Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
The entire training includes real-world projects and highly valuable case studies.
IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.
MODULE 1: DATA ENGINEERING INTRODUCTION
• What is Data Engineering?
• Data Engineering scope
• Data Ecosystem, Tools and platforms
• Core concepts of Data engineering
MODULE 2: DATA SOURCES AND DATA IMPORT
• Types of data sources
• Databases: SQL and Document DBs
• Connecting to various data sources
• Importing data with SQL
• Managing Big data
MODULE 3: DATA PROCESSING
• Python NumPy Package Introduction
• Array data structure, Operations
• Python Pandas package introduction
• Data wrangling with Pandas
• Managing large data sets with Pandas
• Data structures: Series and DataFrame
• Importing data into Pandas DataFrame
• Data processing with Pandas
MODULE 4: DATA ENGINEERING PROJECT
• Setting Project Environment
• Data Ingestion through Pandas methods
• Hands-on: Ingestion, Transform Data and Load data
MODULE 1: PYTHON BASICS
• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity
MODULE 2: PYTHON CONTROL STATEMENTS
• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements
MODULE 3: PYTHON DATA STRUCTURES
• Basic data structure in python
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions
MODULE 4: PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Iterators
• Generator functions
• Lambda functions
• Map, reduce, filter functions
MODULE 5: PYTHON NUMPY PACKAGE
• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations
MODULE 6: PYTHON PANDAS PACKAGE
• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis
MODULE 1 : OVERVIEW OF STATISTICS
MODULE 2 : HARNESSING DATA
MODULE 3 : EXPLORATORY DATA ANALYSIS
MODULE 4 : HYPOTHESIS TESTING
MODULE 5 : CORRELATION AND REGRESSION
MODULE 1: DATA ENGINEERING INTRODUCTION
• What is Data Engineering?
• Data Engineering scope
• Data Ecosystem, Tools, and platforms
• Core concepts of Data engineering
MODULE 2: DATA WAREHOUSE FOUNDATION
• Data Warehouse Introduction
• Database vs Data Warehouse
• Data Warehouse Architecture
• ETL (Extract, Transform, and Load)
• ETL vs ELT
• Star Schema and Snowflake Schema
• Data Mart Concepts
• Data Warehouse vs Data Mart — Know the Difference
• Data Lake Introduction
• Data Lake Architecture
• Data Warehouse vs Data Lake
MODULE 3: DATA SOURCES AND DATA IMPORT
• Types of data sources
• Databases: SQL and Document DBs
• Connecting to various data sources
• Importing data with SQL
• Managing Big data
MODULE 4: DATA PROCESSING
• Python NumPy Package Introduction
• Array data structure, Operations
• Python Pandas package introduction
• Data structures: Series and DataFrame
• Importing data into Pandas DataFrame
• Data processing with Pandas
MODULE 5: DOCKER AND KUBERNETES FOUNDATION
• Docker Introduction
• Docker Vs. regular VM
• Hands-on: Running our first container
• Common commands (Running, editing, stopping, and managing images)
• Publishing containers to DockerHub
• Kubernetes Orchestration of Containers
• Build Docker on Kubernetes Cluster
MODULE 6: DATA ORCHESTRATION WITH APACHE AIRFLOW
• Data Orchestration Overview
• Apache Airflow Introduction
• Airflow Architecture
• Setting up Airflow
• TAG and DAG
• Creating Airflow Workflow
• Airflow Modular Structure
• Executing Airflow
MODULE 7: DATA ENGINEERING PROJECT
• Setting Project Environment
• Data pipeline setup
• Hands-on: build scalable data pipelines
MODULE 1 : AWS DATA SERVICES INTRODUCTION
MODULE 2 : DATA INGESTION USING AWS LAMDBA
MODULE 3 : DATA PIPELINE WITH AWS KINESIS
MODULE 4 : DATA WAREHOUSE WITH AWS REDSHIFT
MODULE 5 : DATA PIPELINE WITH AZURE SYNAPSE
MODULE 6 : STORAGE IN AZURE
MODULE 7: AZURE DATA FACTORY
MODULE 8 : DATA ENG PROJECT WITH AZURE/AWS
MODULE 1: DATA WAREHOUSE FOUNDATION
• Data Warehouse Introduction
• Database vs Data Warehouse
• Data Warehouse Architecture
• ETL (Extract, Transform, and Load)
• ETL vs ELT
