Learn big data hadoop training in Mountain View with a practical focus on Hadoop, HDFS, YARN, MapReduce, Hive, Pig, Sqoop, Spark, Spark SQL, Spark Streaming, Kafka, and SQL. You will build distributed data processing, ingestion, querying, and pipeline workflow skills that fit data engineering and analytics roles.
14,200+ (Placed)
Freshers to IT
7,100+ (Placed)
Non-IT to Tech
5,800+ (Placed)
Career Gap Fillers
6,400+ (Placed)
Upskilling Success
24,999₹
In 60 Days + Placement
Free Session
Get Job with our Guaranteed Placement Support Program
5.2 LPA • ServiceNow Developer
5.7 LPA • Android Developer
6 LPA • Android Developer
7 LPA • Android Developer
9 LPA • AI Engineer
5 LPA • Data Scientist
8 LPA • Machine Learning Engineer
4 LPA • AI Engineer
4 LPA • Associate Product Manager
3.45 LPA • Network Engineer
3.75 LPA • NOC Support Engineer
4.4 LPA • Network Administrator
9 LPA • AI Engineer
12 LPA • Data Engineer
16 LPA • Data Engineer
5.1 LPA • RPA Developer
7.83 LPA • RPA Business Analyst
5.55 LPA • Data Scientist
40 LPA • AWS Devops Engineer
25 LPA • Salesforce Developer
30 LPA • Project Administrator
20 LPA • Digital Marketing Specialist
12 LPA • Social Media Manager
8 LPA • Digital Marketing Analyst
8.5 LPA • Performance Marketer
4.3 LPA • Digital marketing specialist
8 LPA • Performance Marketer
4.3 LPA • AI Engineer
7.5 LPA • Data Engineer
4.5 LPA • Salesforce Developer
8.45 LPA • Computer Vision Engineer
5 LPA • SAP FICO Analyst
5.8 LPA • SAP FICO Analyst
4.2 LPA • Servicenow Developer
4.8 LPA • TOSCA Automation Engineer
8 LPA • Data Engineer
8 LPA • Tosca Test Analyst
10 LPA • Machine Learning Engineer
4.4 LPA • Tosca Administrator
5.7 LPA • ML Engineer
6 LPA • Devops Engineer
7.15 LPA • DevSecOps Engineer
6.3 LPA • AI Engineer
7 LPA • MLOps Engineer
5 LPA • HR Executive
7 LPA • Data Scientist
10 LPA • Brand Marketing Specialist
8.2 LPA • AI Engineer
Learning Hadoop is only useful when you can explain distributed storage, batch processing, Hive queries, and data pipelines in a real interview. Inventateq focuses on job-ready preparation for data engineering and big data roles in Mountain View, with support that connects the course work to hiring expectations.
In Mountain View, big data skills are used across data engineering, analytics, platform, and cloud data teams. Pay grows with experience as you move from Hadoop support and ETL work into design, architecture, and lead data engineering responsibilities.
Big Data Hadoop Average Salary by Experience
In Mountain View, big data skills are used across data engineering, analytics, platform, and cloud data teams. Pay grows with experience as you move from Hadoop support and ETL work into design, architecture, and lead data engineering responsibilities.
Big Data Hadoop Average Salary by Experience
4.7/5 Google Rating | 1,432+ Verified Reviews
4.7 / 5
By Google Reviews
4.7 / 5
By Justdial
4.7 / 5
By Sulekha Courses
4.7 / 5
By Course Suggest
Success Result: Our students are competing at global levels. Watch their placement journey here.
Data Engineering @ Accenture - 12 LPA
Junior Accountant @ Fortis - 8 LPA
SQL Developer @ Wipro - 10 LPA
Salesforce Administrator @ Deloitte - 15 LPA
RPA Developer @ IBM - 20 LPA
Tableau Developer @ Genpact - 20 LPA
Talent Acquisition Executive @ Tech Mahindra - 12 LPA
Angular Developer @ Capgemini - 15 LPA
Data Analyst @ Amazon - 25 LPA
Java Developer @ Oracle - 25 LPA
CATIA Design Engineer @ TATA Motors - 20 LPA
AWS Solutions Architect @ Accenture - 25 LPA
Python Developer @ Mphasis - 15 LPA
QA Engineer @ HCLTech - 15 LPA
Cloud DevOps Engineer @ LTIMindtree - 18 LPA
Digital Marketer @ Google - 20 LPA
BTM Layout @ Inventateq - -
Genuine Reviews @ Inventateq - 25 LPA - 50LPA
Data Engineering @ Accenture - 12 LPA
Junior Accountant @ Fortis - 8 LPA
SQL Developer @ Wipro - 10 LPA
Salesforce Administrator @ Deloitte - 15 LPA
RPA Developer @ IBM - 20 LPA
Tableau Developer @ Genpact - 20 LPA
Talent Acquisition Executive @ Tech Mahindra - 12 LPA
Angular Developer @ Capgemini - 15 LPA
Data Analyst @ Amazon - 25 LPA
Java Developer @ Oracle - 25 LPA
CATIA Design Engineer @ TATA Motors - 20 LPA
AWS Solutions Architect @ Accenture - 25 LPA
Python Developer @ Mphasis - 15 LPA
QA Engineer @ HCLTech - 15 LPA
Cloud DevOps Engineer @ LTIMindtree - 18 LPA
Digital Marketer @ Google - 20 LPA
BTM Layout @ Inventateq - -
Genuine Reviews @ Inventateq - 25 LPA - 50LPA
0.0
GOOGLE RATING
0k+
REVIEWS
4.7/5 · 1,432+ Verified Reviews
Inventateq teaches Big Data Hadoop in a practical sequence, starting with big data foundations and moving through Hadoop, HDFS, MapReduce, Hive, Pig, Sqoop, Spark, and modern cloud platforms. The course is built around real distributed data concepts, not just definitions, so learners can understand how data is stored, moved, processed, and queried in enterprise systems.
