Big Data Training Courses courses in United States

Introduction to Hadoop Ecosystem and Architecture• Overview of Hadoop components and architecture• HDFS concepts and storage management• Hadoop cluster setup and configuration basics• Namenode, Datanode, and secondary namenode rolesHDFS Administration and Mana...

• Hadoop Administrators and System Administrators
• Big Data Engineers and Platform Engineers
• DevOps Engineers working on data platforms
• Cloud Infrastructure Engineers
• IT Professionals managing distributed systems

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Hadoop & Spark for Administrators Training, validating their expertise in Hadoop cluster administration, Spark deployment, resource management, and big data platform optimization.

Prerequisites: • Basic understanding of Linux/Unix operating system• Familiarity with networking and distributed systems concepts• Basic knowledge of Big Data concepts is helpful• Understanding of command-line operations

Introduction to Hadoop Development Environment• Overview of Hadoop architecture and components• Setting up Hadoop development environment• HDFS commands and file system operations• Data storage and retrieval conceptsHDFS Programming and Data Handling• Reading...

• Java Developers and Software Engineers
• Big Data Developers and Data Engineers
• Backend Developers working with large datasets
• IT Professionals transitioning into Big Data development
• Students and professionals entering Hadoop ecosystem

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Hadoop for Developers Training, validating their expertise in Hadoop development, MapReduce programming, HDFS data processing, and distributed application development.

Prerequisites: • Basic knowledge of Java programming• Understanding of data structures and algorithms fundamentals• Familiarity with Linux/Unix command line• Basic understanding of databases and data concepts

Introduction to Hadoop & Big Data Concepts • What is Hadoop and where it is used • Big data challenges and Hadoop solutions • Hadoop ecosystem overview • Hadoop cluster architectureHDFS Fundamentals • HDFS architecture and components • NameNode and DataNode ro...

 • Big data developers and engineers
 • Data engineers and analytics engineers
 • IT professionals supporting big data platforms
 • Data analysts working with large datasets
 • Professionals starting with Hadoop ecosystem

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Upon successful completion of the training, participants will receive an AcadNXT Certificate of Completion for Hadoop Fundamentals. This digital, verifiable certification validates practical Hadoop distributed storage, big data processing, and Hadoop ecosystem fundamentals and can be shared on LinkedIn and included in professional profiles to enhance big data and data engineering career credibility.

Prerequisites:  • Basic understanding of data concepts and databases • Familiarity with Linux commands is helpful • Interest in big data and distributed systems

Introduction to HBase & NoSQL Concepts • What is HBase and where it is used • HBase architecture and components • HBase data model concepts • Use cases for HBaseHBase Installation & Configuration Basics • HBase deployment modes • HBase configuration files • St...

 • Big data developers and engineers
 • Data engineers working with NoSQL databases
 • Application developers building scalable data platforms
 • Architects designing big data storage solutions
 • Professionals working with HBase in production environments

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Upon successful completion of the training, participants will receive an AcadNXT Certificate of Completion for HBase for Developers. This digital, verifiable certification validates practical HBase NoSQL development, big data storage, and distributed database skills and can be shared on LinkedIn and included in professional profiles to enhance big data and NoSQL career credibility.

Prerequisites:  • Basic understanding of Hadoop and HDFS concepts • Familiarity with Java or another programming language is helpful • Understanding of big data and distributed systems

Introduction to NGINX Architecture and Setup• Overview of NGINX architecture and components• Installing NGINX on Linux environments• Configuration file structure and directives• Starting, stopping, and managing NGINX servicesBasic Web Server and Reverse Proxy...

• System Administrators and Linux Administrators
• DevOps Engineers and Cloud Engineers
• Web Server and Infrastructure Engineers
• Backend Developers working with web applications
• IT Professionals managing web hosting environments

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in NGINX Essentials Training, validating their expertise in web server administration, reverse proxy configuration, load balancing, and performance optimization using NGINX.

Prerequisites: • Basic understanding of Linux/Unix operating systems• Familiarity with networking fundamentals (HTTP, DNS, TCP/IP)• Basic command-line usage skills• Understanding of web servers is helpful but not mandatory

Introduction to PySpark & Spark with Python • What is PySpark and where it is used • Spark architecture for Python users • Setting up PySpark environment • Creating first PySpark applicationPySpark DataFrames Fundamentals • Creating DataFrames from files and d...

 • Data engineers and analytics engineers
 • Python developers working with big data
 • Data analysts handling large datasets
 • Data scientists using Spark for data preparation
 • Professionals adopting PySpark

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Upon successful completion of the training, participants will receive an AcadNXT Certificate of Completion for PySpark Essentials. This digital, verifiable certification validates practical PySpark big data processing, distributed analytics, and Python-based Spark skills and can shared on LinkedIn and included in professional profiles to enhance big data and data engineering career credibility.

Prerequisites:  • Basic knowledge of Python programming • Understanding of data concepts and databases • Familiarity with SQL is helpful

Introduction to Python and Spark Ecosystem• Overview of Apache Spark architecture• PySpark setup and environment configuration• RDD concepts and distributed computing basics• Creating and managing Spark sessionsPySpark DataFrame Fundamentals• Creating DataFram...

• Data Engineers and Data Analysts
• Python Developers entering big data domain
• Analytics Engineers and BI Professionals
• Software Developers working with data pipelines
• IT Professionals transitioning into data engineering

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Python & Spark Training, validating their expertise in PySpark development, distributed data processing, Spark SQL, and big data analytics using Apache Spark.

Prerequisites: • Basic understanding of Python programming• Familiarity with data structures and basic programming concepts• Basic knowledge of SQL is helpful• Understanding of data concepts and analytics fundamentals

Introduction to Spark NLP and Text Processing• Overview of NLP and Spark NLP ecosystem• Spark NLP architecture and pipeline components• Text preprocessing techniques• Tokenization and normalization workflowsFeature Engineering for NLP• Stop words removal and s...

• Spark NLP architecture and pipeline framework
• Text preprocessing and normalization techniques
• Tokenization, stemming, and lemmatization workflows
• Named Entity Recognition (NER) implementation
• Sentiment analysis and text classification models

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Spark NLP Pipelines Training, validating their expertise in natural language processing, Spark NLP frameworks, text analytics, and scalable AI pipeline development.

Prerequisites: • Basic understanding of Python programming• Familiarity with machine learning and data processing concepts• Basic knowledge of Apache Spark is helpful• Understanding of NLP fundamentals is recommended

Introduction to Talend and ETL Fundamentals• Overview of ETL and data integration concepts• Talend architecture and components• Installation and workspace setup• Creating first Talend jobData Integration and Mapping Basics• Connecting to databases and flat fil...

• Data Engineers and ETL Developers
• Business Intelligence Professionals
• Database Developers and Administrators
• Data Analysts working with large datasets
• IT Professionals entering data integration domain

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Talend Essentials Training, validating their expertise in ETL development, data integration, workflow automation, and data pipeline design using Talend.

Prerequisites: • Basic understanding of databases and SQL concepts• Familiarity with data handling and spreadsheets• Basic knowledge of data warehousing concepts is helpful• Analytical thinking and problem-solving skills