• Star Schema and Snowflake Schema
• Data Mart Concepts
• Data Warehouse vs Data Mart — Know the Difference
• Data Lake Introduction
• Data Lake Architecture
• Data Warehouse vs Data Lake
MODULE 2: DOCKER FOUNDATION
• Docker Introduction
• Docker Vs. regular VM
• Hands-on: Running our first container
• Common commands (Running, editing, stopping and managing images)
• Publishing containers to Docker Hub
• Kubernetes Orchestration of Containers
• Build Docker on Kubernetes Cluster
MODULE 3: KUBERNETES CONTAINER ORCHESTRATION
• Kubernetes Introduction
• Setting up Kubernetes Clusters
• Kubernetes Orchestration of Containers
• Build Docker on Kubernetes Cluster
MODULE 4: DATA ORCHESTRATION WITH APACHE AIRFLOW
• Data Orchestration Overview
• Apache Airflow Introduction
• Airflow Architecture
• Setting up Airflow
• TAG and DAG
• Creating Airflow Workflow
• Airflow Modular Structure
• Executing Airflow
MODULE 5: DATA ENGINEERING PROJECT
• Setting Project Environment
• Data pipeline setup
• Hands-on: build scalable data pipelines
MODULE 1 : DATABASE INTRODUCTION
MODULE 2 : SQL BASICS
MODULE 3 : DATA TYPES AND CONSTRAINTS
MODULE 4 : DATABASES AND TABLES (MySQL)
MODULE 5 : SQL JOINS
MODULE 6 : SQL COMMANDS AND CLAUSES
MODULE 7 : DOCUMENT DB/NO-SQL DB
MODULE 1: BIG DATA INTRODUCTION
• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction
MODULE 2: HDFS AND MAP REDUCE
• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
• Hands-on Map Reduce task
MODULE 3: PYSPARK FOUNDATION
• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs
MODULE 4: SPARK SQL and HADOOP HIVE
• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
• Working with Spark SQL Query Language
MODULE 5: MACHINE LEARNING WITH SPARK ML
• Introduction to MLlib Various ML algorithms supported by Mlib
• ML model with Spark ML.
• Linear regression
• logistic regression
• Random forest
MODULE 6: KAFKA and Spark
• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations
Defining data engineering involves understanding its purpose as the discipline focused on designing, developing, and managing systems and processes to efficiently handle and analyze vast volumes of data. Data engineering aims to establish robust data pipelines, ensure the integrity and quality of data, and support the utilization of data for making informed decisions.
To enter the field of data engineering in Amritsar, individuals can consider the following measures:
Build a strong understanding of mathematics, statistics, and programming.
Develop proficiency in programming languages such as Python or SQL.
Acquire expertise in database management systems and data manipulation techniques.
Familiarize themselves with big data technologies like Hadoop and Spark.
Gain practical experience and enhance their skills through project work and real-world applications.
Participating in data engineer training comes with numerous perks, such as:
Acquiring in-demand skills and expertise in the field of data engineering.
Enhancing employment prospects across different industries.
Gaining practical experience by working on hands-on projects.
Staying updated with the latest industry trends and advancements.
Yes, data engineering is poised for a favorable future. With the ever-increasing volume and complexity of data, organizations across various industries will rely on data engineers to effectively manage and optimize their data infrastructure. The continuous advancements in technology and the expanding data landscape ensure a promising future for data engineering professionals.
The prerequisites for enrolling in a data engineer course in Amritsar can vary depending on the specific course and provider. However, it is generally recommended to have a foundational understanding of mathematics, statistics, and programming. Familiarity with databases, SQL, and programming languages like Python or Java can also be advantageous for a smoother learning experience.
