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
Applying engineering principles and techniques, data engineering encompasses the management of the entire data lifecycle. It encompasses activities such as data collection, ingestion, storage, processing, integration, and delivery, with a strong focus on scalability, reliability, and efficiency.
To pursue a career as a data engineer in Shillong, follow these steps:
Get a degree in computer science or a related field.
Learn programming languages like Python and databases like SQL.
Familiarize yourself with data storage and processing technologies.
Gain hands-on experience with data engineering tools and frameworks.
Understand data integration and ETL processes.
Stay updated with industry trends and advancements.
Build a portfolio of data engineering projects.
Network with professionals in the field.
Seek job opportunities and internships in Shillong.
Keep learning and upskilling in data engineering.
Moving from a mechanical background to data engineering is achievable with the right steps. Although having a computer science or related educational background can be advantageous, individuals can bridge the gap by acquiring essential skills like programming, database management, and data processing. Exploring specific data engineering training programs or pursuing relevant certifications can also boost your credibility and competence in this area.
Current developments and emerging patterns in the data engineering field include the increasing adoption of cloud-based data platforms and services like AWS and Azure, enabling scalable and efficient data processing. There is a growing focus on real-time data streaming and processing, leveraging technologies such as Apache Kafka and Flink. Data engineering is also witnessing advancements in data governance and privacy regulations, with organizations implementing stricter protocols. Additionally, there is a rising trend of utilizing automated data pipeline orchestration tools for streamlined and efficient data processing workflows.
The future prospects for individuals pursuing a career as data engineers are promising. With the increasing reliance on data-driven decision-making across industries, there is a growing demand for skilled professionals who can handle the complexities of data collection, processing, and analysis. As organizations continue to generate vast amounts of data, data engineers will play a vital role in designing efficient data infrastructure and ensuring the accuracy and reliability of data pipelines.
The fees for data engineer training in Shillong can vary depending on factors such as the institute chosen, the duration of the course, and the mode of training (online or classroom). On average, the cost may range from approximately 40,000 INR to INR 1,00,000. It is recommended to conduct thorough research on different training providers in Shillong to obtain precise information about the fees associated with their data engineer training programs.
DataMites is considered one of the top choices for Data Engineer Training. With their comprehensive curriculum, industry-relevant projects, and experienced instructors, DataMites provides high-quality training in data engineering. They have a strong track record of delivering excellent education and equipping individuals with the skills and knowledge needed to succeed in the field of data engineering.
Successful completion of Data Engineer Training in Shillong opens up career opportunities such as Data Engineer, Data Architect, Big Data Engineer, Data Warehouse Developer, or Data Integration Engineer. These job roles present diverse avenues for professionals to apply their data engineering skills and contribute to organizations' data management and analytics processes.
Excelling as data engineers necessitates key competencies like programming proficiency (Python, Java, etc.), expertise in database management (SQL, NoSQL), familiarity with big data processing frameworks (Hadoop, Spark), strong understanding of data integration and ETL processes, and the ability to work with data warehousing concepts.
The average salary range for Data Engineers in Shillong can vary depending on factors such as experience, skills, industry, and the organization's size. Generally, the average salary range for Data Engineers in Shillong falls between INR 3,00,000 to INR 8,00,000 per annum.
DataMites provides inclusive training programs that are aligned with industry requirements, led by experienced instructors, and emphasize practical projects and hands-on learning. This ensures that participants acquire the essential skills and knowledge to succeed in the field of data engineering.
The DataMites Certified Data Engineer Training program in Shillong encompasses subjects like the fundamentals of data engineering, database management, data warehousing, ETL processes, big data processing frameworks, data visualization, and advanced analytics techniques.
The duration of the DataMites Data Engineer Course in Shillong depends on the chosen learning mode. For online instructor-led training, it typically spans around 6 months, with over 150 learning hours. However, the duration may vary for self-paced learning options.
The cost of Data Engineer Training at DataMites in Shillong is not fixed and can fluctuate depending on factors such as the program of choice, training mode (online or classroom), and any additional resources or features included. The fees for the data engineer course at DataMites in Shillong typically vary between approximately INR 26,548 and INR 68,000, based on the specific program and any supplementary components provided.
DataMites' Flexi-Pass concept offers learners the flexibility to participate in multiple batches of the same course within a defined timeframe. This enables learners to revisit the course content, reinforce their understanding of concepts, and enhance their learning experience by diving deeper into the subject matter.
Typically, the prerequisites for enrolling in the Data Engineer Course at DataMites in Shillong include having a background in computer science, engineering, mathematics, or a related field.
Yes, participants who successfully complete Data Engineer training at DataMites receive multiple certifications. DataMites is associated with prestigious organizations like the International Association of Business Analytics Certifications (IABAC), NASSCOM FutureSkills Prime, and Jain (Deemed-to-be University), ensuring that the training programs meet industry standards and offer reputable certifications.
In the event that you miss a session during Data Engineer training at DataMites, they usually offer alternatives such as accessing recorded sessions or attending makeup sessions scheduled for a later date. This policy ensures that learners can catch up on missed content and continue their learning progress.
Yes, DataMites frequently offers the opportunity to attend a demo class prior to making the course fee payment. This allows individuals to familiarize themselves with the teaching style, engage with instructors, and gain an understanding of the course content and structure. It assists in making an informed decision before enrolling in the training program.
Absolutely, DataMites offers classroom-based training sessions for Data Engineer courses in Shillong. Learners have the choice to enroll in this training mode according to their preferences. Whether it is classroom or online training, DataMites guarantees a comprehensive learning experience with a focus on developing strong data engineering skills.
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.