PG Program in Data Engineering
- Program Highlights
- Program Coverage
- Program Fee
Schedule and Registration
Basics of Data Engineering
Scripting Language Requirement: Unix / Linux
Basic Language Requirement: Python, Java & Scala
Working Knowledge of Operating Systems
Deep Database Knowledge – SQL, and NoSQL
Data Warehousing – Hadoop, MapReduce, HIVE, Hbase, PIG,
Apache Spark, Kafka
Familiarity with basic Data Mining Methodologies
Stream Data processing from IoT
Re-engineer Enterprise Data Architecture without hampering BAU
Work with relational and NoSQL data models
Create scalable and efficient data warehouses
Work efficiently with massive datasets
Build and interact with a Cloud-based data warehouse
Automate and monitor data pipelines
Develop proficiency in Stream Processing using Cloud Data Lake
Solve the appropriate us cases using big data technologies
The list of tools used depends on the volume of this data, the speed of their arrival and heterogeneity.
Good companies which innovate and compete on data like Netflix, LinkedIn, Amazon & Google expects to have knowledge of coding, Data Structures and Algorithms complexity.
Praxis Placement Program
A data engineer is responsible for providing the reliable infrastructure of the data. A Data Engineering group is accountable for the entire ownership of the data, namely, their acquisition, storage, permission, delivery and processing. These data engineers ensure a smooth flow of data between systems and processes.
ETL (Extract, Transform, and Load) are the steps which a data engineer follows to build the data pipelines. ETL is essentially a blueprint for how the collected raw data is processed and transformed into data ready for analysis.
Data engineers are expected to know a fair bit of programming and familiarity with scripting. It is desirable (though not mandatory) to have engineering background. An acumen towards technology is a necessary requirement to succeed in the job.
- ETL Engineer
The ETL engineer is responsible to maintain the veracity of the data in the source and target system. They ensure that the right kind of tools, permission and system pipelines are in place for smooth transfer of the data.
- Database Administrator
This role requires extensive knowledge of traditional as well as the new-age NoSQL and Cloud databases. They ensure that the data generating and the data ingesting systems are up and running in a live business scenario.
- Data Engineer
A data engineer lays down the foundation for data management systems to ingest, integrate and maintain all the data sources. The person needs to have working database knowledge and also needs to understand the needs of the business and its long time data scalability needs. This role requires knowledge of tools like SQL, XML, Hive, Pig, Spark etc.
- Enterprise Data Architect
The master of the lot. An Architect needs to have knowledge of database tools, languages like Python, Java and Scala, distributed systems like Hadoop, among other things. It’s a combination of tasks of Database Administrator & Data Engineer into one single role.
Responding to Industry needs
Diagram adapted from Monica Rogati’s excellent article, ‘The AI Hierarchy of Needs’
- Comprehensive 550 hours full-time Data Engineering programme for Enterprises handling massive Datasets
- Includes Agile, DevOps and Design Thinking as part of the learning process
- Specially designed workshops for Corporate Communication skills and Strorytelling with Data
|Working with traditional Data (Trim I)||Engineering Platforms for Big Data (Trim II)||Running Enterprise business on Cloud (Trim III)|
|Concepts of Data Modeling||Introduction to Hadoop Ecosystem||Understand how enterprise businesses generate data|
|Java Programming||Working with NoSQL Databases||Automate Data Pipelines with Apache Airflow|
|Algorithms & Data Structure||Big Data middlewares (Sqoop)||Data Stream Analytics with Apache Storm|
|Python functional programming (self-paced)||Fundamentals of Scala & Spark||IoT & Sensor Data – Acquisition, Management & Application|
|Operating Systems basics (Unix)||Server management with Zookeeper||Building a Data Lake with Apache Spark|
|Introduction to RDBMS||Data Pipelines using Apache Kafka||Data Security & Privacy|
|SQL programming||Data Visualization using D3JS||Running a Data Lake from the Cloud using Devops|
|ETL Concepts||Application orchestestration – Dockers & Kubernets|
Migration of a Traditional DW (PostGres based) to a Cloud Big Data Data Warehouse. Building an orchestrated application on the cloud for managing sensor-data. Real-time Streaming feeds analysing using Kafka & Storm and Real-time Visualization using D3JS
Electives & VACs
Area Of Concentration
All figures in Indian National Rupees
|Particulars||Academic Fee||GST||Refundable Deposit||Installment|
*Academic Fee includes Tuition, , Library and Academic activity fee.
Refunds: After admission the entire amount paid by the student shall stand forfeited except the refundable deposit mentioned above. This applies to dismissals as well as withdrawals, voluntary or otherwise, from the institute’s rolls.
Laptop: Each student must have a wi-fi enabled personal laptop before the start of classes. For the data engineering program, the laptop has to be a 64 bit machine and should be equipped with minimum of 6 GB RAM.
Educational Loans: Praxis has tied up with Credila and Avanse Financial Services for student loans, details of which would be furnished on selection.
Disputes: Any disputes are subject to the jurisdiction of Kolkata courts only.
- B.Tech, B. Sc/M. Sc Computer Science /IT, BCA or MCA
- Professionals trying to switch careers
- 50% marks throughout career
- Familiarity with Computer systems
The 9 months Full Time Post Graduate Program In Data Engineering (PGP DE) is offered from the Bangalore Campus of Praxis Business School.