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.

    Kafka Training Courses

    Instructor-led live online Kafka training courses to help you gain expertise in Kafka API, architecture, configuration, installation, integration with Hadoop, Spark, Storm.
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    Data Engineer Fundamentals Courses

    Learn how to code like a data engineer and build up the foundation skills for a data engineering. 
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    Azure Data and AI Courses

    Web Age's Azure Data Engineering training courses teach the skills needed to perform Big Data Engineering on Microsoft Cloud Services.  Through hands-on experience with Azure data ...
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    AWS Data Engineering Courses

    Our AWS Data Engineering training prepares you for all aspects of hosting big data and performing distributed processing on the AWS platform
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    Data Engineer Courses

    Data Literacy and Tools Overview

    Course ID: TP3152
    Delivery: On-Site or Instructor-led Virtual
    This course will provide participants with an introduction to key terms and important concepts related to data literacy.  Additionally, it will provide a brief overview of the core ...
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    Data Engineering, ETL, and DataOps

    Course ID: WA3385
    Delivery: On-Site or Instructor-led Virtual
    This course provides a comprehensive introduction to key concepts and tools in the field of data engineering, ETL and DataOps.
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    Introduction to Talend

    Course ID: TP2700
    Delivery: On-Site or Instructor-led Virtual
    This 3 day course provides an introduction to Talend.
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    API Management Fundamentals for Architects

    Course ID: WA2895
    Delivery: On-Site or Instructor-led Virtual
    Does your organization want to expose its internal system APIs for external or public consumption in a completely secure and controlled way? With this API management course designed ...
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    Cloud Data Engineering with NiFi on AWS or GCP

    Course ID: TP2913
    Delivery: On-Site or Instructor-led Virtual
    This 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 ...
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    Data Engineering Bootcamp Training using Python and PySpark

    Course ID: WA3020
    Delivery: On-Site or Instructor-led Virtual
    This hands-on Data Engineering Bootcamp teaches attendees the foundations of data engineering using Python and Spark SQL. Students learn how to build production-ready data-driven solutions ...
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    The Top 10 Essential Data Engineer Skills for 2022

    data engineer skills for 2022

    In 2022 the number of jobs in the Data Science domain are continuing to rise and Data Engineering Skills are taking precedence.

    So if you want to know what data engineer skills are required and make sense for a data engineer in 2022, then you’re in the right place.

    Keep Reading!



    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 Training 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. We’re proudly distinguished by these clients, partners and awards.

    aws training   Microsoft Silver Partner   ISACA logo   
    open group Scrum Alliance Registered   profit100

    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.
    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 .

    Data Engineering & Data Analytics Upskilling Trends


    Complimentary White Paper

    Everything you NEED to know in 10 minutes!


    Frequently Asked 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 Engineering 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!

    Click here to see our Guaranteed to Run Virtual Online Class Schedules