Data Engineering Training Program At Skillfloor

Name: Certified Data Engineering Professional
Brand: Skillfloor
SKU: 40
Rating: 4.9 (480 reviews)

Overview

The Data Engineering Course offered by SkillFloor is a comprehensive program designed to equip aspiring professionals with the skills and knowledge required to excel in the field of big data engineering. Through a blend of theoretical concepts and hands-on practical exercises, this online course delves deep into the intricacies of data engineering, covering topics such as data ingestion, storage, processing, and analysis. Participants will learn to harness the power of cutting-edge technologies and tools commonly used in the industry, ensuring they are well-prepared to tackle real-world challenges as data engineers. With a focus on practical applications and industry-relevant projects, SkillFloor's Data Engineering Course stands out as a top choice for individuals seeking to embark on a rewarding career path in this field. Explore the course today and take the first step towards becoming a proficient data engineer.

SkillFloor's Data Engineer Course is a premier online offering tailored for individuals aspiring to specialize in data engineering. This comprehensive program covers all aspects of data management and processing, providing participants with the necessary skills to thrive in the current informative environment. By enrolling in this course, students gain access to a wealth of resources and hands-on learning opportunities, allowing them to master key concepts such as data warehousing, ETL (Extract, Transform, Load) processes, and distributed computing. With a curriculum designed by industry experts and instructors with extensive experience, SkillFloor's Big Data Engineer Course is an ideal choice for anyone looking to enhance their expertise and advance their career in the field of data engineering.

~~₹60,000~~

₹14,900

Course Duration

4

Internship

1-month

Live Training

Yes

Career Assistance

Yes

Enrol Now

Course Highlights

Introduction to Data Engineering: An overview of the data engineering field, including its importance and how it fits within the broader context of data analysis and data science.
Data Modeling Concepts: Basic principles of data modelling, including the creation and management of databases, schema design, and normalization processes.
SQL and NoSQL Databases: Instruction on both traditional SQL databases for structured data and NoSQL databases for unstructured data, emphasizing when and how to use each.
ETL Processes: Understanding of Extract, Transform, and Load (ETL) processes, including how to efficiently gather data from multiple sources, clean it, and prepare it for analysis.
Big Data Technologies: An introduction to the tools and technologies used to process and analyze big data, such as Hadoop and Spark.
Cloud Data Services: Exploration of cloud-based data engineering services offered by platforms like AWS, Google Cloud, and Microsoft Azure, and how to leverage them for scalable data solutions.
Data Pipelines and Workflows: Techniques for building and managing automated data pipelines that ensure the smooth flow of data from source to destination, including workflow orchestration tools.
Data Security and Compliance: Fundamental concepts in securing data and ensuring compliance with legal and regulatory requirements, such as GDPR.
Real-world Projects: Hands-on projects that mimic real-world data engineering problems, allowing learners to apply the concepts and tools they have learned.
Preparation for Certification: Guidance and resources to help participants prepare for and complete the certification exam, affirming their knowledge and skills in data engineering.

Certification

Getting the Certified Data Engineering Associate certificate means you're really good at dealing with data. It's like a badge saying, "Hey, I know how to design, build, and take care of systems that handle lots of information.People who earn this certificate learn important things like managing databases, organizing data, and ensuring big sets of information are processed and analyzed well. They also get familiar with using cloud-based solutions for data.

This certificate is a big deal because it proves that someone is committed to staying up-to-date with the latest in data engineering. When someone completes this program, it means they know the basics of data engineering really well. This makes them valuable in the world of technology that relies on data.So, having the Certified Data Engineering Associate badge is like saying, "I know my stuff when it comes to handling data in today's tech world." It's a recognized mark that shows someone is skilled in the important principles of data engineering.

