Description

This certification is supposed for IBM Big Data Engineers. The Big Data Engineer works immediately with the Data Architect and hands-on Developers to transform the architect’s Big Data imaginative and prescient and blueprint right into a Big Data reality. The Data Engineer possesses a deep degree of technical expertise and enjoy throughout a big range of merchandise and technology. A  Data Engineer is aware a way to practice technology to resolve huge records problems, and has the cappotential to construct massive-scale records processing structures for the enterprise. Data engineers develop, keep, check and examine huge records answers inside organizations. They offer enter at the wanted hardware and software program to the architects.

Big Data Engineers consciousness on collecting, parsing, handling and studying massive records units, as a way to offer the proper records units and visible gear for evaluation to the records scientists. They apprehend the complexity of records and might take care of specific records variety (structured, semi-structured, unstructured), volume, velocity (consisting of circulation processing), and veracity. They additionally deal with the data governance and safety demanding situations related to the records. They have an excellent heritage in software program engineering and sizeable programming and scripting enjoy.

To gain the IBM Certified Big Data Engineer, applicants have to byskip one check. To benefit extra expertise and skills, and put together for the check primarily based totally at the activity roles and check objectives, click on at the hyperlink to the check beneath and talk to the Test Preparation tab.

Recommended Skills

Understand the records layer and specific regions of ability challenge/chance withinside the records layer

Ability to translate useful necessities into technical specifications.

Ability to take common solution/logical structure and offer bodily structure.

Understand Cluster Management

Understand Network Requirements

Understand Important interfaces

Understand Data Modeling

Ability to identify/guide non-useful necessities for the solution

Understand Latency

Understand Scalability

Understand High Availability

Understand Data Replication and Synchronization

Understand Disaster Recovery

Understand Overall overall performance (Query Performance, Workload Management, Database Tuning)

Propose advocated and/or nice practices concerning the movement, manipulation, and garage of records in a huge records solution (consisting of, however now no longer confined to:

Understand Data ingestion technical alternatives

Understand Data garage alternatives and ramifications (for example , apprehend the extra necessities and demanding situations delivered through records withinside the cloud)

Understand Data querying techniques & availability to guide analytics

Understand Data lineage and records governance

Understand Data variety (social, system records) and records volume

Understand/Implement and offer steerage round records safety to guide implementation, consisting of however now no longer confined to:

Understand LDAP Security

Understand User Roles/Security

Understand Data Monitoring

Understand Personally Identifiable Information (PII) Data Security concerns

Software regions of important consciousness:

BigInsights

BigSQL

Hadoop

Cloudant (NoSQL)

Software regions of peripheral consciousness:

Information Server

Integration with BigInsights, Balanced Optimization for Hadoop, JAQL Push down capability, etc

Data Governance

Security functions of BigInsights

Information Server (MetaData Workbench for Lineage)

Optim Integration with BigInsights (archival)

DataClick for BigInsights (Future: DataClick for Cloudant – to tug operation records into Hadoop for Analytics – scripts to be had today)

BigMatch (seeking to get to a unmarried view)

Guardium (monitoring)

Analytic Tools (SPSS)

BigSheets

Support in Hadoop/BigInsights

Data Availability and Querying Support

Streams

Interface/Integration with BigInsights

Streaming Data Concepts

In reminiscence analytics

Netezza

DB2 BLU

Graph Databases

Machine Learning (System ML)

Requirements

Exam C2090-101: IBM Big Data Engineer

Each check:

1.   carries questions requiring unmarried and more than one solutions. For more than one-solution questions, you want to pick out all required alternatives to get the solution correct. You could be suggested what number of alternatives make up the ideal solution.

2.   is designed to offer diagnostic remarks at the Examination Score Report, correlating lower back to the check objectives, informing the check taker how she or he did on every phase of the check. As a result, to keep the integrity of every check, questions and solutions aren’t distributed

Exam Objectives

Please be aware this examination has been withdrawn

This check includes 5  sections containing a complete of fifty three more than one-desire questions. The chances after every phase name replicate the approximate distribution of the whole query set throughout the sections.

Number of questions: 53

Number of inquiries to byskip: 34

Time allowed: 75 minutes

Status: Withdrawn

 

Section 1: Data Loading34%

Load unstructured records into InfoSphere BigInsights

Import streaming records into Hadoop the use of InfoSphere Streams

Create a BigSheets workbook

Import records into Hadoop and create Big SQL desk definitions

Import records to HBase

Import records to Hive

Use Data Click to load from relational reassets into InfoSphere BigInsights with a self-provider process

Extract records from a relational supply the use of Sqoop

Load log records into Hadoop the use of Flume

Insert records through IBM General Parallel File System (GPFS) Posix document gadget API

Load records with Hadoop command line utility

 

Section 2: Data Security8%

Keep records steady inside PCI standards

Uses masking (e.g. Optim, Big SQL), and redaction to defend touchy records

 

Section 3: Architecture and Integration17%

Implement MapReduce

Evaluate use instances for choosing Hive, Big SQL, or HBase

Create and/or question a Solr index

Evaluate use instances for choosing ability document codecs (e.g. JSON, CSV, Parquet, Sequence, etc..)

Utilize Apache Hue for seek visualization

 

Section 4: Performance and Scalability15%

Use Resilient Distributed Dataset (RDD) to enhance MapReduce overall performance

Choose document codecs to optimize overall performance of Big SQL, JAQL, etc.

Make particular overall performance tuning selections for Hive and HBase

Analyze overall performance concerns while the use of Apache Spark

 

Section 5: Data Preparation, Transformation, and Export26%

Use Jaql question techniques to convert records in InfoSphere BigInsights

Capture and prep social records for analytics

Integrating SPSS version scoring in InfoSphere Streams

Implement entity decision inside a Big Data platform (e.g. Big Match)

Utilize Pig for records transformation and records manipulation

Use Big SQL to convert records in InfoSphere BigInsights

Export processing outcomes out of Hadoop (e.g. DataClick, DataStage, etc.)

Utilize steady areas in InfoSphere Streams to make certain as a minimum as soon as processing