Brochure
Enquire- Define data modeling and learn how to structure data effectively to create knowledge, including polyglot persistence and when to combine relational, document, and graph databases for different needs
Intermediate
Online
66 Hours
Quick facts
| particular | details | |
|---|---|---|
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Medium of instructions
English
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Mode of learning
Self study, Virtual Classroom
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Mode of Delivery
Video and Text Based
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Course overview
Today's world is rapidly transferring into digital space, where one will find large amounts of data being stored by all types of businesses and organisations irrespective of their size. It has become easy to create smart storage space for this data without spending much only because of the technological advancements.
It has become extremely important for companies to systematically store the data and use it wisely. Here the need of a data engineer comes. To meet up to the expectations and stand out to be chosen by a good company one has to sharpen their skills, so here comes ‘Nano programme in Data engineering’ by ‘Udacity’ in collaboration with ‘Insight’. A wonderful platform where one can build their skills and beat the competition prevailing in the industry.
The highlights
- Online classroom programme
- Flexible learning program
- Certification from Udacity
Program offerings
- Real world projects
- Project reviews
- Project feedback
- Content co-created with insight
Course and certificate fees
The fees for the course Data Engineering is -
| Fees components | Amount |
| Subscription · Bundle | Rs. 17,911 / month |
| Subscription · Monthly | Rs. 6,999 / month |
| Individual Course | Rs. 39,999 / one-time payment |
certificate availability
Yes
certificate providing authority
Udacity
Eligibility criteria
Python
Sql
Experience
To take the Data Engineering certification course, the candidate should have introductory programming or additional real-world software development experience. Including:
- Procedures, objects, modules, and libraries
- Problem solving; Algorithms and data structures
- Lists, tuples, and dictionaries; Conditions, loops
- Troubleshooting and debugging; Research & documentation
- Strings, numbers, and variables; statements, operators, and expressions
What you will learn
Knowledge of aws technology
After completion of this course in Data Engineering, the candidates will gain insight on the following areas.
- By taking the Data Engineering certification training course, candidates will be able to work appropriately with utmost accuracy with massive datasets
- Candidates will master tools such as AWS, Airflow and Spark.
- Understand the process of creating NoSQL data and relational models
- The process of monitoring and automating data pipelines will be learned
- The Data Engineering certification course will also help in the creation of efficient and scalable data warehouses
- Get an in-depth understanding of interacting and building a cloud-based data lake
Who it is for
The Nano programme in Data engineering is meant for professionals who have prior immediate knowledge of SQL and Python programming skills. The candidate should have introductory programming or additional real-world software development experience.soft
Admission details
The following is the admission process for the Nano programme in Data engineering:
Step 1: Visit the URL for the programme https://www.udacity.com/course/data-engineer-nanodegree--nd027
Step 2: On the bottom of the page there are two options of payment available choose the one that the candidate desires by clicking the ‘Enrol Now’ button.
Step3: If the candidate is new click check out button, or sign in if already have an account
Step4: The candidate can then make the payment via debit card, credit or net banking.
The syllabus
Module 1 - Data Modeling with AWS
Introduction to Data Modeling
Data Modeling for Relational Databases
- Apply relational database concepts and techniques, including ACID properties, normalization, star schemas, and JSONB columns, to organize and query structured and unstructured data S
Data Modeling for Document Stores and NoSQL
- Explore NoSQL data modeling, covering document databases, storage formats, OLTP vs. OLAP, hot vs. cold data, and how models connect logical design to physical storage
Data Modeling for Graph Databases
- Explore graph databases, emphasizing relationships as first-class data using nodes and edges, including NoSQL concepts, Cypher/GQL querying, modeling, and why graphs excel at connected data
Data Modeling for ACME
- Apply your data modeling skills end-to-end: design a normalized PostgreSQL schema, flexible MongoDB collections, and a Neo4j recommendation graph for a growing e-commerce company
Module 2 - Data Warehouses on AWS
Introduction to Data Warehousing
- Explore how data warehouses unify scattered operational systems into a single source of truth, and compare OLTP vs. OLAP, dimensional modeling basics, and star and snowflake schemas.
Dimensional Modeling for Analytics
- Define fact grain, build fact and dimension tables with surrogate keys, and write Redshift DDL that encodes distribution styles, sort keys, and compression for performance.
Extracting and Transforming Source Data
- Build ETL pipelines in Python that pull data from PostgreSQL, Cassandra, and Neo4j, then clean, conform, and derive dimensions for a consistent warehouse-ready dataset.
