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Data Engineering Courses

We offer proven Data Engineering Courses regularly
delivered to our worldwide Fortune 500 clients

 WATCH NOW!  Data Engineering with Python and PySpark

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Why Take Data Engineering Courses with Web Age?

Our Data engineering courses will teach you to organize, analyze and interpret your many sources of big data and information.

The data engineering courses we offer are comprehensive and provide your teams the skills they need to discover insights required for analyzing and tackling critical factors such as risk, performance, quality, forecasting, estimating, simulation, business process improvement, and much more.

Get the Data Engineering Courses you need to stay ahead with our expert-led Data Engineer Training courses.

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Data Engineering Upskilling Program

DATA ENGINEERING COURSE
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Web Age Solutions Live Online Data Engineering Courses    Web Age Solutions Onsite Data Engineering Courses   Web Age Solutions Classroom Data Engineering Courses

data engineering coursesThe Top 10 Essential Data Engineer Skills
for 2021!

You NEED to keep your certified data engineer
courses, training, and skills up to date by embracing the
new technology tools available.

What are those skills?

Keep Reading…

Data Engineering Courses – Fundamentals

 Data Engineering Course Title Course ID Course Duration

Data Engineering Bootcamp Training

A data engineer conceives, builds and maintains the data infrastructure that holds your enterprise’s advanced analytics capacities together.

Learn about the world of data engineering.

This five  day Data Engineering course is supplemented by hands-on labs that help attendees reinforce their theoretical knowledge of the learned material.

 

WA3020 5 Days

Workflow Management with apache airflow

Apache Airflow is a configuration-as-code OSS solution for workflow automation that is positioned as a replacement of cron-like scheduling systems.

Written in Python, Airflow enables developers to programmatically author, schedule for execution, and monitor highly configurable complex workflows.

 

WA3001 2 Days

Data Engineer Training with Python

Data engineering is a software engineering practice with focus on design, development, and the productionizing of data processing systems.  It includes all the practical aspects of data acquisition, transfer, transformation, and storage on-prem or in the cloud.
This intensive hands-on data engineering course teaches the students how to apply Python to the practical aspects of data engineering and introduces the students to the popular Python libraries used in the field, including NumPy, pandas, Matplotlib, scikit-learn, and Apache Spark.

 

WA2905 3 Days

Data Engineering Course for Managers

Data Engineering Course for Managers.

 

WA2922 1/2 Day

API Management Fundamentals for Architects Training

API Management Fundamentals for Architects.
Audience: Developers, Software Engineers, Data Scientists, and IT Architects

 

WA2895 2 Days

Data Engineering with PySpark

Audience:  Data Warehouse and Data Lake Specialists, Software Developers

Prerequisites: General background in programming and/or data processing; ability to learn a new language (Python) by doing stepwise exercises

 

WA2950 3 Days

Intermediate Data Engineering Course with Python

This is an intermediate data engineering course.

WA3032 2 Days

Data Science and Data Engineering Course for Architects

This intensive data engineering  course covers the theoretical and practical aspects of applying the principles and methods of Data Science and Data Engineering in practice.

The students are introduced to the relevant concepts, terminology, theory, and tools used in the field.

This data engineering course is complemented by a variety of hands-on exercises to help the attendees reinforce their theoretical knowledge of the material being studied.

WA3057 4 Days

Data Literacy and Tools Overview

This Data Literacy course will provide facilitators with an introduction to key terms and important concepts related to data literacy.

Additionally, it will provide a brief overview of the core functionality available in Tableau and Power BI.

TP3152 1 Day

Azure Data Engineering Courses

 Course Title Course ID Course Duration

Migrate SQL and NoSQL Workloads to Azure  (Bundle)

This Azure Data Engineer course provides the knowledge and skills to migrate SQL and NoSQL data workloads to Azure.

The students will learn the objectives of data platform modernization and how it is suitable for given business requirements. They will also learn each stage of the data platform modernization process and define what tasks are involved at each stage, such as the assessment and planning phase.

