Data Engineering Certification Course with Placement

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Become a master in Data Engineering at 3RI Technologies.

Key Features

Course Duration : 5 Months

Real-Time Projects : 2

Online Live Training

EMI Option Available

Certification & Job Assistance

24 x 7 Lifetime Support

Our Industry Expert Trainer

We are a team of 10+ Years of Industry Experienced Trainers, who conduct the training with real-time scenarios.
The Global Certified Trainers are Excellent in knowledge and highly professionals.
The Trainers follow the Project-Based Learning Method in the Interactive sessions.

Data Engineer Training Overview

Introduction to Data Engineering Course

Introduction:

Data Engineering is becoming increasingly important in today’s business environment as a result of the exponential growth of data and the need to derive meaningful insights from it. As a result of the advancement of technologies like the Internet of Things (IoT), social media, and online transactions, organisations are producing enormous amounts of data every day. Data engineers are crucial for managing, analysing, and analysing this data to produce business value. If you aspire to become a proficient Data Engineer, look no further than 3RI Technologies. With their Data Engineering Course with placement, you can acquire the necessary skills and knowledge to embark on a successful career in this exciting field.

Why Choose 3RI Technologies for Data Engineering Course?
  1. Cutting-Edge Curriculum: At 3RI Technologies, the Data EngineerClasses in Puneis precisely planned to cover every facet of this fast-expanding industry. The curriculum is regularly revised to reflect the most recent business trends, ensuring that students are given information that is both pertinent and useful. Every crucial aspect of data engineering is thoroughly covered, from data gathering and storage through data modelling, processing, and visualisation.
  2. Experienced Faculty: Any training program’s effectiveness depends on having qualified and skilled instructors. The team at 3RI Technologies is comprised of industry leaders with strong backgrounds in data engineering. The learning process is made more practical and powerful by the lecturers’ extensive academic knowledge as well as their useful insights from actual projects.
  3. Hands-On Projects: You cannot prepare for the challenges of the actual world solely through theory. As a result, 3RI Technologies places a strong emphasis on practical projects that allow for hands-on learning. Students have the chance to deal with actual datasets while utilising different data engineering methods and tools. Their ability to solve problems is improved, and they gain the confidence they need to succeed in the field.
  4. Industry-Recognized Data Engineer Certification: You will receive certificates that are recognized in the IT industry once you have finished the Data Engineer Certification course at 3RI Technologies. Your abilities and knowledge are validated by these certificates, providing you an advantage in the employment market. Candidates that have certificates are valued by employers because they demonstrate a dedication to lifelong learning and professional development.
  5. Placement Assistance: The Data Engineer Course with placement at 3RI Technologies is one of its best qualities. They actively assist students in obtaining job placements and have strong connections with the top companies in the sector. The dedicated placement team helps you construct a résumé, performs practice interviews, and offers career advice so you are well-prepared for job interviews.
  6. Course Flexibility: The Data Engineer Course in Pune from 3RI Technologies supports a range of learning styles with the option of offline or online classes. Students can benefit from face-to-face interactions with expert trainers and peers during offline classes for quick question clarification and stimulating discussions. However, Data Engineer online course allows students the flexibility of remote learning, enabling them to access course materials from any location with an internet connection and complete their studies at their own pace. Regardless of the mode they select, students can expect a high-quality education, practical understanding, and extensive data engineering learning.
Course Eligibility
  • Ideal for recent graduates in Computer Science or related fields.
  • Suitable for Freshers in computer science or related fields.
  • Software Engineers, Testers, Database Administrators, and Business Analysts can benefit from the course.
  • The course helps professionals transition into data engineering and broaden their skill set.
  • It provides opportunities for professionals to explore new career avenues in the field of data engineering.
  • Professionals can leverage their existing skills and experience to excel in data management, processing, and analysis.
  • The course equips working professionals with the necessary knowledge and tools to succeed in the dynamic field of data engineering.
Overview of Data Engineering Course

Overview of Data engineering Course

Data Engineering course offered by 3RI Technologies covers a wide range of important topics in depth. In addition to learning the fundamentals of Python programming, students will also get practical expertise in handling and interpreting data. Additionally, they will obtain a solid understanding of SQL and Data Structures, enabling them to efficiently maintain and query databases.

