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
Data engineering refers to the process of designing, constructing, and managing the infrastructure and systems necessary for the collection, storage, processing, and analysis of large volumes of data, ensuring its availability, reliability, and accessibility for data-driven decision-making.
a. Acquire a solid foundation in mathematics, statistics, and programming.
b. Gain proficiency in data manipulation, database management, and data integration.
c. Develop expertise in big data technologies, such as Hadoop, Spark, and cloud platforms.
d. Build a portfolio of data engineering projects showcasing your skills and capabilities.
e. Seek internships or entry-level positions in organizations that require data engineering expertise.
f. Continuously update your knowledge by staying informed about emerging technologies and industry trends.
The timeframe for becoming a data engineer can vary depending on individual circumstances and the learning path chosen. Generally, it may take anywhere from six months to two years to gain the necessary skills and experience to start a career as a data engineer.
a. Gain in-depth knowledge of data engineering concepts, tools, and techniques.
b. Acquire hands-on experience with industry-standard data engineering technologies.
c. Enhance job prospects and increase earning potential in the rapidly growing field of data engineering.
d. Develop a strong foundation for career progression and opportunities in data-related roles.
Prerequisites for enrolling in a data engineering course in Itanagar:
a. Basic understanding of mathematics, statistics, and programming concepts.
b. Familiarity with databases and SQL.
c. Proficiency in at least one programming language, such as Python or Java.
d. Knowledge of data manipulation and data analysis techniques.
e. Some courses or programs may have specific prerequisites or recommended prior experience, so it is essential to review the requirements of the chosen course or institute.
The cost of data engineering training can vary depending on the institute, program duration, and the level of instruction. In general, the data engineer training fees in Itanagar can be anywhere from 40,000 INR to INR 1,00,000. It is advisable to research different training providers in Itanagar to determine the specific costs associated with their courses.
Datamites is considered one of the best institutes for data engineering training. With comprehensive curriculum, industry-relevant projects, and experienced instructors, the institute provides a strong foundation in data engineering concepts, tools, and techniques. Its focus on practical hands-on learning and industry connections makes them a preferred choice for individuals aspiring to excel in the field of data engineering.
After completing data engineering training, individuals can explore various job opportunities such as Data Engineer, Data Analyst, Big Data Engineer, ETL Developer, Database Administrator, or Cloud Data Engineer. These roles can be found in diverse industries including technology, finance, healthcare, e-commerce, and more.
Essential skills for a data engineer:
a. Proficiency in programming languages like Python, Java, or Scala.
b. Strong knowledge of SQL and experience with database management systems.
c. Understanding of big data technologies like Hadoop, Spark, and NoSQL databases.
d. Data modeling and data architecture design skills.
e. Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
f. Experience in data pipeline development, data integration, and ETL processes.
g. Problem-solving and analytical thinking abilities.
h. Effective communication and collaboration skills.
The average salary for Data Engineers in Itanagar can vary depending on factors such as experience, skills, industry, and organization. However, the average salary for Data Engineer is ₹8,90,000 per year in the India, as reported by Glassdoor.
Data Analytics offers promising career prospects, with a wide range of job opportunities available. Professionals in this field can find employment in various sectors, including technology companies, consulting firms, financial institutions, healthcare organizations, e-commerce companies, and government agencies. Data Analytics Job titles may include Data Analyst, Data Scientist, Business Intelligence Analyst, Data Engineer, Machine Learning Engineer, and Data Consultant, among others.
While a specific educational path may not be mandatory for a career in data analytics, having a degree in a related field can be advantageous. Employers often prefer candidates with a bachelor's or master's degree in mathematics, statistics, computer science, economics, business analytics, or a related discipline. Additionally, certifications and specialized training in data analytics, data science, or relevant tools can further enhance your skills and marketability.
To obtain data engineering training in Itanagar, you can enroll in courses offered by reputable training institutes such as DataMites®, either through their online programs or by attending in-person classes if available.
The DataMites Certified Data Engineer Courses in Itanagar covers a comprehensive curriculum that includes topics like data integration, data modeling, ETL processes, data warehousing, big data technologies, and cloud platforms. Hands-on projects and real-world case studies are also included to enhance practical skills.
The Data Engineer Course at DataMites® in Itanagar is open to individuals who have a basic understanding of mathematics, statistics, and programming. Aspiring data engineers, IT professionals, software engineers, and data enthusiasts looking to transition into data engineering roles are eligible to enroll.
The duration of the DataMites Data Engineer Course in Itanagar can vary based on the learning mode chosen. Typically, it ranges is 6-Month and 150+ Learning Hours for online instructor-led training and may vary for self-paced learning options.
Pursuing online data engineer training from DataMites® offers several benefits, including flexibility in learning at your own pace and convenience, access to industry-expert instructors, hands-on assignments and projects, interactive learning materials, and networking opportunities with a global community of learners.
The cost of the DataMites Data Engineer Training in Itanagar may vary based on factors such as the learning mode chosen and any additional services or resources included. However, the data engineer course fee in Itanagar can vary from INR 26,548 to INR 68,000.
Yes, DataMites® provides classroom training for Data Engineer courses in Itanagar, allowing students to have in-person learning experiences and interactions with instructors and peers. We do provide data engineer offline training in Itanagar ON DEMAND.
The instructor for the Data Engineer Course in Itanagar at DataMites® is a qualified and experienced professional with expertise in data engineering and related fields. DataMites® ensures that their instructors have industry experience and possess in-depth knowledge of the subject matter.
Flexi-Pass is a concept offered by DataMites® that provides learners with the flexibility to access recorded sessions of their courses. It allows individuals to revisit or catch up on missed classes, providing convenience and ensuring that learners have comprehensive access to course content.
Yes, upon successful completion of the Data Engineer training from DataMites®, you will receive certifications. DataMites® offers industry-recognized certifications that validate your skills and knowledge in data engineering, enhancing your credibility and increasing your job prospects in the field.
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.