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 involves the design, development, and management of systems and processes for collecting, storing, processing, and analyzing large volumes of data. It focuses on ensuring data reliability, efficiency, and availability for effective data-driven decision-making.
To pursue a career as a data engineer in Panaji, you can follow these steps:
a. Obtain a degree in computer science, engineering, or a related field.
b. Develop proficiency in programming languages such as Python, SQL, or Java.
c. Gain knowledge of database management systems and data processing frameworks like Hadoop and Spark.
d. Acquire practical experience through internships, projects, or working on data-related tasks.
e. Continuously update your skills by staying informed about emerging technologies and industry trends.
Yes, it is possible to transition from the mechanical domain to data engineering. While a background in computer science or a related field may provide a smoother transition, you can bridge the gap by acquiring relevant skills such as programming, database management, and data processing. Additional training or data engineer certification programs specific to data engineering can also be beneficial.
Emerging trends in data engineering include:
a. Adoption of cloud-based data platforms and services.
b. Integration of artificial intelligence and machine learning in data processing and analysis.
c. Increased focus on real-time data streaming and processing.
d. Implementation of data governance and data privacy regulations.
e. Utilization of automated data pipeline orchestration tools.
The future prospects for individuals pursuing a career as data engineers are promising. With the exponential growth of data and the increasing reliance on data-driven decision-making, there is a growing demand for skilled data engineers across industries. Companies need professionals who can handle complex data infrastructure, process large datasets, and extract meaningful insights, leading to a wide range of career opportunities.
The cost of Data Engineer Training in Panaji may vary depending on factors such as the institute, course duration, and training mode (online or classroom). Typically, the fees for data engineer training in Panaji can range from approximately 40,000 INR to INR 1,00,000. It is recommended to research different training providers in Panaji to determine the specific costs associated with their courses.
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. We 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.
After completing Data Engineer Training, one can expect job roles such as Data Engineer, Database Administrator, ETL Developer, Big Data Engineer, Cloud Data Engineer, or Data Warehouse Engineer. These roles can be found in industries like technology, finance, healthcare, e-commerce, and more.
Essential skills for a successful data engineer include:
a. Proficiency in programming languages like Python, SQL, or Java.
b. Knowledge of database management systems and data modeling.
c. Experience with big data processing frameworks like Hadoop, Spark, or Apache Kafka.
d. Understanding of data integration and ETL (Extract, Transform, Load) processes.
e. Strong problem-solving and analytical skills.
f. Familiarity with cloud platforms and data warehousing concepts.
g. Effective communication and collaboration skills.
The average salary range for Data Engineers in Panaji can vary depending on factors such as experience, skills, industry, and the organization's size. Generally, Data Engineers in Panaji can expect salaries ranging from INR 4,00,000 to INR 10,00,000 per year.
Choosing DataMites for Data Engineer Training in Panaji offers several advantages. They provide comprehensive and industry-relevant training programs that cover essential data engineering concepts, tools, and techniques. With experienced instructors, practical projects, and hands-on learning, DataMites ensures that you gain the necessary skills and knowledge to excel in the field of data engineering.
The DataMites Certified Data Engineer Training program in Panaji covers a wide range of topics, including data engineering fundamentals, database management, data warehousing, ETL (Extract, Transform, Load) processes, big data processing frameworks, data visualization, and advanced analytics techniques.
The duration of the DataMites Data Engineer Course in Panaji varies based on the learning mode selected. Typically, online instructor-led training lasts for approximately 6 months, comprising more than 150 learning hours. However, the duration may differ for self-paced learning alternatives.
The cost of Data Engineer Training at DataMites in Panaji can vary depending on factors such as the program, training mode (online or classroom), and any additional features or resources included. The fees for the data engineer course at DataMites in Panaji range from approximately INR 26,548 to INR 68,000, depending on the specific program and any additional features included.
The Flexi-Pass concept offered by DataMites provides learners with the flexibility to attend multiple batches of the same course within a specified timeframe. This allows learners to review the course content, revise concepts, and reinforce their learning. It provides an opportunity to revisit the course material and gain a deeper understanding of the subject.
The eligibility criteria to enroll in the Data Engineer Course at DataMites® in Panaji generally include a background in computer science, engineering, mathematics, or related fields.
Yes, upon completion of Data Engineer training from DataMites®, you will receive multiple certifications. DataMites® is affiliated with esteemed organizations such as 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 standards and provide recognized certifications.
If you miss a session during Data Engineer training at DataMites®, they typically provide options to access the recorded sessions or attend a makeup session at a later date. DataMites aims to ensure that learners have the opportunity to cover missed content and continue their learning journey.
DataMites® often provides the option to attend a demo class before making the course fee payment. This allows you to experience the teaching style, interact with instructors, and get a glimpse of the course content and structure. It helps in making an informed decision before committing to the training program.
DataMites® offers both online and classroom training options for Data Engineer courses in Panaji. Learners can choose the training mode that best suits their preferences and schedule. Both modes provide high-quality instruction and practical learning experiences to help you master 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.