This course also emphasises important data engineering issues like data gathering, storing, integrating, and processing. The students will learn about the principles of Data Warehousing, which comprises developing and enhancing data storage systems to support efficient analytics and decision-making.Additionally, students will gain hands-on experience with well-known tools and technologies used in the industry, such as AWS for cloud-based data processing and storage and Apache Spark for processing enormous amounts of data.

Throughout the course, students will work on real-world simulation projects that will allow them to apply their knowledge and skills. The hands-on Data Engineer Training and Placement will deepen their understanding of data engineering processes and enhance their capacity for problem-solving. After completing the course, students will have a strong foundation in Data Engineering and be prepared to explore rewarding career opportunities in roles requiring data administration, processing, and analysis.

Data Engineering Employment Opportunities

A competent Data Engineer is in high demand as companies become more and more data-centric. Companies in a range of sectors, including Technology, E-Commerce, Banking, And Healthcare, are actively looking for experts who can manage and optimize their data infrastructure. You can open up a world of thrilling employment prospects with the Data Engineer Training at 3RI Technologies, including:

  1. Data Engineer: Data Engineers are in charge of planning, constructing, and managing data infrastructure and pipelines. They guarantee the smooth transfer of data between systems, allowing analysts and data scientists to derive valuable insights. Working with cutting-edge tools like Apache Hadoop, Apache Spark, and cloud-based data platforms will be part of your job as a data engineer.
  2. Big Data Engineer:Big data engineers with the necessary skills are in high demand due to the big data industry’s fast growth. These experts use distributed computing frameworks like Apache Hadoop and Apache Spark to manage and process large datasets. You can start a lucrative career as a Big Data engineer with the knowledge you’ll acquire from the course.

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EMI Plan Registration Percent
EMI
With
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10%
1st installment - 1st month of joining
70%
2nd installment - 2nd month
20%

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120 to 150 Hrs Course Duration
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data engineering course Syllabus

Best-in-class content by leading faculty and industry leaders in the form of videos, cases and projects, assignments and live sessions

1: Fundamentals of Statistics & Data Science
  1. Fundamentals of Data Science and Machine Learning
  • Introduction to Data Science
  • Need of Data Science
  • BigData and Data Science’
  • Data Science and machine learning
  • Data Science Life Cycle
  • Data Science Platform
  • Data Science Use Cases
  • Skill Required for Data Science
  1. Mathematics For Data Science
  • Linear Algebra
    • Vectors
    • Matrices
  • Optimization
    • Theory Of optimization
    • Gradients Descent
  1. Introduction to Statistics
  • Descriptive vs. Inferential Statistics
  • Types of data
  • Measures of central tendency and dispersion
  • Hypothesis & inferences
  • Hypothesis Testing
  • Confidence Interval
  • Central Limit Theorem
  1. Probability and Probability Distributions
  • Probability Theory
  • Conditional Probability
  • Data Distribution
  • Distribution Functions
    • Normal Distribution
    • Binomial Distribution
2: MS Excel
  1. Using Spreadsheet
  • What is Excel?
  • Why Use Excel?
  • Excel Overview
  • Excel Ranges,Selection of Ranges
  • Excel Fill,Fill Copies,Fill Sequences,Sequence of Dates
  • Excel add,move,delete cells
  • Excel Formulas
  • Relative and Absolute References
  1. Functions
  • SUM
  • AVERAGE
  • COUNT
  • MAX & MIN
  • RANDBETWEEN
  • TRIM
  • LEN
  • CONCATENATE
  • TODAY & NOW
  1. Advanced Functions
  • Excel IF Function
  • Excel If Function with Calculations
  • How to use COUNT, COUNTIF, and COUNTIFS Function?
  • Excel Advanced If Functions
  1. Data Visualization
  • Excel Data Analysis – Data Visualization
  • Visualizing Data with Charts
  • Chart Elements and Chart Styles
  • Data Labels
  • Quick Layout
3: RDBMS: SQL
  • An Introduction to RDBMS & SQL
  • Data Retrieval with SQL
  • Pattern matching with wildcards
  • Basics of sorting
  • Session summary
  • Order by clause
  • Aggregate functions
  • Group by clause
  • Having clause
  • Nested queries
  • Inner join
  • Multi join
  • Outer join
  • Adding and Deleting columns
  • Changing column name and Data Type
  • Creating Table from existing Table
  • Changing Constraints Foreign key
4: Python for Data Science
  1. An Introduction to Python
  • Why Python , its Unique Feature and where to use it?
  • Python environment Setup/shell
  • Installing Anaconda
  • Understanding the Jupyter notebook
  • Python Identifiers, Keywords
  • Discussion about installed module s and packages
  1. Conditional Statement ,Loops and File Handling
  • Python Data Types and Variable
  • Condition and Loops in Python
  • Decorators
  • Python Modules & Packages
  • Python Files and Directories manipulations
  • Use various files and directory functions for OS operations
  1. Python Core Objects and Functions
  • Built in modules (Library Functions)
  • Numeric and Math’s Module
  • String/List/Dictionaries/Tuple
  • Complex Data structures in Python
  • Python built in function
  • Python user defined functions

