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 explained as the practice that involves the design, development, and management of systems and processes to handle and analyze large amounts of data effectively. It emphasizes the creation of reliable data pipelines, ensuring the quality and integrity of data, and enabling data-driven decision-making. Data engineering plays a crucial role in the collection, organization, and utilization of data for various applications and business insights.
Individuals aspiring to pursue a data engineering career in Imphal can take the following steps:
Lay a solid foundation in mathematics, statistics, and programming.
Gain proficiency in programming languages like Python or SQL.
Develop expertise in database management systems and data manipulation techniques.
Familiarize themselves with big data technologies such as Hadoop and Spark.
Enhance their skills through hands-on projects and practical experience.
Certainly, data engineering holds promising prospects for the future. The growing importance of data-driven decision-making and the exponential growth of data volumes have created a strong demand for skilled data engineers. The role of data engineering in handling and processing data efficiently to extract valuable insights and drive business outcomes positions it as a field with abundant opportunities and a bright future ahead.
Undergoing training in data engineering offers several advantages for individuals, including:
Developing valuable skills and knowledge in the field of data engineering.
Expanding career opportunities in diverse industries.
Gaining practical experience through project-based learning.
Keeping abreast of industry trends and technological advancements.
Joining a data engineer course in Imphal typically requires meeting certain criteria that may vary depending on the course and institution. Generally, it is beneficial to have a basic understanding of mathematics, statistics, and programming. Familiarity with databases, SQL, and programming languages such as Python or Java can also be advantageous for a more seamless learning journey.
Prerequisites for enrolling in a data engineer course in Imphal may vary depending on the specific program and institution. However, a basic understanding of mathematics, statistics, and programming concepts is generally beneficial. Knowledge of databases, SQL, and programming languages like Python or Java can also be advantageous.
Individuals considering data engineer training in Imphal should anticipate costs that may vary based on factors like the training institution, program duration, and training mode (online or classroom). Generally, the fees for data engineer training in Imphal can be expected to range from approximately 40,000 INR to 1,00,000 INR.
For data engineer training, DataMites comes highly recommended as an institute of choice. They provide a comprehensive curriculum, industry-relevant projects, and experienced instructors who equip students with the essential skills and knowledge required in data engineering.
After undergoing data engineer training, individuals can embark on various career paths, including opportunities as Data Engineers, Database Administrators, ETL Developers, Big Data Engineers, and Cloud Data Engineers. These career options span industries such as technology, finance, healthcare, and e-commerce.
Data engineer job positions are not exclusively limited to individuals with prior experience. Entry-level roles or positions targeted at individuals with limited experience are available in the data engineering field. By participating in internships or gaining practical experience through projects, individuals without experience can showcase their skills and competence to secure data engineer job positions.
Data engineer training adds substantial value by equipping individuals with the skills and knowledge required for success in the field. It encompasses crucial concepts, tools, and techniques involved in developing robust data pipelines, managing databases, and ensuring efficient data processing and analysis. This proficiency is highly coveted in the modern business landscape, where data plays a central role in driving growth, innovation, and competitiveness.
If you're looking to obtain data engineering training in Imphal, one option is to enroll at DataMites. They offer comprehensive courses that cover essential data engineering concepts, tools, and techniques. With a focus on practical learning, hands-on experience, and expert guidance, DataMites provides a strong foundation in data engineering.
The DataMites Certified Data Engineer Training program in Imphal provides instruction in a variety of subjects. These include data engineering concepts, tools, and technologies like Hadoop, Spark, SQL, and data pipeline development. The program also incorporates practical exercises and hands-on projects to ensure a comprehensive understanding and practical application of these topics.
The requirements for enrolling in the Data Engineer Course at DataMites® in Imphal can vary. Typically, 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.
Yes, DataMites® conducts classroom training for Data Engineer Courses in Imphal, in addition to their online training offerings. They ensure flexibility by providing individuals the option to select the training mode that suits their needs and preferences.
The instructors for the Data Engineer Course in Imphal at DataMites® have a strong background in data engineering. They possess expertise in data engineering concepts, tools, and industry practices, allowing them to provide valuable instruction and guidance to participants throughout the training program.
DataMites® offers a variety of learning formats for data engineering courses, including instructor-led online training, classroom training, and self-paced learning. These options allow individuals to select the learning format that best meets their preferences and time constraints.
Yes, at DataMites®, individuals are given the opportunity to attend demo classes before they are required to pay the course fee. This allows them to get a preview of the teaching style, course content, and interact with instructors, empowering them to make an educated choice.
Yes, at DataMites®, individuals have the option to pay the course fee in installments. This flexible payment arrangement considers the financial constraints that learners may face, allowing them to manage the course fee while pursuing their data engineering training.
Certainly, DataMites® offers placement support for individuals enrolled in Data Engineer Training in Imphal. They extend valuable assistance in terms of career guidance, resume preparation, interview readiness, and facilitating job placements to enhance participants' career prospects.
DataMites® offers flexible payment options for their training programs, providing learners with convenience and ensuring secure transactions. Accepted payment methods include credit cards, debit cards, net banking, and other digital payment options. Through their online channels, learners can choose the payment method that aligns with their preferences, facilitating a smooth and seamless transaction process while maintaining security.
The expected length of the DataMites Data Engineer Course in Imphal depends on the chosen learning mode. On average, online instructor-led training spans approximately 6 months, with over 150 learning hours. However, the duration may differ for self-paced learning options.
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.