Data Engineering Certificate Course India
- Accredited by NASSCOM, Approved by Government of India
- 80 Hours Assignments & Real-Time Projects
- Complementary Hadoop and Spark
- Complementary ML on Cloud
- Complementary Python Programming
- Enroll and avail Government of India (GOI) Incentives after successfully clearing the mandatory Future Skills Prime Assessment
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360DigiTMG Certified Data Science Program in association with Future Skills Prime accredited by NASSCOM, approved by the Government of India. Certified Course on Data Engineering explores the various tools Data Engineers use and find out the difference in the job responsibilities of a Data Scientist and a Data Engineer. It expands learners' understanding of the various skills involved in knowing tools like Python, Spark, Kafka, and Jupyter. Spyder, TensorFlow, Keras, PyTorch, etc., with advanced SQL techniques. Students get a chance to extract raw data from various data sources in multiple formats and transform them into actionable insights, and deploy them into a single, easy-to-query database. They learn to handle huge data sets and build data pipelines to optimize processes for big data. Students get a chance to dive deeper into advanced data engineering projects that will help in gaining practical experience.
Data Engineering Course Overview
Explore the various tools data engineers use and find out the difference in the job responsibilities of a Data Scientist and a Data Engineer. Expand your understanding of the various skills involved in to know Get exposed to tools like Python, Spark, Kafka, Jupyter. Spyder, TensorFlow, Keras, PyTorch, etc. along with advanced SQL techniques. Extract raw data from various data sources in multiple formats and then transform them into actionable insights, and deploy them into a single, easy-to-query database. Learn to handle huge data sets and build data pipelines to optimize processes for big data. Dive deeper into advanced data engineering projects which will help you gain practical experience and skills.
What is Data Engineering?
A Data Engineer collects and transforms data to empower businesses to make data-driven decisions. He has to pay attention to security and compliance; reliability and fidelity; scalability and efficiency; and flexibility and portability while designing, operationalizing and monitoring data processing systems.
Data Engineering Training Learning Outcomes
These modules will lay out the foundation for data science and analytics. The core of Data Engineering involves an understanding of various techniques like data modelling, building data engineering pipelines, and deploying the analytics models. Students will learn how to wrangle data and perform advance analytics to get the most value out of data. As you progress, you'll learn how to design as well as build data pipelines and work with big data of diverse complexity and production databases. You will also learn to extract and gather data from multiple sources, build data processing systems, optimize processes for big data, build data pipelines, and much more. With this course develop skills to use multiple data sources in a scalable way and also master the skills involved in descriptive and inferential statistics, interactive data analysis, regression analysis, forecasting, and hypothesis testing. Also, learn to
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Who Should Sign Up?
- Science, Maths, Compute Graduates
- IT professionals who want to Specialize in Digital Tech
- SQL and related developers or software developers
- Students/IT professionals having an interest in Data and Databases
- Professionals working in the space of Data Analytics
- Academicians and Researchers working in the space of Data Analytics/Science
- Cloud and BigData enthusiasts
Data Engineering Course Syllabus
These modules on Data Engineering are designed to ensure that they are at par with the current industry requirements for Data Engineers. All the modules will wrap up with hands-on practice using real tools and real-time multiple databases. With these modules you will learn to manage, load, extract, and transform data to facilitate delivering of results that your organization can leverage. You will also learn to master the core skills of cleansing, and migrating data.
- Intro to Data Engineering
- Data Science vs Data Engineering
- Building Data Engineering Infrastructure
- Working with Databases and various File formats (Data Lakes)
- SQL
- MySQL
- PostgreSQL
- NoSQL
- MongoDB
- HBase
- Apache Cassandra
- Cloud Sources
- Microsoft Azure SQL Database
- Amazon Relational Database Service
- Google Cloud SQL
- IBM Db2 on Cloud
- SQL
- Extra-Load, Extract-Load-Transform, or Extract-Transform-Load paradigms
- Preprocessing, Cleaning, and Transforming Data
- Cloud Data Warehouse Service
- AWS: Amazon Redshift
- GCP: Google Big Query
- IBM: Db2 Warehouse
- Microsoft: Azure SQL Data Warehouse
- Distributed vs. Single Machine Environments
- Distributed Framework - Hadoop
- Various Tools in Distributed Framework to handle BigData
- HBase
- Kafka
- Spark
- Apache NiFi
- Distributed Computing on Cloud
- ML and AI platforms on Cloud
- Various Tools in Distributed Framework to handle BigData
- Databases and Pipelines
- Data Pipeline
- Features of Pipelines
- Building a pipeline using NiFi