4. Introduction to NumPy

  • Array Operations
  • Arrays Functions
  • Array Mathematics
  • Array Manipulation
  • Array I/O
  • Importing Files with Numpy

5. Data Manipulation with Pandas

  • Data Frames
  • I/O
  • Selection in DFs
  • Retrieving in DFs
  • Applying Functions
  • Reshaping the DFs – Pivot
  • Combining DFs
    Merge
    Join
  • Data Alignment 

6. SciPy

  • Matrices Operations
  • Create matrices
    Inverse, Transpose, Trace,   Norms , Rank etc
  • Matrices Decomposition
    Eigen Values & vectors
    SVDs

7.Visualization with Seaborn

    • Seaborn Installation
    • Introduction to Seaborn
    • Basics of Plotting
    • Plots Generation
    • Visualizing the Distribution of a Dataset
    • Selection color palettes  

8. Visualization with Matplotlib

  • Matplotlib Installation
  • Matplotlib Basic Plots & it’s Containers
  • Matplotlib components and properties
  • Pylab & Pyplot
  • Scatter plots
  • 2D Plots-
  • Histograms
  • Bar Graphs
  • Pie Charts
  • Box Plots
  • Customization
  • Store Plots

9. SciKit Learn

  • Basics
  • Data Loading
  • Train/Test Data generation
  • Preprocessing
  • Generate Model
  • Evaluate Models

10. Descriptive Statistics

. Data understanding

  • Observations, variables, and data matrices
  • Types of variables
  • Measures of Central Tendency
  • Arithmetic Mean / Average
    • Merits & Demerits of Arithmetic Mean and Mode
    • Merits & Demerits of Mode and Median
    • Merits & Demerits of Median Variance

11. Probability Basics

  • Notation and Terminology
  • Unions and Intersections
  • Conditional Probability and Independence

12. Probability Distributions

  • Random Variable
  • Probability Distributions
  • Probability Mass Function
  • Parameters vs. Statistics
  • Binomial Distribution
  • Poisson Distribution
  • Normal Distribution
  • Standard Normal Distribution
  • Central Limit Theorem
  • Cumulative Distribution function

13.  Tests of Hypothesis

  • Large Sample Test
  • Small Sample Test
  • One Sample: Testing Population Mean
  • Hypothesis in One Sample z-test
  • Two Sample: Testing Population Mean
  • One Sample t-test – Two Sample t-test
  • Paired t-test
  • Hypothesis in Paired Samples t-test
  • Chi-Square test

14. Data Analysis

  • Case study- Netflix
  • Deep analysis on Netflix data
5: Machine Learning
  1. Exploratory Data Analysis
  • Data Exploration
  • Missing Value handling
  • Outliers Handling
  • Feature Engineering
  1. Feature Selection
  • Importance of Feature Selection in Machine Learning
  • Filter Methods
  • Wrapper Methods
  • Embedded Methods
  1. Machine Learning: Supervised Algorithms Classification
  • Introduction to Machine Learning
  • Logistic Regression
  • Naïve Bays Algorithm
  • K-Nearest Neighbor Algorithm
  • Decision Tress
    1. SingleTree
    2. Random Forest
  • Support Vector Machines
  • Model Ensemble
  • Model Evaluation and performance
    • K-Fold Cross Validation
    • ROC, AUC etc…
  • Hyper parameter tuning
    • Regression
    • classification
  1. Machine Learning: Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Decision Tree and Random Forest Regression
  1. Machine Learning: Unsupervised Learning Algorithms
  • Similarity Measures
  • Cluster Analysis and Similarity Measures
  1. Ensemble algorithms
  • Bagging
  • Boosting
  • Voting
  • Stacking
  • K-means Clustering
  • Hierarchical Clustering
  • Principal Components Analysis
  • Association Rules Mining & Market Basket Analysis 

