What will be the focus of this course?

The Data Engineering course aims to provide aspirants with an in-depth understanding of all the essential tools and skills used by Data Engineers. The course provides hands-on learning of leading as Python, SQL, Spark, Kafka, and many more.

What will be the mode of training?

The training will be conducted in hybrid mode i.e., through the classroom as well as live instructor-led virtual sessions. The timings for both the sessions will be the same.

When do I get to work on live projects?

After the successful completion of 80% of your assignments, you are assigned to a live project where you will work with a group of students to bring the project to closure. After that, you will make a project presentation.

When do I get my certificate?

After the successful completion of the program, you will be awarded the Data Engineering certificate, powered by IBM..

Who all can pursue a Data Engineering course?

This course is designed for students as well as working professionals. The basic requirement to undertake this course includes a degree in engineering, computer applications, or mathematics.

Do I need to pay extra to receive a certificate?

No, there are no extra charges for the certification. The cost is included in the package.

What if I miss out on a session?

Not to worry, if you miss out on a session you can access the recorded session from the online Learning Management System (LMS)..

Do I get a job guarantee?

We do not guarantee placements nevertheless, our placement cell supports you with resume building sessions, mock interviews, mentorship, and interview preparation. Our team also helps you launch your career by providing interview opportunities.

Certificate Program in

Data Engineering Course in Malaysia

With Data Engineering, learn to optimize, label, explore, transform and move data to produce meaningful and useful insights to empower your business.

40 Hours Classroom & elearning Sessions
20+ Hours Assignments
20+ Hours of Industry Use Cases
6 months Learning Management System access
Blockchain enabled tamper-proof security certificate

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Academic Partners & International Accreditations

Calendar-for-Virtual Interactive Classes

January

Start Date

01^st

January
TBC

Timings
Show all sessions

Data Engineering

business analytics course duration in Jodhpur

Total Duration

3 Months

Prerequisites

Need basic programming skills
Python knowledge is highly recommended
SQl/Nosql experience is highly desired
Basic knowledge on Linux platform

Data Engineering Training Overview

The significant role of Data Engineers is to focus on processing data in a way that makes it easy for them to extract meaningful value from it. In this course, explore the various skills involved in laying down a successful foundation for digital transformation and facilitates the deployment of data science initiatives. Learn the tools and frameworks that data engineers need today to leverage AI, machine learning, and business intelligence skills to effectively manage data.
Data Engineering skills are constantly evolving with several skills unique to their profile. Learn advanced SQL techniques along with tools like Python, Keras, Pytorch, Spyder, etc. to handle a huge amount of data and solve real-world problems faster. Gain a thorough understanding of building data pipelines and integrating them smoothly with various data structures to keep pace with big data needs and implement data science and AI initiatives in your organizations.

What is Data Engineering?

To make data-driven decisions, data has to be collected, sorted, and analyzed giving results to quality data. This process of giving insights from data to businesses using advanced analytical tools is termed Data Engineering.

Data Engineering Training Learning Outcomes

Transforming data from a raw format to attractive analytical boards is a long journey that requires a dedicated environment known as data pipelines to obtain, process, and store data. Data Engineers are architects of these data platforms and give value to data analysis, business intelligence, and machine learning. In this course, you will learn to create and manage data infrastructures and build and deploy data pipelines to explore the various techniques to optimize large volumes of data and algorithms. You will also get to work with large and multi databases and learn how to create a Python data pipeline from scratch. Find out how tree data structures speed up the processing of data analysis. As you move on, you will also learn to work with complex data that are collected from multiple sources to build data processing systems. This course will give you an understanding of the skills involved in descriptive and inferential analysis and how data processing is optimized using various tools like NumPy. Also, learn to analyze data in Python and take a dive into object-oriented programming (OOP). Understand data warehouse and data manipulation and learn how to use Big Query ML to build different kinds of ML models. Join the Data Engineering course to get a hands-on introduction to developing and building data pipelines. Also-

Understand the meaning of Data Engineering and its ecosystem

Learn to extract data from multiple files and databases

Learn to construct and deploy a data pipeline in production

Work with various file formats in both NoSQL and Relational databases

Master the skills and techniques to refine, transform, and add value to your data

Explore the various data structures including Linked Lists, Stacks, Dictionaries, Queues, etc.

Block Your Time

40 hours

Classroom &
elearning sessions

20+ hours

Assignments

20+ hours

Industry Use Cases

Enroll Now

Who Should Sign Up?

Data professionals who work in conjunction with data science team
Professionals aspiring to work on database designs and build data pipelines
Professionals like ML developers, Data Engineers , Architects, Software developers
Candidates from computer science and IT backgrounds aspiring to crack jobs in data engineering fields
Professionals working as business intelligence analyst or database administrators

Data Engineering Course Modules

These modules are going to take you through the essential skills required to become a successful data engineer. You will learn how to gather, extract, transform, and then deploy data pipelines in the cloud. Using big data tools and real-time multiple databases you will gain expertise in skills of refining, and exchanging data. These modules are crafted keeping in mind the current industry trends and market requirements.

Distributed Framework - Hadoop

Introduction Distributed Framework
Apache Spark
- Core, SQL, MLlib
Introduction to Apache Airflow
- Building Pipeline in Apache Airflow
- Deploy and Monitor Data Pipeline
- Production Data Pipeline

ML Pipeline on Cloud - AutoML (AWS SageMaker, Azure Auto ML, Vertex AI (GCP)

Real Time Streaming on Cloud (Azure and GCP)
Deploy and Monitor Data Pipelines
- Production Data Pipeline
- Connecting to Databases
Data Lakes
- Populating a data lake
- Reading and Scanning the data lake
- Insert and Query a staging database

Building a Real Time Data Pipeline

Introduction to Pub/Sub Messaging System and Apache Kafka
Building kafka Cluster
Setup Zookeeper and Kafka Cluster
Configuring and Testing Kafka Cluster
Streaming Data with Apache Kafka
Data Processing with Apache Spark and Databricks (public cloud)

Data Pipeline Modules - Self-Paced Learning Modules

1. Data Science primer and ML Pipelines and AutoML pipelines

2. Data Architectures

3. SQL (Data Sources)

4. Big Data (Distributed Framework)

5. NoSQL (MongoDB)

6. SQL/NoSQL + Python - Data Ingestion

Trends in Data Engineering Certification

A Data Engineer deals with delivering data analytics across various systems using data modeling, data storage, data transforming, and data maintenance. With high demand in the data engineering services market and with growth estimates from 18.3% to an unbelievable 31.5% p.a., consulting firms like Accenture and other tech companies like Cognizant are in dire need of data engineers who can capture big data, to govern, analyze, tag, and utilize. AI will take care of repetitive tasks in the field of quality assurance that will reduce the time consumed while performing these tasks.

With increasing development in software, data engineers will also be seen leveraging HTTP/3 in the layer of data collection for communicating across the web. For smooth exchange of data and distributed storage, the use of blockchain technology will also be seen in action along with the use of the AWS Lambda function by data engineers to process and execute data pipelines.