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Last Updated: Sep 04, 2026

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As the field of Big Data continues to grow, companies increasingly require qualified professionals who can effectively manage and analyze their large datasets. The IBM C2090-101 certification exam is designed to verify that candidates possess the skills and knowledge necessary to be a successful Big Data Engineer.

Achieving the IBM Big Data Engineer certification validates an individual's skills and knowledge in the field of big data engineering. It demonstrates that the individual has the ability to design and implement big data solutions using IBM technologies, as well as troubleshoot and optimize these solutions to meet business requirements. IBM Big Data Engineer certification can help IT professionals advance their careers and increase their earning potential by providing them with a competitive edge in the job market.

Reference: https://www.ibm.com/certify/exam?id=C2090-101

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IBM C2090-101 exam covers a wide range of topics related to big data engineering. This includes data ingestion, data transformation, data storage, data processing, and data analysis. C2090-101 exam also covers topics such as data security, data governance, and data quality. Professionals who pass the IBM C2090-101 exam will have a thorough understanding of big data technologies and will be able to apply their knowledge to real-world scenarios.

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IBM C2090-101 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Loading34%- Import streaming data using InfoSphere Streams
- Load log files with Flume and command-line tools
- Create Big SQL tables and load data into Hadoop
- Extract data from relational sources using Sqoop
- Load unstructured data into InfoSphere BigInsights
- Ingest data into HBase and Hive
Topic 2: Architecture and Integration17%- Integrate GPFS and distributed file systems
- Design cluster architecture and storage layers
- Ensure consistent processing with InfoSphere Streams
- Evaluate use cases for Hive, Big SQL, and HBase
- Implement MapReduce and RDD concepts
Topic 3: Data Preparation, Transformation, and Export26%- Use Pig for data transformation
- Create BigSheets workbooks
- Export results from Hadoop environment
- Perform entity resolution and data cleansing
- Apply Jaql for data manipulation
Topic 4: Performance and Scalability15%- Apply best practices for latency and throughput
- Ensure high availability and scalability
- Optimize query execution and workload management
- Tune Hive, HBase, and Big SQL performance
Topic 5: Data Security8%- Apply data masking and redaction techniques
- Comply with security standards (PCI, PII protection)
- Implement access control and LDAP security
- Monitor and secure sensitive data
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