What is the role of the Data Engineer?
Data engineers play a vital role in any enterprise data analytics team and responsible for managing, optimizing, overseeing, and monitoring data retrieval, storage, and distribution throughout the organization
Data engineers are also responsible for finding trends in data sets and developing algorithms to help make raw data more useful to enterprises. One should have a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages. However, data engineers also need to be proficient in communication skills to work across departments to understand what business leaders want to gain from the company’s large datasets.
Data engineers are often responsible for building algorithms to help give easier access to raw data, but to do this, they need to understand the company’s or client’s objectives. It’s important to have business goals in line when working with data, especially for companies that handle large and complex datasets and databases.
Data engineers also need to understand how to optimize data retrieval and how to develop dashboards, reports, and other visualizations for stakeholders. Depending on the organization, data engineers may also be responsible for communicating data trends. Larger organizations often have multiple data analysts or scientists to help and understand data, while smaller companies might rely on a data engineer to work in both roles.
About this course
This course is perfect to design data models, build data warehouses and data lakes, automate data pipelines, and work with massive datasets. At the end of the program, you will combine your new skills by completing a capstone project. However, students should have intermediate SQL and Python programming skills.
Students will be able to learn-
- Create user-friendly relational and NoSQL data models.
- Create scalable and efficient data warehouses.
- Work efficiently with massive datasets.
- Build and interact with a cloud-based data lake.
- Automate and monitor data pipelines.
- Develop proficiency in Spark, Airflow, and AWS tools.
What you will learn from this course?
You will be able to learn to design data models, build data warehouses and data lakes, automate data pipelines, and work with massive datasets. At the end of the program, you will ready for combining your new skills by completing a capstone project.
Why should you enroll in this Course?
The data engineering field is expected to continue growing rapidly over the next several years, and there’s a huge demand for data engineers across industries. Udacity has collaborated with industry professionals to offer a world-class learning experience so you can advance your data engineering career. Also, you will get hands-on experience running data pipelines, building relational and NoSQL data models, creating databases on the cloud, and more.
Udacity provides high-quality support as you master in-demand skills that will qualify you for high-value jobs in the data engineering field and help you land a job you love. By the end of the Nano-degree program, you will have an impressive portfolio of real-world projects and valuable hands-on experience.
Course 1: Data Modeling
LESSON ONE: Introduction to Data Modeling
- Understanding the purpose of data modeling.
- Identifying the strengths and weaknesses of different types of databases and data storage techniques.
- Create a table in Postgres and Apache Cassandra.
LESSON TWO: Relational Data Models
- Understand when to use a relational database.
- Understand the difference between OLAP and OLTP databases.
- Create a normalized data table.
- Implement denormalized schemas (e.g. STAR, Snowflake).
LESSON THREE: NoSQL Data Models
- Understand when to use NoSQL databases and how they differ from relational databases.
- Select the appropriate primary key and clustering columns for a given use case.
- Create a NoSQL database in Apache Cassandran.
Course 2: Cloud Data Warehouses
LESSON ONE: Introduction to the Data Warehouses
- Understand Data Warehousing architecture.
- Run an ETL process to denormalize a database (3NF to Star).
- Create an OLAP cube from facts and dimensions.
- Compare columnar vs. row-oriented approaches.
LESSON TWO: Introduction to the Cloud with AWS
- Understand cloud computing.
- Create an AWS account and understand their services.
LESSON THREE: Implementing Data Warehouses on AWS
- Identify components of the Redshift architecture.
- Run ETL process to extract data from S3 into Redshift.
- Set up AWS infrastructure using Infrastructure as Code (IaC).
- Design an optimized table by selecting the appropriate distribution style and sorting key.
Course 3: Spark and Data Lakes
LESSON ONE: The Power of Spark
- Understand the big data ecosystem.
- Understand when to use Spark and when not to use it.
LESSON TWO: Data Wrangling with Spark
- Manipulate data with SparkSQL and Spark Dataframes.
- Use Spark for ETL purposes.
LESSON THREE: Debugging and Optimization
- Troubleshoot common errors and optimize their code using the Spark WebUI.
LESSON FOUR: Introduction to Data Lakes
- Understand the purpose and evolution of data lakes.
- Implement data lakes on Amazon S3, EMR, Athena, and Amazon Glue.
- Use Spark to run ELT processes and analytics on data of diverse sources, structures, and vintages.
- Understand the components and issues of data lakes.
Course 4: Automate Data Pipelines
LESSON ONE: Data Pipelines
- Create data pipelines with Apache Airflow.
LESSON TWO: Data Quality
- Track data lineage and set up data pipeline schedules.
- Partition data to optimize pipelines.
LESSON THREE: Production Data Pipelines
- Build reusable and maintainable pipelines and build your own Apache Airflow plugins.
- Implement subDAGs.
- Set up task boundaries and Monitor data pipelines.
What jobs will this program prepare you for?
This program is designed to prepare people to become data engineers. This includes job titles such as analytics engineers, big data engineers, data platform engineers, and others. Data engineering skills are also helpful for adjacent roles, such as data analysts, data scientists, machine learning engineers, or software engineers.
This Nano-degree Programs Includes:
- Experienced Project reviews.
- Technical mentor support.
- Personal career services.
How is the Nano-degree program structured?
The Data Analyst Nano-degree program is comprised of content and curriculum to support five (5) projects. They also estimated that students can complete the program in four (4) months working 10 hours per week. Each project will be reviewed by the Udacity reviewer network and platform. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
The Data Engineer Nano-degree program is designed for students with intermediate Python and SQL skills. In order to successfully complete the program, students should be comfortable with the following programming concepts:
- 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
- Joins, Aggregations, subqueries, and table definition and manipulation (Create, Update, Insert, Alter)
If you do not meet the requirements to enroll, What should you do?
Udacity has a number of Nano-degree programs and free courses that can help you prepare: Introduction to Python Programming SQL for Data Analysis.
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- Clarity on explanation, all the tasks and demos.
- Projects are challenging and useful.
- The course content explains both the theory and practicalities of each technology in an easy-to-understand manner.
- The lessons are well-organized and highly informative.
- Real world projects and feedback is extremely helpful.
- Expectation to have a bit improvement in instructor’s presentation skill.
- Need to be familiar with basic programming libraries.
Specification: Online Data Engineer course