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 encapsulates the intricate process of conceptualizing, constructing, and orchestrating the foundational infrastructure and systems necessary for the seamless collection, storage, processing, and analysis of expansive data sets. The overarching objective is to ensure the unfaltering availability, dependability, and accessibility of data, fostering an environment conducive to well-informed decision-making.
a. Cultivate a robust foundation in mathematical acumen, statistical prowess, and a command over programming languages.
b. Attain virtuosity in the art of data manipulation, adeptly managing databases, and seamlessly integrating diverse data streams.
c. Nurture expertise in cutting-edge big data technologies, navigating the intricacies of Hadoop, Spark, and various cloud platforms.
d. Assemble an impressive portfolio spotlighting a diverse array of data engineering projects, showcasing practical proficiency.
e. Embark on internships or secure entry-level positions in organizations that place a premium on the invaluable skill set of a data engineer.
f. Maintain an unwavering commitment to staying ahead of the industry curve by staying abreast of emerging technologies and evolving trends.
The trajectory towards becoming a seasoned data engineer is not a fixed journey, typically spanning from six months to two years. The timeline is influenced by individual circumstances and the chosen educational path, reflecting the nuanced nature of skill acquisition in this dynamic field.
a. Cultivate an exhaustive understanding of intricate data engineering concepts, leveraging hands-on experience with industry-standard technologies.
b. Witness a substantial uptick in job prospects, accompanied by an enhanced earning potential reflective of the acquired proficiency.
c. Forge a robust foundation poised for sustained career progression within roles that are inherently data-centric.
The financial investment associated with embarking on a data engineering training journey in Baner typically falls within the range of 40,000 INR to 1,00,000 INR. This investment varies based on factors such as the institute's reputation, program duration, and the depth of instructional content.
DataMites emerges as the paragon institute for data engineering training, distinguishing itself through a meticulously crafted curriculum, hands-on industry projects, and an instructional team boasting seasoned experts.
Post the crucible of training, a myriad of professional opportunities beckon, ranging from roles as diverse as a Data Engineer, Data Analyst, Big Data Engineer, ETL Developer, Database Administrator, to the coveted position of a Cloud Data Engineer. These opportunities span across industries, adding a layer of versatility to a data engineer's career.
a. Establish a foundational grasp of the mathematical, statistical, and programming principles that underscore the data engineering landscape.
b. Demonstrate an inherent familiarity with databases, coupled with an exhibition of proficiency in SQL, a cornerstone of data manipulation.
c. Showcase proficiency in at least one programming language, be it Python, Java, or a comparable dialect.
d. Exhibit an adept understanding of data manipulation techniques and analytical methodologies, essential for navigating the complex landscape.
Critical skills that form the bedrock of a data engineer's proficiency include an adept command of programming languages, mastery of SQL, a nuanced understanding of big data technologies, a penchant for data modeling, familiarity with the intricate architecture of cloud platforms, all underpinned by robust problem-solving capabilities and effective communication acumen.
The compensation landscape for Data Engineers in Pune is a dynamic one, contingent upon variables such as experience, skill set, industry dynamics, and the organizational context. On an average scale, Glassdoor reports an annual salary figure of INR ₹8,59,480 for Data Engineers in Pune, reflective of the growing significance attributed to their pivotal role.
For data engineering training in Baner, explore the comprehensive DataMites® program, available online and in-person, providing a well-rounded education for real-world applications.
The program covers data integration, modeling, ETL processes, data warehousing, big data technologies, and cloud platforms. Hands-on projects and real-world case studies enhance practical skills and understanding.
If you're looking for data engineer courses in Pune, DataMites conducts classroom training in strategic locations, including Baner and Kharadi. These diverse options are chosen for the convenience and accessibility of aspiring learners in the city.
Designed for those with a foundational understanding of mathematics, statistics, and programming, the course suits aspiring data engineers, IT professionals, software engineers, and those transitioning into data engineering roles.
The course spans approximately 6 months, with over 150 learning hours, ensuring a thorough exploration of the curriculum.
Online training provides flexibility, access to industry-expert instructors, hands-on assignments, real-world projects, interactive learning materials, and networking opportunities with a global community of learners.
The course fee, ranging from INR 26,548 to INR 68,000, varies based on the learning mode and additional services, representing a valuable investment in education and career development.
Yes, DataMites® provides classroom training for Data Engineer courses in Baner, allowing students to experience in-person learning and direct interactions with instructors and peers. Offline training is also available on demand.
Instructors at DataMites® in Baner are qualified professionals with practical experience and expertise in data engineering, ensuring a high-quality learning experience.
The Flexi-Pass allows learners to access recorded sessions, providing flexibility to revisit or catch up on missed classes for a comprehensive learning experience.
Upon completion, you'll receive industry-recognized certifications, including those from the International Association of Business Analytics Certifications (IABAC), validating your skills and knowledge with the prestige of IABAC accreditation.
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.