Exam Name: IBM Big Data Engineer
Certification Provider: IBM
Corresponding Certification: IBM Analytics: Platform Analytics
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IBM Big Data Engineer: C2090-101 Exam Premium File as PDF & Test Engine
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IBM Big Data Engineer C2090-101 Exam
IBM Big Data Engineer C2090-101 Exam is requisite to obtain IBM Analytics Platform Certification. This IBM C2090-101 Exam validates and measures the candidate knowledge in extract data from relational source using Sqoop, Utilize Apache Hue for search Visualization, Export Processing Results out of Hadoop and utilize Consistent Regions in infosphere Streams. Big Data Engineers and Data Security Consultants usually hold or pursue this certification and you can expect the same job roles after completion of IBM Analytics Platform certification.
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IBM C2090-101 Exam Syllabus Topics:
| Topic | Details | Weights |
|---|---|---|
| Performance and Scalability | -Use Resilient Distributed Dataset (RDD) to improve MapReduce performance -Choose file formats to optimize performance of Big SQL, JAQL, etc. -Make specific performance tuning decisions for Hive and HBase -Analyze performance considerations when using Apache Spark | 15% |
| Architecture and Integration | -Implement MapReduce -Evaluate use cases for selecting Hive, Big SQL, or HBase -Create and/or query a Solr index -Evaluate use cases for selecting potential file formats (e.g. JSON, CSV, Parquet, Sequence, etc..) -Utilize Apache Hue for search visualization | 17% |
| Data Preparation, Transformation, and Export | -Use Jaql query methods to transform data in InfoSphere BigInsights -Capture and prep social data for analytics -Integrating SPSS model scoring in InfoSphere Streams -Implement entity resolution within a Big Data platform (e.g. Big Match) -Utilize Pig for data transformation and data manipulation -Use Big SQL to transform data in InfoSphere BigInsights -Export processing results out of Hadoop (e.g. DataClick, DataStage, etc.) -Utilize consistent regions in InfoSphere Streams to ensure at least once processing | 26% |
| Data Security | -Keep data secure within PCI standards -Uses masking (e.g. Optim, Big SQL), and redaction to protect sensitive data | 8% |
| Data Loading | -Load unstructured data into InfoSphere BigInsights -Import streaming data into Hadoop using InfoSphere Streams -Create a BigSheets workbook -Import data into Hadoop and create Big SQL table definitions -Import data to HBase -Import data to Hive -Use Data Click to load from relational sources into InfoSphere BigInsights with a self-service process -Extract data from a relational source using Sqoop -Load log data into Hadoop using Flume -Insert data via IBM General Parallel File System (GPFS) Posix file system API -Load data with Hadoop command line utility | 34% |
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