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 can be defined as the practice of designing, developing, and managing systems and processes that enable the acquisition, storage, organization, processing, and delivery of data. Its primary focus is on creating and maintaining the infrastructure and architecture required for efficient and reliable data processing. Data engineering also involves ensuring data quality, integration, and accessibility. By facilitating these aspects, data engineering supports data-driven decision-making and enables the implementation of various data-intensive applications and analytics projects.
The average timeline for becoming a data engineer can differ based on individual circumstances, including prior experience, educational background, learning dedication, and training intensity. Typically, it takes several months to a couple of years to develop the essential skills and knowledge required for a data engineer role. This timeframe involves gaining expertise in areas such as data modeling, database management, ETL processes, big data frameworks, data warehousing, and other pertinent technologies and tools.
Data Engineer Training in Dehradun is priced differently based on factors such as the training provider, course duration, and curriculum scope. Typically, the cost of data engineer training in Dehradun falls within the range of approximately 40,000 INR to INR 1,00,000. For accurate and detailed information regarding the specific costs of courses, it is advisable to conduct thorough research on various training providers operating in Dehradun.
Both data engineering and data analytics hold significant value in their respective domains. It is not a question of one field being more valuable than the other but rather depends on individual preferences, skills, and career goals. Data engineering focuses on building and managing data infrastructure, while data analytics involves analyzing and extracting insights from data. Both fields contribute to the overall data ecosystem, and the choice between them should align with your specific interests and career aspirations.
The general salary range for Data Engineers in Dehradun varies based on factors such as experience, skills, and the scale of the organization. Typically, Data Engineers in Dehradun can anticipate salaries falling within the range of approximately INR 4,00,000 to INR 10,00,000 per year.
Qualification requirements for enrolling in a Data Engineer Course in Dehradun can vary depending on the specific program and training provider. However, having a background in computer science, engineering, mathematics, or a related field is generally beneficial. Some courses may also specify prerequisites in areas like programming, database management, or statistics.
Yes, Data Science and Data Engineering are separate domains with distinct focuses. Data Science involves extracting insights and building predictive models from data, while Data Engineering primarily deals with tasks related to data collection, storage, processing, and managing data infrastructure. While they collaborate closely, they are considered as distinct fields within the broader realm of data-related disciplines.
No, DevOps and data engineering are not interchangeable terms. While there may be some overlap in concepts, they represent separate fields. DevOps focuses on fostering collaboration between software development and operations teams, whereas data engineering centers around managing and processing data infrastructure to enable data-driven operations and analytics. Although related, they have distinct objectives and responsibilities.
Yes, it is possible for individuals with no prior experience to secure entry-level Data Engineer job positions. While experience can be advantageous, individuals can increase their chances by acquiring relevant skills through training programs, showcasing practical projects, and obtaining data engineer certifications that demonstrate their knowledge and capabilities in data engineering.
The curriculum of a data engineer course usually encompasses key subjects such as database management, data modeling, ETL (Extract, Transform, Load) processes, big data processing frameworks, data warehousing, data governance, and data integration. Additionally, practical hands-on projects may be incorporated to enhance proficiency in the industry's prevalent tools and technologies.
If you are seeking data engineering training in Dehradun, one option is to enroll in the Data Engineer Course provided by DataMites. DataMites is a well-established institute known for its comprehensive training programs in data engineering. With highly experienced instructors and a curriculum designed to meet industry requirements, DataMites can equip you with the essential skills and knowledge needed to excel in the field.
The Data Engineer Course at DataMites® in Dehradun is open to individuals who meet the eligibility criteria, which typically includes having a background in computer science, engineering, mathematics, or a related field.
The DataMites Certified Data Engineer Training program conducted in Dehradun incorporates key components essential for a thorough understanding of data engineering. These components usually encompass data modeling, database management, ETL processes, big data frameworks, data warehousing, data governance, and data integration. The program also emphasizes practical projects and hands-on exercises to reinforce learning outcomes.
The average timeframe for completing the DataMites Data Engineer Course in Dehradun varies based on the learning mode chosen. Typically, for online instructor-led training, the course duration is around 6 months, requiring more than 150 learning hours. However, the timeframe may vary for self-paced learning options.
To obtain a course completion certificate from DataMites®, participants need to fulfill the specified requirements of the Data Engineer training program. Once the requirements are met, DataMites® will issue a certificate, certifying the individual's completion of the course. This certificate serves as a testament to their expertise and successful completion of the training program.
Yes, DataMites® in Dehradun offers Data Engineer Courses that come with placement assistance. They are committed to helping participants secure relevant job placements in the field of data engineering. For specific details regarding the extent of placement support provided, it is recommended to contact DataMites® directly.
DataMites® offers the Flexi-Pass concept, which grants learners the flexibility to participate in multiple batches of the same course within a designated timeframe. This enables learners to revisit course content, reinforce their understanding of concepts, and enhance their learning. The Flexi-Pass allows individuals to delve deeper into the subject matter and solidify their knowledge.
Yes, individuals who successfully complete Data Engineer training from DataMites® are granted various certifications. DataMites® has established partnerships with esteemed organizations, including the International Association of Business Analytics Certifications (IABAC), NASSCOM FutureSkills Prime, and Jain (Deemed-to-be University). These affiliations ensure that the training programs meet industry benchmarks and bestow certifications that are highly regarded, serving as a testament to the individuals' data engineering skills.
The documentation requirements for training sessions at DataMites® can vary depending on the course and program. As a general guideline, it is recommended to bring a valid identification proof, such as a government-issued ID card, to the training session. To obtain precise details about any additional papers or documents necessary, participants should refer to the communication received from DataMites®.
At DataMites®, there is a procedure in place to address situations where participants miss a session during Data Engineer Training in . Generally, they offer solutions such as access to recorded sessions or the option to attend makeup sessions. These provisions enable participants to cover any missed content and maintain continuity in their learning experience.
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.