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 the collection, storage, processing, and analysis of large volumes of data. It focuses on building robust data pipelines, managing databases, ensuring data quality and integrity, and enabling efficient data processing and analysis for organizations to derive valuable insights and make data-driven decisions. Data engineering encompasses tasks such as data ingestion, data transformation, data integration, and the implementation of data infrastructure and technologies to support the organization's data needs.
To pursue a data engineering career in Raipur, consider the following steps:
a. Acquire a strong foundation in mathematics, statistics, and programming.
b. Learn programming languages such as Python or SQL.
c. Gain proficiency in database management systems and data manipulation techniques.
d. Familiarize yourself with big data technologies like Hadoop, Spark, and cloud platforms.
e. Enhance your skills through practical projects and real-world data engineering experience.
Benefits of undergoing training as a Data Engineer include:
a. Acquiring in-demand skills and knowledge in data engineering technologies and methodologies.
b. Enhancing job prospects and career opportunities in various industries.
c. Gaining practical experience through hands-on projects and industry-relevant case studies.
d. Keeping up with the latest trends and advancements in the field of data engineering.
The future outlook for data engineering is promising. With the exponential growth of data and the increasing reliance on data-driven decision-making, the demand for skilled data engineers is expected to continue rising. Data engineers play a crucial role in managing and processing data to extract meaningful insights and drive business outcomes.
Prerequisites for enrolling in a Data Engineer Course in Raipur may vary depending on the specific program and institute. Generally, a basic understanding of mathematics, statistics, and programming concepts is beneficial. Knowledge of databases, SQL, and programming languages like Python or Java can also be advantageous.
The cost of Data Engineer Training in Raipur can vary depending on factors such as the institute, program duration, and delivery mode (online or classroom). Typically, the fees for data engineer training in Raipur range from 40,000 INR to INR 1,00,000.
Yes, the choice of the best institute for data engineering training depends on various factors such as the curriculum, faculty expertise, industry connections, alumni reviews, and training delivery modes. DataMites is indeed considered one of the top institutes for data engineering training. They offer a comprehensive curriculum that covers the essential concepts, tools, and techniques of data engineering. The institute provides industry-relevant projects and hands-on experience to ensure practical learning. With experienced instructors, DataMites aims to provide a strong foundation in data engineering and equip students with the necessary skills and knowledge to excel in the field.
Upon completing Data Engineer Training, various job opportunities become available, such as Data Engineer, Database Administrator, ETL Developer, Big Data Engineer, and Cloud Data Engineer. These roles can be found in industries such as technology, finance, healthcare, e-commerce, and more.
While experience can be beneficial, some entry-level Data Engineer positions may be open to individuals with no prior experience. Starting as a junior data engineer or gaining practical experience through internships or projects can provide opportunities to enter the field and develop necessary skills.
Data Engineer Training is significant and valuable as it equips individuals with the skills and knowledge needed to excel in the field of data engineering. The training covers essential concepts, tools, and techniques, allowing individuals to build robust data pipelines, manage databases, and ensure efficient data processing and analysis. This expertise is crucial in today's data-driven world, where organizations rely on data for informed decision-making and business success.
To obtain data engineering training in Raipur, DataMites is the best place to consider. Datamites offers comprehensive data engineering courses that cover essential concepts, tools, and techniques. They provide industry-relevant projects, hands-on experience, and expert guidance to help you develop the necessary skills. With experienced instructors and a strong focus on practical learning, Datamites ensures that you gain a solid foundation in data engineering.
The DataMites Certified Data Engineer Training program in Raipur covers a wide range of topics including data engineering concepts, tools, and technologies such as Hadoop, Spark, SQL, and data pipeline development. It includes hands-on projects and practical exercises to enhance your skills.
Eligibility criteria for enrolling in the Data Engineer Course at DataMites® in Raipur may vary. Generally, 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.
The duration of the DataMites Data Engineer Course in Raipur 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.
Yes, DataMites® provides classroom training for Data Engineer courses in Raipur along with online training options. You can choose the training mode that suits your preferences and convenience.
The trainers for the Data Engineer Course in Raipur at DataMites® are experienced professionals with expertise in data engineering concepts, tools, and industry practices. They provide guidance, mentorship, and support throughout the training program.
DataMites® offers various training methods for data engineering courses, including instructor-led online training, classroom training, and self-paced learning options. You can select the method that aligns with your learning preferences and schedule.
Yes, DataMites® provides the option to attend demo classes before paying the course fee. This allows you to experience the teaching style, course content, and interact with instructors to make an informed decision.
Yes, DataMites® offers the flexibility to pay the course fee in installments. We understand the financial constraints some learners may have and provide the option to divide the course fee into manageable installments. This allows individuals to pursue their data engineering training while easing the financial burden.
DataMites® offers Data Engineer Courses with placement assistance in Raipur. They provide career support and guidance to students, which includes resume building, interview preparation, and connecting with potential employers to help secure job opportunities.
DataMites® accepts a range of payment methods for their training programs. You can make payments conveniently through online channels using credit cards, debit cards, net banking, and other digital payment options. This flexibility enables learners to choose the payment method that suits their preferences and ensures a smooth and secure transaction process.
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.