Students will also learn the available migration tools and how they are suitable for each stage of the data migration process. The student will learn how to migrate to the three target platforms for SQL based workloads; Azure Virtual Machines, Azure SQL Databases and Azure SQL Database Managed Instances . The student will learn the benefits and limitations of each target platform and how they can be used to fulfill both business and technical requirements for modern SQL  workloads. The student will explore the changes that may need to be made to existing SQL based applications, so that they can make best use of modern data platforms in Azure.

The students will also learn what is Cosmos DB and how you can migrate MongoDB and Cassandra workloads to Cosmos DB.

TP2882 3 Days

Migrate Data Workloads to Azure  (Bundle)

This Azure Data Engineering course provides the knowledge and skills to migrate SQL, NoSQL and Open Source data workloads to Azure.

The students will learn the objectives of data platform modernization and how it is suitable for given business requirements. They will also learn each stage of the data platform modernization process and define what tasks are involved at each stage, such as the assessment and planning phase. Students will also learn the available migration tools and how they are suitable for each stage of the data migration process. The student will learn how to migrate to the three target platforms for SQL based workloads; Azure Virtual Machines, Azure SQL Databases and Azure SQL Database Managed Instances . The student will learn the benefits and limitations of each target platform and how they can be used to fulfil both business and technical requirements for modern SQL  workloads. The student will explore the changes that may need to be made to existing SQL based applications, so that they can make best use of modern data platforms in Azure.

The students will  learn what is Cosmos DB and how you can migrate MongoDB and Cassandra workloads to Cosmos DB.

The students will learn what is required to migrate MySQL and PostgreSQL workloads to Azure SQL Database.

 

TP2883 4 Days

AWS Data Engineering Courses

 Course Title Course ID Course Duration

Cloud Data Engineer Training with NiFi on AWS

This Cloud Data Engineer course is intended for students looking to learn data processing on the cloud with Apache NiFi – a visually programmed software tool that automates the movement and transformation of data between systems. Course material will cover data engineering theory and practical development advice.

 

TP2913 3 Days

 

 

How to Build Your Data Science Team

Data engineers utilize the various stages in a pipeline from acquisition and transport, to storage, processing and servicing continually improving their methods and practices. Today’s Data Engineer must become proficient at programming, learn automation and scripting, understand may different data stores, master data processing techniques, efficiently schedule workflows, know the ever changing cloud landscape, and keep up with trends.

This data engineer training video will delve into today’s best tools and techniques that great data scientists utilize to efficiently and effectively understand outcomes from their datasets, and capture, transform and shape their data stores.

Related Data Engineering Course:

Data Engineering Bootcamp (Using Python and PySpark) 

Proven Results in our Data Engineering Courses

For over 20 years, we have trained thousands of developers at some of the country’s largest tech companies – including many Fortune 500 companies. Here are a few of the clients we have delivered Data Engineering Courses to:

Booz Allen Hamilton Data Engineering Courses     Liberty Mutual Data Engineering Courses     FedEx Ground Data Engineering Courses     Fidelity Investments Data Engineering Courses     Lockheed Martin Data Engineering Courses    Data Engineering Courses

Here are some reviews from past students who completed our Data Engineering Courses:

“I was SO impressed with the level of knowledge that the instructor has.  I will definitely consider classes from Web Age in the future!  The instructor gave us many real-life examples and provided links to further learning .”

“This was a great course. I loved the blend of Python Concepts Plus Just enough Data science to be productive”

“Instructor was very thorough, yet practical. He was a great communicator and explained everything in layman’s terms.”

“Great tutorials! I will go back to these”

“This course is excellent. It gave me an overview of data science and a good understanding. It put me in the right direction of data analysis in my work.”

Featured Data Engineer Training Article

What is Data Engineering?

Data engineering is a software engineering practice with a focus on design, development, and productionizing of data processing systems.

Data Engineering Courses

Data Engineering Courses

Frequently Asked Data Engineer Course Questions

What is a Data Engineer?

A data engineer conceives, builds and maintains the data infrastructure that holds your enterprise’s advanced analytics capacities together.