7. Recommendation Systems

  • collaborative filtering model
  • content-based filtering model.
  • Hybrid collaborative system.
6: Artificial Intelligence & Deep Learning
  1. Artificial Intelligence
    • An Introduction to Artificial Intelligence
    • History of Artificial Intelligence
    • Future and Market Trends in Artificial Intelligence
    • Intelligent Agents – Perceive-Reason-Act Loop
    • Search and Symbolic Search
    • Constraint-based Reasoning
    • Simple Adversarial Search (Game-Playing)
    • Neural Networks and Perceptions
    • Understanding Feedforward Networks
    • Boltzmann Machines and Autoencoders
    • Exploring Backpropagation
  2. Deep Networks and Structured Knowledge
    • Understanding Sensor Processing
    • Natural Language Processing
    • Studying Neural Elements
    • Convolutional Networks
    • Recurrent Networks
    • Long Short-Term Memory (LSTM) Networks
  3. Natural Language Processing
    • Natural Language Processing
    • Natural Language Processing in Python
    • Studying Deep Learning
    • Artificial Neural Networks
    • ANN Intuition
    • Plan of Attack
    • Studying the Neuron
    • The Activation Function
    • Working of Neural Networks
    • Exploring Gradient Descent
    • Stochastic Gradient Descent
    • Exploring Backpropagation
  4. Artificial and Conventional Neural Network
    • Understanding Artificial Neural Network
    • Building an ANN
    • Building Problem Description
    • Evaluation the ANN
    • Improving the ANN
    • Tuning the ANN
  5. Image Processing / Machine Vision
  • Image basics
  • Loading and saving images
  • Thresholding
  • Bluring
  • Masking
  • Image Augmentation
  1. Conventional Neural Networks
  • CNN Intuition
  • Convolution Operation
  • ReLU Layer
  • Pooling and Flattening
  • Full Connection
  • Softmax and Cross-Entropy
  • Building a CNN
  • Evaluating the CNN
  • Improving the CNN
  • Tuning the CNN
  1. Recurrent Neural Network
  • Recurrent Neural Network
  • RNN Intuition
  • The Vanishing Gradient Problem
  • LSTMs and LSTM Variations
  • Practical Intuition
  • Building an RNN
  • Evaluating the RNN
  • Improving the RNN
  • Tuning the RNN
  1. Time Series Data
  • Introduction to Time series data
  • Data cleaning in time series
  • Pre-Processing Time series Data
  • Predictions in Time Series using ARIMA, Facebook Prophet models.
7: GIT Complete Overview
  • Introduction to Git and Distributed version control
  • Life Cycle
  • Create clone & commit Operations
  • Push & Update Operations
  • Stash, Move, Rename & Delete Operations
8: Machine Learning in Cloud

Machine Learning Features and Services

  • Using python in Cloud
  • How to access Machine Learning Services
  • Lab on accessing Machine learning services
  • Uploading Data
  • Preparation of Data
  • Applying Machine Learning Model
  • Deployment by publishung Models using  AWS or other cloud computing.

 

9: Data Visualization with Tableau

1. Introduction to Data Visualization and the Power of Tableau

  • Architecture of Tableau
  • Product Components
  • Working with Metadata and Data Blending
  • Data Connectors
  • Data Model
  • File Types
  • Dimensions & Measures
  • Data Source Filters
  • Creation of Sets.

