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 discipline that involves the design, development, and management of systems and processes to acquire, store, organize, process, and deliver data. It focuses on building and maintaining the infrastructure and architecture required for efficient and reliable data processing, ensuring data quality, integration, and accessibility. Data engineering plays a crucial role in enabling data-driven decision-making and supporting various data-intensive applications and analytics initiatives.
The timeframe for becoming a data engineer can vary depending on several factors, including the individual's prior experience, educational background, dedication to learning, and the intensity of training. Generally, it takes several months to a couple of years to acquire the necessary skills and knowledge to work as a data engineer. This timeframe involves gaining proficiency in areas such as data modeling, database management, ETL (Extract, Transform, Load) processes, big data frameworks, data warehousing, and other relevant technologies and tools.
The fees for Data Engineer Course in Gandhinagar can vary based on factors such as the training provider, duration of the course, and the extent of the curriculum. Generally, the cost of data engineer training in Gandhinagar falls within the range of approximately 40,000 INR to INR 1,00,000. It is advisable to conduct thorough research on various training providers in Gandhinagar to obtain accurate and detailed information regarding the specific costs of their courses.
Data engineering and data analytics are different but complementary fields. It is not a matter of one being better than the other, but rather depends on your interests, skills, and career goals. Data engineering focuses on designing and managing data infrastructure, while data analytics involves analyzing and interpreting data to derive insights. Both fields play crucial roles in the data ecosystem, and the choice between them depends on your specific interests and career aspirations.
The average salary range for Data Engineers in Gandhinagar can vary based on factors such as experience, skills, and the organization's size. Generally, Data Engineers in Gandhinagar can expect salaries ranging from INR 4,00,000 to INR 10,00,000 per year.
Data Science and Data Engineering are considered as distinct fields. While they are closely related and often work together, Data Science focuses on extracting insights and building predictive models from data, whereas Data Engineering primarily deals with the collection, storage, processing, and management of data infrastructure.
The requirements for enrolling in a Data Engineer Course in Gandhinagar can vary depending on the training provider and the specific program. However, a background in computer science, engineering, mathematics, or a related field is generally beneficial. Some courses may have prerequisites in programming, database management, or statistics.
While experience can enhance job prospects, individuals with no prior experience can still secure entry-level Data Engineer positions by demonstrating relevant skills, completing data engineering training programs, and showcasing practical projects or certifications that validate their knowledge and capabilities.
DevOps and data engineering are distinct fields but share some overlapping concepts. DevOps focuses on collaboration between software development and operations teams, aiming to streamline software development processes, while data engineering focuses on the management and processing of data infrastructure to support data-driven operations and analytics.
The curriculum of a data engineer course typically covers essential topics such as database management, data modeling, ETL (Extract, Transform, Load) processes, big data processing frameworks, data warehousing, data governance, and data integration. It may also include practical hands-on projects to develop proficiency in relevant tools and technologies used in the industry.
To obtain data engineering training in Gandhinagar, you can enroll in the Data Engineer Course offered by DataMites. DataMites is a reputable institute that provides comprehensive data engineering training programs. Our experienced instructors and industry-aligned curriculum will equip you with the necessary skills and knowledge to excel in the field of data engineering.
The key components of the DataMites Certified Data Engineer Training in Gandhinagar typically include comprehensive coverage of data engineering concepts, tools, and technologies. It may cover areas such as data modeling, database management, ETL processes, big data frameworks, data warehousing, data governance, and data integration. The program often includes practical projects and hands-on exercises to reinforce learning.
Eligibility criteria for enrolling in the Data Engineer Course at DataMites® in Gandhinagar may vary depending on the specific program. Generally, individuals with a background in computer science, engineering, mathematics, or related fields are eligible.
The duration of the DataMites Data Engineer Course in Gandhinagar is flexible and depends on the learning mode chosen by the participant. For online instructor-led training, the typical duration is around 6 months with more than 150 learning hours. However, the duration may vary for self-paced learning options.
DataMites® follows a certification process to validate course completion. Upon successfully fulfilling the requirements of the Data Engineer training program, you will receive a certificate from DataMites®. The certification demonstrates your proficiency and completion of the course.
DataMites® offers Data Engineer Courses with placement assistance in Gandhinagar. They aim to provide support to participants in securing suitable job opportunities in the field of data engineering. The specific details of the placement assistance can be obtained from DataMites® directly.
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.
Upon successfully completing Data Engineer training from DataMites®, you will be awarded multiple certifications. DataMites® is affiliated with renowned organizations such as the International Association of Business Analytics Certifications (IABAC), NASSCOM FutureSkills Prime, and Jain (Deemed-to-be University). These affiliations guarantee that the training programs meet industry standards and offer recognized certifications, validating your expertise in data engineering.
The documentation requirements for training sessions at DataMites may differ depending on the specific course and program. Generally, it is recommended that participants bring a valid identification proof, such as a government-issued ID card, along with any specific documents mentioned in the communication received from DataMites.
DataMites® usually has a policy in place to address missed sessions during Data Engineer training. They may offer options to access recorded sessions or provide opportunities to attend makeup sessions to cover the missed content.
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.