This comprehensive data engineering course equips participants with the essential skills and knowledge to design, build, and manage data infrastructur...Read more

Helix Tech

Helix Tech

Helix Tech

  • Hands on Training
  • Flexible Timings
  • Industry Based Training
  • Experienced Experts
  • Affordable Fees
  • Placement Opportunities

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Key Highlights

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Highly Qualified Experts

Flexible Batch Timings

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Course Highlights


In the digital age, data has become a strategic asset, driving decision-making and innovation across industries. The role of a skilled data engineer has never been more crucial in ensuring the seamless flow of data from its raw state to valuable insights. Our Data Engineering Course is designed to equip you with the knowledge, tools, and hands-on experience needed to excel in this dynamic field.The Data Engineering Course is a deep dive into the world of data infrastructure, ETL processes, and data management. It is meticulously crafted to cater to both beginners who want to enter the field and experienced professionals aiming to enhance their skill set. Explore the Extract, Transform, Load (ETL) paradigm, mastering the techniques to extract data from diverse sources, transform it into the desired format, and load it into storage or analytical systems.


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At Helix Tech IT Solutions Inc, we believe that the success of a company is rooted in the talents and aspirations of its employees. We are committed to creating a positive impact on the tech industry by bridging the gap between top tech talent and companies while empowering job seekers to achieve their career aspirations. We believe in creating a thriving ecosystem where technology and innovation comes to life.


Course Curriculum

  • Understanding the role of a Data Engineer and their contribution to the data ecosystem.
  • Exploring the data engineering lifecycle and its significance in data-driven decision-making.
  • Relational databases and NoSQL databases and their use cases.
  • Introduction to distributed data storage systems like Hadoop HDFS and Amazon S3.
  • Relational vs. NoSQL data modeling approaches.
  • Overview of workflow orchestration tools like Apache Airflow and Luigi.
  • Workflow management tools (Apache Airflow)
  • Pipeline monitoring and management
  • Data validation techniques
  • Handling missing and erroneous data
  • Data quality frameworks
  • Data encryption
  • Access control and authorization
  • Compliance with data regulations
  • Horizontal vs. vertical scaling
  • Performance optimization strategies
  • Load balancing
  • Hadoop ecosystem
  • Spark and distributed computing
  • NoSQL databases
  • Connecting to external data sources
  • Data integration patterns

Professional Certificate

Beginner level

No previous experience necessary

Course Session

25 Days 1 Hours per day

Flexible schedule

Learn at your own pace

Course Key Features

Gain expertise in creating effective data models that ensure data consistency, accuracy

Hone your problem-solving abilities by tackling real-world data engineering challenges, applying critical thinking to design optimal solutions

Skills Covered
  • Data Warehousing
  • SQL and NoSQL Databases
  • Cloud Platforms
Job Roles
  • Data Warehouse Developer
  • Cloud Data Engineer
  • Streaming Data Engineer


The training is designed to describe the main goals and objectives of the training, such as enhancing skills, improving knowledge, etc.
This training is intended for specify the target audience, such as beginners, professionals, specific job roles, etc.
List any required prerequisites, such as prior knowledge, experience, or skills that participants should have before taking the training.

Interview Question

The ETL (Extract, Transform, Load) process involves extracting data from various sources, transforming it into a consistent format, and loading it into a data warehouse or database for analysis. ETL ensures that data is clean, accurate, and ready for analysis.
A data warehouse is a centralized repository that stores structured and organized data from various sources. It supports reporting, analytics, and business intelligence by providing a consistent and historical view of data.
Schema design involves defining the structure of data storage in databases or data warehouses. It impacts data organization, querying efficiency, and ensures compatibility with downstream applications.

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