2. Scatter Plot

  • Gantt Chart
  • Funnel Chart
  • Waterfall Chart
  • Working with Filters
  • Organizing Data and Visual Analytics
  • Working with Mapping
  • Working with Calculations and Expressions
  • Working with Parameters
  • Charts and Graphs
  • Dashboards and Stories
10: Project Work and Case Studies
  • Machine Learning end to end Project blueprint
  • Case study on real data after each model.
  • Regression predictive modeling – E-commerce
  • Classification predictive modeling – Binary Classification
  • Case study on Binary Classification – Bank Marketing
  • Case study on Sales Forecasting and market analysis
  • Widespread coverage for each Topic
  • Various Approaches to Solve Data Science Problem
  • Pros and Cons of Various Algorithms and approaches
  • Amazon-Recommender
  • Image Classification
  • Sentiment Analysis

Project Domains: Finance

  • Insurance company wants to decide on the premium using various parameters of the client.
  • It’s an important problem to keep the clients and attract new ones.

By completing this project you will learn:

  • How to collect data?, how to justify right features? , Which ML / DL model is best in this situation? How much data is enough?
  • How to have CI/CD in the project?
  • How to do Deployment of Project to cloud?

 

Image Processing in Health care

  • A hospital wants to automate Detection of pneumonia in X-rays using image processing.

By doing this project you will understand

  • How to handle image data? How to preprocess and augment image data? How to choose right model for image process?
  • How to apply transfer learning in image processing?
  • How to do incremental learning & CI/CD in the project?
  • How to do Deployment of Project to cloud?

 

 

Natural Language Processing

  • One of the companies wants to automate applicant’s level in English communication.
  • Create a ML/DL model for this task.

By completing this project you will learn

  • How to do convert text to right representation? How to preprocess text data? How to select right ML/DL model for text data ?
  • How to do transfer learning in Text Analytics?
  • How to do CI/CD in text analytics project?
  • How to do Deployment of Project to cloud?

 

Mechanical

  • A mechanical company wants to perform predictive maintenance of engine parts.
  • This enables company to efficiently change parts before machine fails.

By performing this task you will learn,

  • How to handle time series data?
  • How to preprocess time series data?
  • How to create ML/DL model for Time series Data?
  • How to do CI/CD in text analytics project?
  • How to do Deployment of Project to cloud?

 

Sales / Demand Forecasting

  • Predict the sales / demand of a product of a company.
  • Sales / Demand forecasting of the product will help company efficiently manage the resources.
  • Create a ML/DL model for this problem.

By performing this project you will learn,

  • How to handle time series data?
  • How to preprocess time series data?
  • How to create ML/DL model for Time series Data?
  • How to do CI/CD in text analytics project?
  • How to do Deployment of Project to cloud?

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Will I Get Certified?

Upon successfully completing this program, you’ll earn a certificate.

The 3RI certification is accepted and respected by every significant multinational company across the nation. Fresh graduates and corporate trainees are eligible for the assistance. We offer certificate once the academic and practical courses have been finished. The certification that we offer here at 3RI is recognized across the country. The value of your resume will grow as a result. With the assistance of this qualification, you will be able to obtain prominent employment posts in the most successful multinational corporations in the country. The completion of our course as well as the projects that are based on practical application, are prerequisites for receiving the certificate.

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Data Engineer Certification

DATA SCIENCE Online Course Certification

As you have enrolled in our Data Science online course, you have to attend the online Data Science classes. In case you have missed any class due to any personal or health reasons, you have the chance to attend the same session with another upcoming batch. If that is not possible for you, you may listen to the recording of the previous class. And if you have any doubt, you can clarify them during the next class. Doubt clearance is essential to proceed to the next topic.
You also will get a chance to be part of all the quizzes that will be conducted while the Data Science online classes will be going on. The discussions in the Data Science online class will lead to healthy competitions.
As a practice, you will have to finish all the assignments before time and submit them to the concerned instructor for checking. This will help them in understanding whether you can grasp the concept or not. You will receive timely feedback from them to help you work on your weak areas.
Our instructors will not give direct answers to all the coding examples they will take in the Data Science online class. They will direct you to think for yourself and come up with an innovative solution. If the solution is not correct, they will discuss why it is incorrect and how it can get better.
The Data Science online course will end with an exam that will cover theoretical concepts along with practice exams. This will help us to understand how well have you understood the concepts. You may reattempt the exam if you are not able to perform well in this exam.
After completing all your projects and passing this exam, you will be awarded a Certificate of Completion. The certificate holds value across many reputed companies. This will tell your future employers of your capabilities as a Data Scientist.
In case for some reason you do not clear your exam, our instructors will work with you in partnership. They will try to improve your weak areas. Whenever you are confident you may reappear for the exam and earn your certificate.