Loading, Optimization, and Validation in Redshift
- Stage data through S3, load it with the COPY command, optimize tables for parallel query performance, create materialized views, and run data quality validation checks.
Build a Multi-Source E-commerce Analytics Warehouse in Redshift
- Design a star schema and end-to-end ETL pipeline that integrates e-commerce data from three source systems into an optimized, validated Redshift warehouse.
Module 3 - Data Lakes and Lakehouses on AWS
Introduction to Data Lakes and Lakehouses
- Differentiate between the data warehouse, data lake, and data lakehouse paradigms for structured and unstructured data, and learn medallion architecture principles
Building Data Lakes on AWS
- Set up AWS S3 as a data lake foundation, ingest PostgreSQL CDC via DMS, catalog with Glue, query with Athena, and set up Lake Formation governance
Processing Data in the Lake with Spark
- Transform bronze (raw) data to silver (clean/enriched) data with PySpark, then aggregate to gold KPIs (business metrics). Optimize your Spark queries with partitioning and caching.
Implementing Lakehouse Tables
- Create Iceberg tables and S3 Tables with AWS Glue, implement ACID MERGE for CDC upserts, and query historical snapshots (time travel)
Exastore Data Lakehouse on AWS
- Implement a full lakehouse solution with CDC pipeline ingestion, batch data ingestion, Spark ETL processing, medallion architecture, and lakehouse tables
Module 4 - AWS Data Pipelines and Orchestration with Airflow
Introduction to Data Pipelines and Airflow
- Discover how Apache Airflow orchestrates data pipelines as code. Author DAGs and tasks, pass data with XCom, configure runtime parameters, apply Jinja templating, and build branching dependencies.
Data Lineage and Orchestration
- Orchestrate production pipelines with schedules, data intervals, and catchup. Apply single-responsibility design, debug with flatfile snapshots, trigger DAGs on asset events, and map tasks dynamically
Orchestrating Warehouse Workflows with Amazon Redshift
- Build end-to-end ETL and ELT pipelines that move data through S3 into Redshift. Use SQL operators, Jinja template inheritance, and data constraint checks, then deploy to production with Amazon MWAA.
Orchestrating Lakehouse Workflows with AWS Glue and Athena
- Build a lakehouse on AWS with S3, Glue, Iceberg, and Athena. Automate ingestion, handle schema evolution with crawlers, and promote data through bronze, silver, and gold layers.
AWS Data Lakehouse Pipeline for Sparkify
- Design an event-driven lakehouse with Airflow, S3, Glue, Iceberg, and Athena. Build three asset-triggered DAGs that ingest, transform, and promote data through raw, transaction, and analytics layers.
Scholarship Details
Participants willing to pursue Data Engineering certification courses can check out scholarship plans by clicking on the official URL https://udacity.zendesk.com/hc/en-us/categories/360002443511-Scholarship-Programs. After this click on Notify Me and send the essential details. Once the details are mailed, the candidate will start receiving notifications for scholarships that are uploaded on the webpage.
How it helps
By doing this Nano programme in Data engineering the candidate will receive personal career coaching services with technical mentor support. Our classroom experience has real world projects to build your skills along with custom study plans to motivate the candidates. Since the Data Engineering certification course has flexible learning it will help the candidate to learn at their own pace. The Data Engineering certification training course is available with two different payment options which is helpful to the candidate to pay their fees as their own availability.
After completion of the course, we have personal assistance to help the candidate for job search. The candidate will get access to the Data Engineering certification training course classroom immediately after they enrol for the programme. The programme is tailored specifically for the students so that they can complete it as per their time.
FAQs
What experience should the candidate have in order to enrol for this programme?
The candidate should have prior immediate knowledge of SQL and Python programming skills.
Is there instalment available for the payment of fees?
Yes, there is instalment availability by paying the fees per month. Candidates can opt for ‘pay as you go’ if they need the flexibility to learn at their own pace. Even if the candidate opts for 5 months access, they can switch to the monthly price afterwards if more time is required.
Is there availability of faculty to solve the doubts?
Yes, we have the best learning faculty available to solve all the subpages doubts of the candidates and help them out with all their problems regarding the chapters and topics.
How will my projects be reviewed online?
Since the course is online, we have come up with our best way possible to make it easy for the candidates to learn. So, the feedback on the candidate`s projects will be personalized with unlimited submissions and feedback loops. They will be provided with practical tips along with best industry practices. They will also receive additional suggested resources from the technical mentors to improve.
When will the course start from?
The course will start just after the candidate pays their fees. They will get access to their programme classrooms.
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