We stand apart through our commitment to:
The live online batch gives Mountain View learners the same structured Hadoop training with instructor-led sessions and guided practice. You can join from anywhere, follow the module sequence, and get support while working on HDFS, Hive, Spark, and data pipeline topics.
Good for students who want to start in data engineering, big data, or analytics with a clear tool-based foundation.
Useful for developers, support engineers, and analysts who want Hadoop, Hive, and Spark knowledge for data roles.
A strong fit for people who already know SQL and want to move into large-scale data processing.
Suitable for learners who can begin with fundamentals and build distributed data understanding step by step.
Best for learners targeting Big Data Engineer, ETL Developer, Data Engineer, or Hadoop Developer roles.
Duration: Structured module-based training with enough time for hands-on practice.
Mode: Available in classroom and live online formats.
Batch style: Instructor-led sessions with guided tool practice.
Prerequisite: Beginner-friendly, with fundamentals covered from the start.
You can join from Mountain View without needing prior Hadoop experience.
Rated 4.9/5
Inventateq teaches Hadoop with a practical platform mindset, so learners understand both the classic ecosystem and the modern data stack around it. The training moves from fundamentals to project workflow, with clear attention to HDFS, Hive, Spark, ingestion, and cloud platform awareness.
By the end of the course, learners can speak clearly about distributed storage, batch processing, SQL on big data, and pipeline workflow. The training gives you practical exposure to the tools and concepts used in Hadoop-based and modern data engineering environments.
Learn how HDFS, YARN, MapReduce, Hive, Pig, and Sqoop fit together in a working ecosystem. This helps you explain the structure of a big data platform in interviews and project discussions.
Practice schema-on-read concepts, table handling, partitions, and SQL-style analysis on large data. You will know how to use Hive for reporting and transformation tasks.
Gain confidence with blocks, replication, fault tolerance, and file operations in HDFS. This is important for support, engineering, and platform roles.
Understand map, shuffle, reduce, and batch processing logic so you can describe distributed compute clearly. That gives you a stronger base for big data development roles.
See how Spark, Databricks, and cloud data platforms relate to classic Hadoop skills. This makes your training relevant to current data engineering hiring needs.
Complete a project flow that includes ingestion, storage, querying, and transformation of a large dataset. You can use that project to show practical understanding in interviews.
This certification confirms that you understand the Hadoop ecosystem, distributed processing, data movement, and modern big data platform basics. It helps show employers that you have practical training in the tools and workflows used in data engineering and analytics roles.
Earn this certificate upon successful completion of our training program.
Validate your skills with recognized industry credentials.
Earn this certificate upon successful completion of our training program.
Yes. The course starts with big data foundations before moving into Hadoop tools and processing concepts. That makes it suitable for beginners who want a clear path into data engineering and analytics.
Yes, the syllabus includes Hadoop, HDFS, YARN, MapReduce, Hive, Pig, Sqoop, Spark, Kafka, and related tools. The training is built around tool understanding and workflow practice, not only theory.
Yes. Placement support includes resume help, mock interviews, project review, and role guidance. The support is aligned to roles like Big Data Engineer, Data Engineer, ETL Developer, and Hadoop Developer.
They can join if they are ready to learn step by step. The course begins with fundamentals and then builds toward distributed processing and data pipeline concepts. A basic comfort with logic and data handling helps, but deep prior experience is not required.
Yes, live online training is available. Mountain View learners can join instructor-led sessions, follow the module sequence, and get guidance while practicing the tools and project work.
The training is delivered in a structured module format, with enough time for practice across the Hadoop ecosystem and project workflow. The exact pace depends on the batch mode and learner schedule. The focus is on understanding the tools properly before moving to the next module.
Explore more
Browse the same course in nearby cities and training hubs.
Launch your fastest career with Inventateq! Our program equips you with in-demand skills to unlock insights from big data and land your dream job. Join us and become a career hero!