What are the necessary criteria to unlock my DATA SCIENCE 3RI Technologies Certification?

You need to complete all your assignments and projects on time to be eligible for Data Science 3RI Certification. If your instructor asks you to make any necessary changes, you would have to do it and show the results back to your instructor for approval.
An online exam will be conducted to assess your knowledge theoretically and practically. You need to pass this exam to be able to be eligible to get the certification.
Along with these exam results, you would be also assessed based on your overall performance during the Data Science online class.
Once you satisfy all the above criteria, you will be eligible to receive the certification.

How long does it take to complete DATA SCIENCE online training?

You will need nearly 3 months to complete the Data Science online course. This will include assignments and project completion time.
If you want to complete this Data Science online course in a shorter time, then you would need to spend more time each day on every topic and complete the assignments before time to proceed to the next topics.
Your primary focus should be to get the maximum knowledge during each Data Science online classes.

What is the value of DATA SCIENCE Online training certification in IT companies?

The Data Science online training will help you to transform from a novice to an expert in Data Science. During the Data Science online course, you will be working on multiple projects and case studies. This kind of project is the real USP of our course. Through this project, you will learn about the development, deployment, and testing phase of a product cycle. You will learn the best practices that can be implemented.
After this, the only thing that is needed to apply your knowledge in the job that you start working for.

How many chances do I get to pass the online exam conducted for DATA SCIENCE online training?

Unless you clear the exam you will not get the certification. If you fail once, you will get another chance to reappear for the exam. You will be closely working with your instructor on your potential weak areas and strengthen your grounds in that particular area.
However, there are only fewer chances that you will not be able to pass this exam if you attend all the Data Science online classes and complete all your assignments and projects.

If, I don’t pass the online exam when can I reappear for the exam again?

If you fail when you attempt the exam, you will work along with our instructors to find out the areas where you are having difficulty. This analysis will help the instructors to spend extra time with you to strengthen your weaker areas. Once you are confident that you have gained the necessary knowledge, you can appear for the exam.
As a convention, we prefer a minimum gap of 15 days between the two exams.

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Frequently Asked Questions

1. How do you become a Data Engineer?

To become a Data Engineer, you must possess a strong foundation in Math, Statistics, and Computer Science. Enrolling in a specialised Data Engineering Course, such as the one offered by 3RI Technologies, may help you gain the knowledge and skills you need. The course curriculum at 3RI Technologies includes instruction in fundamental topics like programming, Data Structures, SQL, Data Warehousing, Cloud Platforms, and Big Data Technologies.

By working on the practical projects offered in the course, which will give you actual practical data engineering experience, you can apply your skills to real-world circumstances. Additionally, 3RI Technologies offers chances for internships and personal projects, greatly enhancing your prospects. The practical training will improve your skills and make you more desirable to employers.

By completing an in-depth Data Engineering Training program, gaining real-world experience, and consistently improving your skills, you may launch a successful career as a Data Engineer. The guidance and support provided by 3RI Technologies will play a vital role in determining your route to becoming a competent data engineer.

2. What topics will be covered in this training?

The Data Engineering Training at 3RI Technologies covers a comprehensive set of topics to provide students with a strong foundation in data engineering. Some of the key topics covered in the training include:

Python: Students will learn the computer language Python, which is frequently used in data engineering for automating, analysing, and manipulating data.

Data Collection: The strategies for collecting data from different sources, such as databases, APIs, web scraping, and file systems, will be taught to the students.

Data Modelling: To ensure efficient and effective data organisation, students will comprehend the fundamentals of data modelling and database design.

Data Storage: Various forms of data storage systems, including Relational databases (SQL) and NoSQL databases like MongoDB, are covered in the course. For maximum performance and scalability, databases will be designed and managed by the students.

Data processing: Using tools like Apache Hadoop and Apache Spark, students will build proficiency in data processing techniques. They will discover effective methods for handling and analysing massive amounts of data.

Data Visualization: Using data visualisation libraries and tools, students will learn ways to present data in understandable and visually appealing ways.

