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 field of designing, developing, and maintaining the infrastructure and systems necessary for the efficient and reliable processing, storage, and analysis of large volumes of data. It involves building pipelines, data warehouses, and data integration frameworks to support data-driven decision-making.
Undergoing Data Engineer Training in Gwalior offers several advantages, including gaining in-depth knowledge of data engineering concepts and techniques, hands-on experience with industry-standard tools and technologies, improved job prospects and career opportunities, and the ability to contribute effectively to data-driven projects within organizations.
The prerequisites for enrolling in a Data Engineer Course in Gwalior may vary depending on the specific institute or program. However, typically, basic knowledge of programming languages, databases, and SQL is beneficial. A background in computer science or related fields can also be advantageous.
When selecting the ideal institute for data engineering training, it is essential to consider factors such as the curriculum, faculty expertise, industry affiliations, feedback from alumni, and training options. DataMites consistently emerges as a top choice for data engineering training. Through its inclusive curriculum, industry-centric projects, and knowledgeable instructors, the institute provides a strong foundation in data engineering, equipping students with the necessary skills and knowledge to succeed in this field.
After completing Data Engineer Training, potential job roles include Data Engineer, Data Architect, Big Data Engineer, ETL Developer, Database Developer, Data Warehouse Engineer, and Data Integration Specialist. These roles involve designing, building, and maintaining data systems and infrastructure within organizations.
Depending on the institute, program duration, delivery method (online or classroom), and added benefits, the cost of Data Engineer Training in Gwalior may differ. As a general guideline, the fees for data engineer training in Gwalior typically range from 40,000 INR to INR 1,00,000.
Essential skills for a data engineer include proficiency in programming languages such as Python, Java, or Scala, expertise in SQL and database management systems, knowledge of data modeling and ETL (Extract, Transform, Load) processes, familiarity with big data technologies like Hadoop and Spark, and understanding of cloud platforms and data warehousing concepts.
The educational requirements for a data engineering career typically include a bachelor's degree in computer science, information technology, or a related field. However, some positions may require a master's degree or higher level of education, depending on the organization and the complexity of the data engineering tasks involved.
A postgraduate degree is not necessarily required for Data Engineer Training. While a bachelor's degree is typically the minimum educational requirement, individuals can enhance their knowledge and skills in data engineering through specialized training programs, certifications, and hands-on experience.
The choice of programming language for data engineering depends on the specific requirements and the technology stack used within an organization. However, popular languages for data engineering include Python, Java, and Scala, as they offer extensive libraries, frameworks, and toolkits for data processing, manipulation, and analysis.
To acquire training in data engineering in Gwalior, you have the option to enroll in reputable institutes like DataMites®. They offer comprehensive data engineering courses in Gwalior through online or classroom modes, providing hands-on training, practical projects, and expert guidance to develop your data engineering skills.
The DataMites Certified Data Engineer Training in Gwalior covers a wide range of topics, including data engineering concepts, tools, and technologies such as Hadoop, Spark, and SQL. The training program comprises interactive sessions, practical assignments, and real-world case studies to enhance your understanding and proficiency in data engineering.
The duration of the DataMites Data Engineer Course in Gwalior can vary depending on the chosen learning mode. Generally, for online instructor-led training, the course duration is typically around 6 months with 150+ learning hours, while the duration may differ for self-paced learning options.
The cost of DataMites Data Engineer Training in Gwalior can vary, ranging from approximately INR 26,548 to INR 68,000.
Yes, upon successful completion of the Data Engineer training from DataMites®, you will receive certifications. DataMites offers globally recognized certifications from organizations such as IABAC (International Association of Business Analytics Certifications), NASSCOM FutureSkills Prime, and JainX in collaboration with Jain (Deemed-to-be) University.
The eligibility criteria for enrolling in the Data Engineer Course in Gwalior at DataMites® may vary. Generally, individuals with a background in computer science, mathematics, or related fields, as well as professionals aspiring to work in data engineering roles, are eligible to enroll.
The duration to become certified by the IABAC (International Association of Business Analytics Certifications) can vary depending on the specific certification and individual preparation. It typically requires rigorous study, practical experience, and successfully passing the certification examination.
The specific documents required for the training session at DataMites may vary based on the course and program. Typically, participants are advised to bring a valid ID proof, such as a government-issued ID card, and any specific documents mentioned in the communication received from DataMites.
DataMites® accepts various payment methods for their training programs, including online payment through credit cards, debit cards, net banking, and other digital payment options.
Yes, upon successful completion of the Data Engineer Course from DataMites®, you will receive a Data Engineer Course Completion Certificate. This certificate acknowledges your successful completion of the course and serves as evidence of your proficiency in data engineering concepts and techniques.
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.