Big Data Practice Exam

Big Data Practice Exam

About Big Data Exam

The Big Data Certification Exam is designed to validate a candidate’s proficiency in handling, processing, and analyzing massive datasets using modern data processing frameworks and architectures. With the explosion of data generated through digital platforms, IoT devices, and enterprise systems, organizations require skilled professionals who can derive actionable insights from large-scale, unstructured, semi-structured, and structured data sources. This certification is tailored to assess both theoretical knowledge and hands-on expertise in the Big Data ecosystem, including tools such as Hadoop, Spark, Hive, HBase, Kafka, and cloud-based analytics services. The exam ensures that certified individuals can effectively manage data ingestion, storage, processing, and real-time analytics at scale.

Who should take the Exam?

This certification is ideal for professionals seeking to demonstrate their expertise in the Big Data domain. It is well-suited for:

• Data Engineers responsible for building and maintaining data pipelines and storage systems.
• Data Analysts and Scientists working with large datasets to extract business insights.
• Software Engineers and Developers integrating Big Data processing into applications.
• Database Administrators transitioning to distributed and NoSQL database environments.
• Business Intelligence (BI) Professionals seeking to scale their analytics skills.
• IT Professionals involved in cloud, data warehouse, and data lake architecture.
• Graduates and Entry-Level Candidates aiming to establish a strong foundation in Big Data technologies.

Skills Required

• Basic understanding of databases and data structures.
• Familiarity with data formats such as JSON, XML, CSV, and Parquet.
• Proficiency in at least one programming language (e.g., Python, Java, or Scala).
• Exposure to SQL for querying relational databases.
• General awareness of distributed systems and networking concepts.
• Experience with Linux command-line tools and shell scripting is an added advantage.

Knowledge Gained

• Fundamentals of Big Data and its Ecosystem: Understanding volume, velocity, variety, veracity, and value.
• Distributed Computing Principles: Concepts such as parallel processing, fault tolerance, and horizontal scalability.
• Hadoop Ecosystem Mastery: Working with HDFS, MapReduce, YARN, and data ingestion tools like Sqoop and Flume.
• Apache Spark Framework: Hands-on knowledge of Spark Core, Spark SQL, Spark Streaming, and Spark MLlib.
• Data Storage Technologies: Use of NoSQL databases like HBase and Cassandra; data warehousing with Hive.
• Data Ingestion and Processing Pipelines: Real-time data ingestion with Kafka and stream processing using Spark or Flink.
• Data Governance and Security: Understanding role-based access, encryption, data masking, and compliance (GDPR, HIPAA).
• Cloud-Based Big Data Solutions: Deployment of big data workloads using AWS EMR, Google Cloud Dataproc, or Azure HDInsight.

Course Outline

• Definition and characteristics of Big Data (5Vs)
• Use cases and industry applications
• Challenges in traditional data processing systems

Domain 2 - Big Data Architecture and Tools

• Batch vs. real-time processing architectures
• Lambda and Kappa architecture models
• Overview of the Hadoop and Spark ecosystems

Domain 3 - Hadoop Framework and HDFS

• Introduction to HDFS architecture and operations
• MapReduce programming model
• Resource management with YARN

Domain 4 - Data Warehousing and Querying Tools

• Hive data warehousing and SQL-like querying
• Pig scripting and execution
• Partitioning and bucketing strategies

Domain 5 - NoSQL and Columnar Databases

• Introduction to HBase and Cassandra
• CAP theorem and eventual consistency
• Data modeling and querying techniques

Domain 6 - Apache Spark and In-Memory Processing

• Spark architecture and RDDs
• Spark SQL for structured data
• Spark Streaming and real-time processing
• Machine learning with Spark MLlib

Domain 7 - Data Ingestion and Real-Time Streaming

• Data ingestion using Apache Kafka, Flume, and NiFi
• Stream processing with Apache Storm and Flink
• Building resilient data pipelines

Domain 8 - Cloud-Based Big Data Platforms

• Big Data services in AWS, Azure, and Google Cloud
• Storage options: S3, GCS, Blob Storage
• Deploying clusters and managing workloads

Domain 9 - Data Security and Governance

• Authentication and authorization (Kerberos, Ranger, Knox)
• Data encryption at rest and in transit
• Data lineage, metadata management, and compliance