Cloud-based Data Platforms: Platforms for storing, processing, and analysing data on the cloud are covered in the course, along with an introduction to cloud platforms like AWS (Amazon Web Services).

ETL: The basics of ETL (Extract, Transform, Load) procedures, such as data extraction, data transformation, and data loading, will be taught to the students. To create reliable data pipelines, they will work with programmes and frameworks like Apache Kafka.

Data Warehousing: The principles of data warehousing, such as creating and refining data storage systems for effective analytics and reporting, will be learned by the students.

Mongo DB: Students will get first-hand experience using the well-known NoSQL database MongoDB and learn methods to work with unstructured data.

By covering these topics, the Data Engineering Course at 3RI Technologies equips students with the knowledge and skills necessary to excel in the field of Data Engineering and prepares them for real-world Data Engineering challenges.

3. What are Data Engineer skills?

Data engineers require a diverse set of skills to excel in their roles. The Data Engineering Course at 3RI Technologies focuses on developing the following skills:

  1. a) Data Manipulation: Proficiency in manipulating and transforming data using tools like SQL and programming languages like Python or Scala is essential for data engineers. Students will learn various data manipulation techniques to clean, filter, and aggregate data effectively.
  2. b) Data Modeling: Understanding data structures and designing efficient data models is crucial for data engineers. The course covers concepts like relational data modeling, dimensional modeling, and schema design, enabling students to create robust and scalable data models.
  3. c) Data Integration and ETL: Data engineers need to extract data from various sources, transform it into a suitable format, and load it into a target system. The training program equips students with the skills to design and implement ETL processes using tools like Apache Kafka, Apache Spark, and cloud-based ETL services.
  4. d) Distributed Computing: As data volumes continue to grow, distributed computing frameworks like Apache Hadoop and Apache Spark have become indispensable. Students will learn how to leverage these frameworks to process large-scale datasets efficiently and perform complex analytics tasks.
  5. e) Cloud Data Platforms: With the increasing popularity of cloud computing, data engineers must be proficient in working with cloud-based data platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure. The course provides hands-on experience in deploying and managing data pipelines on cloud platforms.
4. What is a Data Engineer and what do they do?

A Data Engineer is a person who plans, creates, and maintains the infrastructure and systems needed for data storage, processing, and analysis. They collaborate closely with data scientists, analysts, and other stakeholders to ensure data availability, quality, and reliability.

Data Engineers work collaboratively with Data Scientists, Analysts, and other stakeholders to understand the data demands and create scalable and trustworthy data pipelines. They have experience using various databases, data warehouses, and large data processing frameworks. Python, Java, or SQL are among the programming languages they are proficient in.

These are the major responsibilities of data engineers:

Data integration: They gather data from different sources, transform it, clean it up, and ensure the data’s accuracy and integrity.

Data Warehousing: They develop and manage data warehouse systems to enable efficient data storage and retrieval.

ETL Processes: Extract, Transform, Load (ETL) processes are developed to move data from source systems to the data warehouse or data lake.

Data Modelling: To organise and arrange data for effective storage and analysis, they develop data models and schemas.

Performance Optimisation: They make data systems more scalable, efficient at processing data, and able to execute queries more quickly.

Data Governance: They put data governance procedures into place and make sure that rules governing data security and privacy are followed.

In general, Data Engineers are essential to helping businesses properly use their data assets and make decisions and get insights based on that data.

5. Why Data engineering training from 3RI Technologies?

The field of data engineering offers excellent job prospects for those who are interested about working with data. Whether you are a fresher looking to specialize in data engineering or an experienced professional seeking to transition into the field, the Data Engineering Courses in India at 3RI Technologies is the best choice.

With the complete Data Engineering Course at 3RI Technologies, you may get the skills, knowledge, and practical experience you need to be successful in this exciting industry.

By choosing 3RI Technologies, you have access to a cutting-edge curriculum, skilled instructors, useful projects, and industry-recognized certifications. Through the training program, you acquire the skills and information required to succeed as a Data Engineer or Big Data Engineer, which opens up a variety of job opportunities across industries.

Whether you are starting your career or trying to improve your abilities, the Best Data Engineering Courses from 3RI Technologies is the best way to get started in the field of data engineering.Unlock your potential and join the ranks of skilled data engineers shaping the future of data-driven enterprises

 

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