The prerequisites for enrolling in a data engineer course in Amritsar may vary depending on the specific program and institute. However, a basic understanding of mathematics, statistics, and programming concepts is typically beneficial. Knowledge of databases, SQL, and programming languages such as Python or Java can also be advantageous.
The average cost of data engineer training in Amritsar can vary depending on factors such as the institution providing the training, the duration of the program, and the mode of delivery (online or classroom). On average, the fees for data engineer training in Amritsar typically range from around 40,000 INR to 1,00,000 INR.
DataMites is widely considered a top choice for data engineer training. Their offerings include a comprehensive curriculum, industry-relevant projects, and experienced instructors who ensure that students gain the essential skills and knowledge in data engineering.
Upon completing data engineer training, individuals gain access to a wide range of employment options. They can pursue roles such as Data Engineer, Database Administrator, ETL Developer, Big Data Engineer, and Cloud Data Engineer in industries like technology, finance, healthcare, and e-commerce.
While prior experience can be advantageous, it is not always a requirement to secure data engineer job positions. Some entry-level roles or junior positions may be accessible to individuals without extensive experience. Engaging in internships or hands-on projects can serve as valuable experiences to demonstrate skills and competence in data engineering.
Data engineer training is considered an asset due to its ability to provide individuals with essential skills and knowledge. It covers vital concepts, tools, and techniques necessary for constructing data pipelines, managing databases, and facilitating efficient data processing and analysis. This expertise is highly valued in today's data-driven world, where organizations depend on data for strategic decision-making and achieving their objectives.
Individuals can access data engineering training in Amritsar by considering enrollment at DataMites. DataMites offers comprehensive courses that cover the essential concepts, tools, and techniques of data engineering. Through hands-on experience, practical projects, and guidance from experienced instructors, DataMites ensures individuals in Amritsar acquire the necessary skills in data engineering.
The DataMites Certified Data Engineer Training in Amritsar includes an extensive curriculum covering a wide range of topics. These include data engineering concepts, tools, and technologies such as Hadoop, Spark, SQL, and data pipeline development. Practical exercises and hands-on projects are incorporated to help you gain practical skills in these areas.
The Data Engineer Course at DataMites® in Amritsar is open to individuals who meet certain criteria. Generally, those with a background in computer science, mathematics, or related fields, as well as professionals looking to pursue careers in data engineering, are eligible to enroll.
The duration required to complete the DataMites Data Engineer Course in Amritsar may vary depending on the learning mode. Typically, online instructor-led training takes around 6 months, comprising over 150 learning hours. However, self-paced learning options may have different timeframes.
Certainly, DataMites® provides classroom training sessions for Data Engineer Courses in Amritsar, along with their online training alternatives. This gives individuals the choice to opt for classroom-based learning, depending on their preferences and convenience.
The Data Engineer Course in Amritsar at DataMites® is led by highly skilled instructors who have expertise in data engineering concepts, tools, and industry practices. These instructors offer valuable guidance, mentorship, and support throughout the training program.
The training for the Data Engineer Course in Amritsar at DataMites® is delivered by highly qualified instructors who specialize in data engineering. These instructors possess extensive knowledge in data engineering concepts, tools, and industry practices, ensuring participants receive comprehensive training and support.
At DataMites®, you can avail yourself of various training options for data engineering courses, including instructor-led online training, classroom training, and self-paced learning. This provides the freedom to choose the training method that suits your learning style and schedule.
Absolutely, DataMites® provides a provision for paying the course fee in installments. Recognizing that learners may have financial limitations, this option allows individuals to manage the course fee more conveniently while benefiting from data engineering training.
Yes, individuals undertaking Data Engineer Courses at DataMites® in Amritsar can expect placement assistance. DataMites® provides comprehensive support in terms of career guidance, resume building, interview preparation, and helping participants connect with potential employers for job placements.
DataMites® accepts a variety of payment methods for their training programs. You can make payments conveniently through online channels using credit cards, debit cards, net banking, and other digital payment options. This flexibility enables learners to choose the payment method that suits their preferences and ensures a smooth and secure transaction process.
The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -
The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.
No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.