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ACA-BIGDATA1 exam Format | ACA-BIGDATA1 Course Contents | ACA-BIGDATA1 Course Outline | ACA-BIGDATA1 exam Syllabus | ACA-BIGDATA1 exam Objectives

Exam Specification: ACA-BIGDATA1 ACA Big Data Certification

Exam Name: ACA-BIGDATA1 ACA Big Data Certification
Exam Code: ACA-BIGDATA1
Exam Duration: 120 minutes
Passing Score: 70%
Exam Format: Multiple-choice, True/False, and Hands-on tasks
Exam Delivery: Proctored online or at a testing center

Course Outline:

1. Introduction to Big Data
- Understanding the concept of Big Data
- Exploring the characteristics and challenges of Big Data
- Overview of the Big Data ecosystem and technologies

2. Big Data Storage and Processing
- Understanding distributed file systems (e.g., Hadoop HDFS)
- Introduction to MapReduce and Apache Spark
- Exploring batch processing and stream processing frameworks

3. Data Ingestion and Integration
- Techniques for data ingestion from various sources
- Integration of structured and unstructured data
- Implementing data transformation and normalization

4. Big Data Analytics
- Introduction to data analytics and machine learning in Big Data
- Utilizing SQL and NoSQL databases for data analysis
- Implementing data visualization techniques

5. Data Governance and Security
- Ensuring data quality and data governance in Big Data projects
- Understanding data privacy and security considerations
- Implementing access control and encryption in Big Data environments

6. Big Data Infrastructure and Deployment
- Designing and configuring Big Data infrastructure
- Deploying and managing Big Data clusters
- Scaling and optimizing Big Data systems

7. Big Data Application Development
- Developing Big Data applications using programming languages (e.g., Java, Python)
- Utilizing Big Data frameworks and libraries (e.g., Apache Hadoop, Apache Spark)
- Implementing real-time data processing and analytics

Exam Objectives:

1. Understand the concepts, characteristics, and challenges of Big Data.
2. Identify and utilize Big Data storage and processing technologies.
3. Ingest and integrate data from various sources into Big Data systems.
4. Apply Big Data analytics techniques for data analysis and insights.
5. Ensure data governance, privacy, and security in Big Data projects.
6. Design, deploy, and manage Big Data infrastructure.
7. Develop Big Data applications using programming languages and frameworks.

Exam Syllabus:

Section 1: Introduction to Big Data (10%)
- Concept and characteristics of Big Data
- Challenges and opportunities in Big Data projects
- Overview of the Big Data ecosystem and technologies

Section 2: Big Data Storage and Processing (20%)
- Distributed file systems (e.g., Hadoop HDFS)
- MapReduce and Apache Spark for data processing
- Batch processing and stream processing frameworks

Section 3: Data Ingestion and Integration (15%)
- Techniques for data ingestion from various sources
- Integration of structured and unstructured data
- Data transformation and normalization

Section 4: Big Data Analytics (15%)
- Data analytics and machine learning in Big Data
- SQL and NoSQL databases for data analysis
- Data visualization techniques

Section 5: Data Governance and Security (10%)
- Data quality and data governance in Big Data projects
- Data privacy and security considerations
- Access control and encryption in Big Data environments

Section 6: Big Data Infrastructure and Deployment (15%)
- Designing and configuring Big Data infrastructure
- Deployment and management of Big Data clusters
- Scaling and optimization of Big Data systems

Section 7: Big Data Application Development (15%)
- Big Data application development using programming languages
- Utilizing Big Data frameworks and libraries
- Real-time data processing and analytics

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