C2090-101 IBM Big Data Engineer Exam Overview

"Please visit at www.certsmaster.com or email us at sales@certsmaster.com

Data Loading
34%

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
Data Security
8%

Keep data secure within PCI standards

Uses masking (e.g. Optim, Big SQL), and redaction to protect sensitive data
Architecture and Integration
17%

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
Performance and Scalability
15%

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
Data Preparation, Transformation, and Export
26%

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"