Big Data Training and Certification in Kuala Lumpur, Malaysia

Big data training equips professionals with the skills needed to handle, analyze, and derive insights from massive datasets. It covers various technologies, including Hadoop, Apache Spark, and NoSQL databases. Participants learn to process structured and unstructured data efficiently. The training emphasizes real-world applications such as predictive analytics and data-driven decision-making. By mastering big data techniques, professionals enhance their ability to solve complex business challenges.

Introduction to Big Data and Hadoop Ecosystem

• Introduction to Big Data:

• What is Big Data?

• Key characteristics of Big Data (Volume, Velocity, Variety, Veracity).

• Applications of Big Data in different industries.

• Challenges of managing and analyzing Big Data.

• Hadoop Ecosystem Overview:

• Introduction to Hadoop and its role in Big Data processing.

• Components of the Hadoop ecosystem:

• HDFS (Hadoop Distributed File System).

• YARN (Yet Another Resource Negotiator).

• MapReduce (Distributed processing model).

• Hands-on Exercise

• Data Storage with HDFS:

• Overview of HDFS architecture and data storage in a distributed environment.

• Managing data in HDFS: file operations (put, get, delete).

• Data replication and fault tolerance in HDFS.

• Hands-on Exercise

• Introduction to MapReduce and YARN:

• Basics of MapReduce programming model: mappers, reducers, and data flow.

• Role of YARN in resource management and job scheduling.

• Hands-on Exercise

Apache Spark, NoSQL Databases, and Big Data Analytics

• Introduction to Apache Spark:

• What is Apache Spark and how it is used in Big Data processing?

• Key features of Spark: In-memory processing, fault tolerance, and scalability.

• Spark architecture and components: RDDs (Resilient Distributed Datasets), DataFrames, and Spark SQL.

• Hands-on Exercise

• Working with NoSQL Databases:

• Introduction to NoSQL databases and their relevance in Big Data.

• Overview of popular NoSQL databases: MongoDB, Cassandra, and HBase.

• Use cases for NoSQL in Big Data: Schema-less design, horizontal scaling, and flexibility.

• Hands-on Exercise

• Big Data Analytics and Data Processing Frameworks:

• Overview of analytics techniques used in Big Data environments.

• Introduction to machine learning and data processing using Spark MLlib.

• Data visualization with Big Data tools: Integration with tools like Tableau and Power BI.

• Hands-on Exercise

• Big Data Best Practices and Challenges:

• Best practices for processing and storing Big Data efficiently.

• Common challenges in Big Data analytics: Data privacy, processing speed, and scalability.

• Hands-on Exercise