- Data Pipeline
- Installing and Configuring the NiFi Registry
- Using the Registry in NiFi
- Versioning pipelines
- Monitoring pipelines
- Monitoring NiFi using GUI
- Using Pything with the NiFi REST API
- Building pipelines in Apache Airflow
- Airflow boilerplate
- Run the DAG
- Run the data pipelines
- Deploy and Monitor Data Pipelines
- Production Data Pipeline
- Creating Databases
- Data Lakes
- Populating a data lake
- Reading and Scanning the data lake
- Insert and Query a staging database
- Building a Kafka Cluster
- Setup Zookeeper and Kafka Cluster
- Configuring and Testing Kafka Cluster
- Streaming Data with Apache Kafka
- Data Processing with Apache Spark
- Real-Time Edge Data with MiNiFi, Kafka, and Spark
- Data Science vs Data Engineering
- Data Engineering Infrastructure and Data Pipelines
- Working with Databases and various File formats (Data Lakes)
- SQL
- MySQL
- PostgreSQL
- NoSQL
- MongoDB
- HBase
- Cloud Sources
- Microsoft Azure SQL Database
- Amazon Relational Database Service
- Google Cloud SQL
- SQL
- Python Programming
- Getting started with Python programming for Data Processing
- Data Types
- Python Packages
- Loops and Conditional Statements
- Functions
- Collections
- String Handling
- File handling
- Exceptional Handling
- MySQL Integration
- INSERT, READ, DELETE, UPDATE, COMMIT, ROLLBACK operations
- MongoDB Integration
- Pre-processing, Cleaning, and Transforming Data
- Data Lake Cloud offerings
- Cloud Data Warehouse Service
- Apache Hadoop
- Pseudo Cluster Installation
- HDFS
- Hive
- HBase
- Sqoop
- Big Data and Apache Kafka
- Producers and Consumers
- Clusters Architectures
- Kafka Streams
- Kafka pipeline transformations
- Spark Components
- Spark Executions – Session
- RDD
- Spark DataFrames
- Spark Datasets
- Spark SQL
- Spark Streaming
- Lambda
- Kappa
- Streaming Big Data Architectures Monitoring pipelines
- Building pipelines in Apache Airflow
- Deploy and Monitor Data Pipelines
- Production Data Pipeline
- Building a Kafka Cluster
- Setup Zookeeper and Kafka Cluster
- Configuring and Testing Kafka Cluster
- Streaming Data with Apache Kafka
- Data Processing with Apache Spark
- AWS Sagemaker for end-to-end ML workflow
- Azure Data factory for ETL
- Storage Accounts
- Designing Data Storage Structures
- Data Partitioning
- Designing the Serving Layer
- Physical Data Storage Structures
- Logical Data Structures
- The Serving Layer
- Data Policies & Standards
- Securing Data Access
- Securing Data
- Data Lake Storage
- Data Flow Transformations
- Databricks
- Databrick Processing
- Stream Analytics
- Synapse Analytics
- Data Storage Monitoring
- Data Process Monitoring
- Data Solution Optimization
- Google Cloud Platform Fundamentals
- Google Cloud Platform Storage and Analytics
- Deeper through GCP Analytics and Scaling
- GCP Network Data Processing Models
- Google Cloud Dataproc
- Dataproc Architecture
- Continued Dataproc Operations
- Implementations with BigQuery for Big Data
- Fundamentals of Big Query
- APIs and Machine Learning
- Dataflow Autoscaling Pipelines
- Machine Learning with TensorFlow and Cloud ML
- GCP Engineering and Streaming Architecture
- Streaming Pipelines and Analytics
- GCP Big Data and Security
- Data Engineering on Microsoft Azure
Why you should take this Program
- The Certified Data Engineering is in association with Future Skills Prime accredited by NASSCOM, approved by the Government of India
- The curriculum is developed keeping in mind the trending tools and techniques that will make the student stand out in the hiring process.
- The learner will be able to earn a Joint Co-Branded Certificate of Participation by 360DigiTMG and Future Skills Prime
- The course is divided into different modules and each module gives students a thorough insight into all the important techniques that will make the learning process seamless and effective.
- 40 plus hours of online classes with capstone live project and 80 plus hours of assignments.
- The learner is eligible for Government of India (GOI) incentives after succesfully clearing the mandatory Future Skills Prime Assessment. For more details please visit: https://futureskillsPrime.in/govt-of-India-incentives.
- Learners will get access to multiple resources like NASSCOM Career Fair, NASSCOM industry events, Bootcamps, Career guidance sessions, etc.
- Learners will be eligible to apply for jobs and get job placement assistance through the Talent Connect Portal of Future Skills Prime.
Learner's Journey
How we prepare you
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Additional assignments of over 80+ hours
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Live Free Webinars
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Resume and LinkedIn Review Sessions
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Lifetime LMS Access
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24/7 support
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Job placements in Data Engineering fields
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Complimentary Courses
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Unlimited Mock Interview and Quiz Session
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Hands-on experience in a live project
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Offline Hiring Events
Call us Today!
+60 19-383 1378