Top 10 reasons

The Basics: Understand key concepts like data modeling, ETL processes, and managing databases to build a strong foundation.
Hands-on Learning: Apply what you learn in real-world projects, ensuring you can use your knowledge in a job setting.
Tools of the Trade: Get familiar with the latest tools and technologies used in data engineering, including popular platforms and programming languages.
Data Warehousing: Learn how to design and implement efficient data storage solutions.
Big Data Integration: Master handling large volumes of data in modern business environments with the integration of big data technologies.
Data Quality and Governance: Discover the importance of maintaining clean and reliable data and learn strategies for doing so.
Cloud Computing: Explore how cloud platforms can be used for scalable and flexible data processing.
Group Projects: Work with your peers on real-world projects to improve teamwork and problem-solving skills.
Certification Exam Prep: Get ready for the certification exam to showcase your data engineering skills.
Career Support: Receive guidance, industry insights, and networking opportunities to kickstart or advance your data engineering career.

Syllabus

Overview of Data Engineering: Roles and Responsibilities

Data Engineering vs. Data Science: Key Differences

Understanding Data Pipelines: ETL and ELT Processes

Data Types and Formats: Structured, Semi-structured, and Unstructured

Overview of Data Warehousing, Data Lakes, and Data Marts

Introduction to Cloud-based Data Engineering: AWS, Azure, and Google Cloud Platforms

Introduction to Data Modeling: ER Diagrams, Normalization, and Relationships

Relational Database Management Systems (RDBMS) Concepts

SQL for Data Engineers: Advanced Queries and Optimizations

Data Warehousing Concepts: Star Schema, Snowflake Schema, Fact and Dimension Tables

NoSQL Databases: Key-value, Document, Columnar, and Graph Databases

Best Practices for Database Design and Optimization

Understanding ETL (Extract, Transform, Load) Processes and Tools

Introduction to Apache Airflow for Workflow Automation and Orchestration

Building Data Pipelines: Batch Processing vs. Stream Processing

ETL Tools Overview: Apache NiFi, Talend, and Informatica

Introduction to Real-time Data Processing: Kafka and Spark Streaming

Monitoring and Debugging Data Pipelines

Introduction to Data Storage Solutions: Block, Object, and File Storage

Cloud Storage Solutions: AWS S3, Azure Blob Storage, and Google Cloud Storage

Data Warehousing on the Cloud: AWS Redshift, Google BigQuery, Azure Synapse Analytics

Introduction to Distributed Computing with Hadoop and Spark

Setting Up Cloud Infrastructure for Data Engineering

Cost Management and Scalability in Cloud Environments

Introduction to Apache Hadoop: HDFS, MapReduce, and YARN

Apache Spark Overview: Core Concepts and RDDs

Spark SQL and DataFrame API for Big Data Processing

Introduction to Spark MLlib for Machine Learning Pipelines

Batch Processing with Spark vs. Real-time Processing with Spark Streaming

Hands-on with Big Data Processing on the Cloud (AWS EMR, Databricks)

Data Privacy and Security in Data Engineering: Compliance (GDPR, HIPAA, etc.)

Data Governance: Metadata Management and Data Lineage

Data Quality Management: Data Cleansing and Validation Techniques

Encryption and Security Best Practices for Data Pipelines

Introduction to Data Catalogs and Discovery Tools

Best Practices in Version Control, Documentation, and Collaboration for Data Engineers

FAQ

A certified data engineering associate is someone who has completed a recognized certification program in data engineering, demonstrating their ability to design, build, and maintain data pipelines and infrastructure.

Certification in data engineering validates your skills, enhances your credibility, and increases your job prospects in fields where managing and processing data efficiently is crucial.

To get certified, you typically need to complete specific training courses, gain practical experience with data engineering tools and technologies, and pass an exam that tests your knowledge of data engineering concepts.

Skills include understanding of databases, proficiency in programming languages like SQL and Python, knowledge of data warehousing and ETL processes, and experience with cloud platforms like AWS or Azure.

With certification, you can pursue roles such as data engineer, ETL developer, database administrator, or cloud engineer in various industries.

It varies but typically takes several months to a year of study and practice to get certified.

It can lead to better job prospects, higher salaries, and opportunities for career advancement in the field of data engineering.

Certified Data Engineering Professional

Overview