This course gives business analysts and data scientists a seamless platform to profile, integrate, cleanse, and move big data without writing code in a Hadoop environment using an intuitive web-based interface.

Lernen Sie, wie Sie / Learn how to

• move data in and out of Hadoop
• interrogate and profile data for quality issues
• transform, transpose, and join data that is fit-for-purpose
• cleanse and integrate data suitable for analysis and reporting
• perform master data management activities of record clustering and survivorship in Hadoop
• load data into the SAS In-Memory Analytics Server for analytics and exploration
• execute custom SAS and HiveQL code inside the Hadoop cluster
• chain custom-built data management flows into re-useable jobs.

Zielgruppe / Who should attend

Business users who interact with data, perform data discovery, query data, and ensure that data is in the proper place and format for other users; data analysts, data scientists, and statisticians who review results of data discovery activities, create new tables, create new data elements, change the format/structure of data tables to view them in a variety of ways, manipulate and score data elements, and load data for use by other users; and data management specialists who apply enterprise standards to the data, ensure data quality throughout the enterprise, move data into and out of the Hadoop cluster, and optimize code running in the Hadoop cluster

Verfügbare Schulungsformen / Formats available	Dauer (kann variieren, Details s. Terminliste)
Präsenzkurs:	2.0 Tage
Live Web Class:	14 Stunden	Systemvoraussetzungen
E-Learning:	14 Stunden/180 Tag Lizenz	Systemvoraussetzungen

There are currently no prerequisites for this course.

In diesem Kurs wird mit folgenden Software Modulen gearbeitet / This course addresses SAS Data Loader for Hadoop Software

Introduction to Big Data and Hadoop

• big data and Hadoop
• Hadoop ecosystem

SAS Data Loader Overview

• SAS Data Loader capabilities and architecture
• SAS Data Loader directives and tasks
• steps common to most directives
• preparing data for analysis and reporting
• course overview and logistics

Acquiring and Discovering Data

• introduction to acquiring and discovering data
• copying a table into Hadoop
• importing a delimited file into Hadoop
• profiling data for inconsistencies
• querying data for relevant columns and rows

Transforming and Transposing Data

• introduction to transforming and transposing data
• transforming data to be fit-for-purpose
• transposing data for use in analysis and reporting

Cleansing Data

• introduction to cleansing data
• parsing data into meaningful subsets
• standardizing data into consistent formats
• using match codes to determine data similarity
• using names to identify gender
• analyzing data for data types
• applying casing for data consistency
• extracting data in useful tokens
• analyzing data for inconsistent patterns

Integrating Data

• introduction to integrating data
• joining data in Hadoop
• sorting and de-duplicating data
• clustering and surviving data to determine a best record
• matching and merging data into a single table
• deleting rows in Hadoop tables
• running user-written programs inside Hadoop

Delivering Data

• introduction to delivering data from Hadoop
• loading data to the SAS LASR Analytic Server for analysis and reporting
• copying Hadoop data to SAS and relational database tables

Managing and Integrating Directives

• introduction to managing and integrating directives
• creating data flows by chaining directives
• integrating directives into SAS platform applications
• running directives as batch jobs

Additional Topics

• SAS and Hadoop data processing
• SAS DS2 programs
• debugging Hadoop jobs