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Master the Fundamental Concepts of Data Engineer. Enroll today and become a skilled Data Engineer expert.
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4.5/5Course Duration
120 Hrs.Live Project
5 ProjectCertification Pass
GuaranteedTraining Format
Live Online /Self-Paced/ClassroomWatch Live Classes
Speciality
200+
Professionals Trained
3+
Batches every month
20+
Countries & Counting
100+
Corporate Served- The Data Engineer Course in Delhi is designed for individuals who wish to develop the skills needed to work with large datasets, build data pipelines, and ensure data systems are scalable, efficient, and reliable. This course covers fundamental topics like data warehousing, ETL (Extract, Transform, Load) processes, big data technologies, cloud computing, and programming languages like Python, SQL, and Spark. Data Engineer Training in Delhi prepares students for real-world data engineering challenges, making them industry-ready for top data roles.
Data Engineer Course in Delhi
- The Data Engineer Course in Delhi aims to equip students with the technical knowledge required for data management and engineering roles. By the end of Data Engineer Classes in Delhi, students will be proficient in building data systems, managing databases, and handling large-scale data processing.
Learn the basics of data engineering and how data flows within an organization.
Master key technologies like SQL, Hadoop, Spark, and cloud platforms (AWS, Azure).
Understand ETL processes, data warehousing, and data pipeline architecture.
Gain hands-on experience with real-time data processing and management.
Explore techniques for optimizing data storage, processing, and retrieval.
- Freshers who complete the Data Engineering Classes in Delhi can expect competitive salaries due to the high demand for skilled professionals in this field. Depending on the organization and skill level, the salary for a fresher typically ranges from 4.5 LPA to 7 LPA.
Entry-level data engineers: 4.5 - 7 LPA.
The salary may increase significantly with experience and expertise.
Professionals can earn higher salaries with proficiency in big data tools and cloud technologies.
- After completing the Data Engineer Course in Delhi, students can advance their careers in several directions. As a data engineer, you will have a clear career path with potential promotions to senior data engineer roles, data architect positions, or even managerial roles in data teams.
Junior Data Engineer Senior Data Engineer Data Architect.
With expertise, you can take leadership roles like Data Engineering Manager or Director of Data Engineering.
Continuous learning and certifications will lead to better job opportunities and higher salaries.
- The Data Engineering Training in Delhi is popular due to the growing demand for data professionals in various industries. Data engineering is the backbone of data analytics and machine learning, and as businesses collect more data, the need for qualified data engineers is increasing.
Data engineering is a high-demand, future-proof career.
The course provides in-depth knowledge of industry-leading tools and technologies.
Delhi is a tech hub with numerous job opportunities for data engineers.
The course offers hands-on training with real-world projects.
- Once you complete the Data Engineer Course in Delhi, youll be prepared to take on various job roles in the data field. Your role will revolve around building and maintaining robust data infrastructure, ensuring smooth data operations, and providing clean and usable data for analysis.
Building and maintaining data pipelines.
Managing databases and cloud infrastructure.
Ensuring data quality and availability for analytics teams.
Collaborating with data scientists to ensure effective data use.
Optimizing data storage and processing systems.
- Industries in Delhi and across India are constantly looking for skilled data engineers. The following sectors are particularly in need of data engineers:
Information Technology (IT): Companies developing software solutions.
Finance & Banking: To analyze and manage large amounts of transactional data.
E-commerce: Handling user data for analytics and business insights.
Healthcare: Managing patient data for research and decision-making.
Telecommunications: Analyzing data from customer networks and services.
- Upon completion of the Data Engineer Course in Delhi, students will receive a certificate that validates their skills and expertise in data engineering. This certificate enhances job prospects and serves as a proof of knowledge for potential employers.
- Our placement process is designed to connect students with top companies in the industry. We provide placement assistance, including interview preparation, resume building, and mock interviews to ensure students are ready for their job search.
Resume-building workshops and interview preparation sessions.
Collaboration with top tech companies for placements.
Dedicated placement cell to guide students through job applications.
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CURRICULUM & PROJECTS
Data Engineer- Professional Training Program
- Data types, loops, and conditionals
- File handling (CSV, JSON, TXT)
- Functions, lambda expressions, error handling
- Working with libraries: Pandas, NumPy
- Data manipulation and cleaning
- Working with APIs and JSON data
- Python for automation and scripting
- Basics of Scala syntax and structure
- Functional programming (map, reduce, filter)
- Case classes and immutability
- Using Scala with Spark (RDDs, DataFrames)
- OOP principles: class, object, inheritance
- Exception handling and file I/O
- Working with Hadoop Java APIs (MapReduce basics)
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- Spark architecture and cluster modes
- RDDs vs DataFrames vs Datasets
- Spark SQL: working with structured data
- Transformations and actions
- Spark Streaming: real-time data processing
- Machine learning pipelines with MLlib (intro level)
- Blocks and replication
- NameNode and DataNode
- File storage commands and usage
- Integration with Spark, Hive, Sqoop
- YARN architecture
- ResourceManager and NodeManager
- Application lifecycle and scheduling
- Mesos vs YARN
- Resource allocation and scheduling
- Deploying Spark/Flink on Mesos
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- Database design concepts
- SQL queries (SELECT, JOIN, GROUP BY)
- Indexing, constraints, normalization
- MySQL, PostgreSQL / PostGIS, Oracle
- Concepts: CAP Theorem, BASE vs ACID
- Key-Value, Document, Columnar types
- Data model, keyspaces, replication, CQL
- Write-heavy optimizations
- BSON, collections, indexing, aggregation framework
- CRUD operations and performance tuning
- Column-family model, integration with Hadoop
- Use cases for wide-column storage
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- Hive architecture: Metastore, Driver, Executor
- HiveQL syntax: DDL, DML, Joins, UDFs
- Partitions and Bucketing
- Integration with HDFS and Spark
- Cloud-based architecture: compute vs storage
- Warehouses, databases, and schemas
- Time Travel, cloning, and zero-copy restore
- Working with semi-structured data (JSON, XML)
- Performance tuning and caching
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- Producer-consumer architecture
- Topics, partitions, brokers
- Kafka Streams basics
- Kafka + Spark/Flink integration
- Stream vs batch processing
- Windows, time semantics (event vs processing time)
- Stateful operators
- Fault-tolerant streaming with checkpoints
- Storm architecture: Spouts and Bolts
- Building topologies
- Real-time analytics pipelines
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- DAGs, Tasks, Operators (PythonOperator, BashOperator, etc.)
- Scheduling and triggering jobs
- XComs, task dependencies, retries
- Airflow UI for monitoring and management
- Integrating with DBs, APIs, Spark, and cloud services
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- S3, IAM, EC2 basics
- Redshift for data warehousing
- AWS Glue for ETL
- EMR for big data
- Azure Data Factory (pipelines)
- Azure Blob Storage
- Azure Synapse for analytics
- BigQuery for analytics
- Google Cloud Storage
- Dataflow for stream and batch pipelines
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Get full course syllabus in your inbox
Get full course syllabus in your inbox
- Sqoop architecture and setup
- Import/export from MySQL, Oracle to HDFS/Hive
- Incremental loads (append, last-modified)
- Performance tuning using mappers
- Batch ETL Pipeline using Airflow + Hive + MySQL
- Real-Time Streaming using Kafka + Spark + MongoDB
- Cloud Data Pipeline using AWS S3 + Glue + Redshift
- System Design Case Study (architecture presentation)
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Course Duration
120 Hrs.
Weekday1 Hr/Day
Weekend2 Hr/Day
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