A data engineer is responsible for building and maintaining the data architecture of a data science project. Data Engineers are responsible for the creation and maintenance of analytics infrastructure that enables almost every other function in the data world. They are responsible for the development, construction, maintenance and testing of architectures, such as databases and large-scale processing systems. As part of this, Data Engineers are also responsible for the creation of data set processes used in modeling, mining, acquisition, and verification.

What is the Data Engineer Role?

  • Data engineers are software engineers who have the primary focus on dealing with data engineering tasks
  • They work closely with System Administrators, Data Analysis, and Data Scientists to prepare the data and make it consumable in subsequent advanced data processing scenarios
  • Most of these activities fall under the category of ETL (Extract, Transform and Load) processes Practitioners in this field deal with such aspects of data processing as processing efficiency, scalable computation, system interoperability, and security
  • Data engineers have knowledge of the appropriate data storage systems (RDBMS or NoSQL types) used by organizations they work for and their external interfaces
  • Typically, data engineers have to understand the database and data schemas as well as the APIs to access the stored data  Depending on the task at hand, internal standards, etc., they may be required to use a variety of programming languages, including Java, Scala, Python, C#, C, etc

What is the difference between a Data Scientist and a Data Engineer?

It is important to know the distinction between these 2 roles.

While there is frequent collaboration between data scientists and data engineers, they’re different positions that prioritize different skill sets. Data scientists focus on advanced statistics and mathematical analysis of the data that’s generated and stored, all in the interest of identifying trends and solving business needs or industry questions. But they can’t do their job without a team of data engineers who have advanced programming skills (Java, Scala, Python) and an understanding of distributed systems and data pipelines.

Broadly speaking, a data scientist builds models using a combination of statistics, mathematics, machine learning and domain based knowledge. He/she has to code and build these models using the same tools/languages and framework that the organization supports.

A data engineer on the other hand has to build and maintain data structures and architectures for data ingestion, processing, and deployment for large-scale data-intensive applications. To build a pipeline for data collection and storage, to funnel the data to the data scientists, to put the model into production – these are just some of the tasks a data engineer has to perform.

Data scientists and data engineers need to work together for any large scale data science project to succeed,

What are the different roles in Data Engineering?

Data Engineer:  A data engineer needs to have knowledge of database tools, languages like Python and Java, distributed systems like Hadoop, among other things. It’s a combination of tasks into one single role.

Data Architect: A data architect lays down the foundation for data management systems to ingest, integrate and maintain all the data sources. This role requires knowledge of tools like SQL, XML, Hive, Pig, Spark, etc.

Database Administrator: As the name suggests, a person working in this role requires extensive knowledge of databases. Responsibilities entail ensuring the databases are available to all the required users, is maintained properly and functions without any hiccups when new features are added.

What are the core Data Engineering skills?

What is the future for Data Engineering?

The data engineering field is expected to continue growing rapidly over the next several years, and there’s huge demand for data engineers across industries.

The global Big Data and data engineering services market is expected to grow at a CAGR of 31.3 percent by 2025.

What is Data Wrangling (Munging)?

Data  wrangling  (a.k.a.  munging)  is  the  process  of  organized  data transformation  from  one  data  format  (or  structure)  into  another

Normally,  it  is  part  of  a  data  processing  workflow  the  output  of  which  is consumed  downstream,  normally,  by  systems  with  focus  on  data analytics  /  machine  learning

Usually,  it  is  part  of  automated  ETL  (Extract,  Transform,  and  Load) activities

Specific  activities  include: Data  cleansing,  removing  data  outliers,  repairing  data  (by  plugging  in some  surrogate  data  in  place  of  the  missing  values),  data  enhancement /  augmenting,  aggregation,  sorting,  filtering,  and  normalizing

Can I take Data Engineering Courses online?

Yes! We know your busy work schedule may prevent you from getting to one of our classrooms which is why we offer convenient Data Engineering Courses online to meet your needs wherever you want. We offer our Data Engineering courses as public Data Engineering Courses or dedicated Data Engineering courses. Ask us about taking a Data Engineer course online!

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US Inquiries / 1.877.517.6540
Canadian Inquiries / 1.877.812.8887