a fixed budget or credits for each student’s cloud labs. Delete clusters when you finish, because a forgotten Redshift cluster can use up that budget. The Azure program’s public syllabus does not say whether Udacity supplies Azure access, so check the classroom before you use your own Azure subscription.

Is the Udacity Data Engineering Nanodegree hard?

It is not a beginner program. Udacity lists nine prerequisites for the AWS version, including intermediate Python, intermediate SQL, relational data models, command line basics and basic GitHub, and it rates the Azure version Advanced. If you cannot yet write a SQL join or a Python function comfortably, start with a Python and SQL course first.

Is this Nanodegree accredited?

No. A Nanodegree is a Udacity certificate, not an accredited degree. Udacity marks both Data Engineering programs as eligible for credit toward its own master’s degrees, which matters only if you might continue into one. For employers, the four reviewed projects matter more than the certificate.

What jobs can I get after this?

The program is aimed at data engineer roles: building warehouses, lakehouses and orchestrated pipelines. Graduates also apply for analytics engineer and ETL developer jobs. Udacity’s page shows U.S. data engineer total compensation of $115K at entry level, but that figure comes from Built In, Glassdoor and Levels.fyi and includes bonuses and stock, so treat it as a ceiling for big-tech roles rather than a typical first offer.

Does the AWS variant prep me for AWS certification?

Partly. The AWS Certified Data Engineer Associate (DEA-C01) exam weights data ingestion and transformation at 34%, data store management at 26%, data operations and support at 22%, and data security and governance at 18%. The Udacity program covers much of the first three with hands-on work, but it is not an exam course, so add a DEA practice course and practice tests before you book the $150 exam.

Does the Azure variant prep me for Microsoft certification?

Only partly. Microsoft’s current data engineering certification is the Fabric Data Engineer Associate (exam DP-700), which centers on Microsoft Fabric. The Udacity Azure program teaches Synapse Analytics, Data Factory and Azure Databricks, which overlap with the concepts but not the Fabric tooling. Use Microsoft Learn’s DP-700 path alongside it.

Can I put the Udacity projects on GitHub?

Yes, and you should: a public repository with a clear README is how most graduates show the work to employers. Write your own solution and explain your design choices in the README. Copying a former student’s repository defeats the project review and leaves you unable to answer the follow-up questions an interviewer will ask.

Can I get a refund?

Udacity’s Terms of Use give you seven days from your first purchase of a subscription or a single program to cancel for a full refund. After that, fees are not refundable, so use the first week to check the classroom and the first project brief.

How does this compare to a coding bootcamp?

Bootcamps usually cost several thousand dollars and add live cohorts and career services. Udacity costs $249 a month, $846 for four months or $999 for the program, is self-paced, and has every project reviewed. If you need structure and accountability, a bootcamp is worth comparing; if you can work alone, Udacity is far cheaper.

How much does the AWS Certified Data Engineer Associate exam cost?

The DEA-C01 exam costs $150. It has 65 questions in 130 minutes, is scored from 100 to 1,000, and you need 720 to pass. The Udacity fee does not include the exam; you book it separately with AWS.

Is the AWS Data Engineer Associate exam hard?

It is a scenario exam on Glue, Redshift, Kinesis, Athena, Lake Formation and pipeline operations. People who posted pass reports on r/AWSCertifications in 2026 typically had a year or more of hands-on AWS work, studied for about a month, and saw practice scores in the high 60s to mid 70s, close to the pass line. Plan for practice exams on top of this Nanodegree, because it does not include any.

Is there a cheaper way to prepare for the DEA exam?

Yes, if you only want the certification. Frank Kane and Stephane Maarek’s Udemy course, AWS Certified Data Engineer Associate 2026 – Hands On!, had a 4.6 rating from 14,063 ratings and two practice exams in October 2026, and 2026 passers often paired it with Tutorials Dojo practice tests. It costs far less than Udacity but has no reviewed projects and no lab account.

What does a data engineer earn?

The U.S. Bureau of Labor Statistics does not track data engineers as a separate occupation. The closest one it does track, database architects, had a median wage of $139,500 in May 2025. Udacity’s program page shows higher figures ($115K entry level to $510K experienced), but those are total-compensation numbers from Built In, Glassdoor and Levels.fyi, including stock and bonus, not typical base pay.

How long does Udacity take to review a project?

Udacity says reviews are often finished in 48 to 72 hours and can take up to a week when volume is high. Do not cancel and resubmit, because that puts you back in the queue. Once a project meets specifications you cannot resubmit it.

Final Verdict: 4.3/5

Udacity’s Data Engineering with AWS is one of the better structured ways to build a cloud data engineering portfolio in 2026. The October 2026 rebuild brought it up to date with the lakehouse tools employers now ask for, the four projects are reviewed, and Udacity supplies the AWS lab account. The Azure program is solid on Synapse, Databricks and Data Factory but has not caught up with Microsoft Fabric.

The downsides are the price and the prerequisites: you need working Python and SQL before you start, and $249 a month only makes sense if you will finish in a few months. If you have both, enroll in the AWS program. If you are not ready, see our Udacity Nanodegree review for the full program comparison.

Enroll in Data Engineering with AWS →

Related: Udacity Data Scientist Nanodegree review · Udacity AWS Cloud Architect review · Udacity AWS Solutions Architect Associate review · Udacity AWS Machine Learning Engineer review · Udacity Cloud DevOps Engineer review · AWS vs Azure · Udacity Nanodegree hub (all programs)

About the author: Josh Hutcheson edits OnlineCourseing and has reviewed online learning platforms since 2019. Connect on LinkedIn.