Fundamentos del desarrollador de Hadoop

Esquema del curso

Introduction to Hadoop

• Hadoop history, concepts
• Ecosystem
• Distributions
• High-level architecture
• Hadoop myths
• Hadoop challenges
• Hardware and software

HDFS

• Design and architecture
• Concepts (horizontal scaling, replication, data locality, and rack awareness)
• Daemons: Namenode, Secondary Namenode, and Datanode
• Communications and heart-beats
• Data integrity
• Read and write path
• Namenode High Availability (HA), Federation

YARN

• YARN Concepts and architecture
• Evolution from MapReduce to YARN
• Data Ingestion
• Flume for logs and other data ingestion into HDFS
• Sqoop for importing from SQL databases to HDFS, as well as exporting back to SQL
• Copying data between clusters (distcp)
• Using S3 as complementary to HDFS
• Data ingestion best practices and architectures
• Oozie for scheduling events on Hadoop

HBase

• Concepts and architecture
• HBase vs RDBMS vs Cassandra
• HBase Java API
• Time series data on HBase
• Schema design

Oozie

• Introduction to Oozie
• Features of Oozie
• Oozie Workflow
• Creating a MapReduce Workflow
• Start, End, and Error Nodes
• Parallel Fork and Join Nodes
• Workflow Jobs Lifecycle
• Workflow Notifications
• Workflow Manager
• Creating and Running a Workflow
• Oozie Coordinator Sub-groups
• Oozie Coordinator Components, Variables, and Parameters

Working with Hive

• Architecture and design
• Data types
• SQL support in Hive
• Creating Hive tables and querying
• Particiones
• Joins
• Text processing
• Labs: various labs on processing data with Hive

Hive advanced

• Transformation and Aggregation
• Working with Dates, Timestamps, and Arrays
• Converting Strings to Date, Time, and Numbers
• Create new Attributes, Mathematical Calculations, and Windowing Functions
• Use Character and String Functions
• Binning and Smoothing
• Processing JSON Data
• Execution Engines (Tez, MR, Spark)

Hive in Cloudera or HortonWorks Distribution (or tools of choice)

• Impala architecture
• Impala joins and other SQL specifics

Spark Basics

• Big Data, Hadoop, and Spark
• What’s new in Spark v2
• Spark concepts and architecture
• Spark ecosystem (core, spark sql, mlib, and streaming)

Spark Shell

• Spark web UIs
• Analyzing dataset

RDDs

• RDDs concepts
• RDD Operations/transformations
• Labs: Unstructured data analytics using RDDs
• Data model concepts
• Particiones
• Distributed processing
• Failure handling
• Caching and persistence
• Spark Dataframes and Datasets
• Intro to Dataframe/Dataset
• Programming in Dataframe/Dataset API
• Loading structured data using Dataframes

Spark SQL

• Spark SQL concepts and overview
• Defining tables and importing datasets
• Querying data using SQL
• Handling various storage formats: JSON/Parquet/ORC

Spark API programming (Scala and Python)

• Introduction to Spark API
• Submitting the first program to Spark
• Debugging/logging
• Configuration properties

Spark and Hadoop

• Hadoop Primer: HDFS/YARN
• Hadoop + Spark architecture
• Running Spark on YARN
• Processing HDFS files using Spark
• Spark and Hive

Machine Learning (ML/MLlib)

• Machine Learning primer
• Machine Learning in Spark: MLlib/ML
• Spark ML overview (newer Spark2 version)
• Algorithms: Clustering, Classifications, and Recommendations

GraphX

• GraphX library overview
• GraphX APIs

Spark Streaming

• Streaming concepts
• Evaluating Streaming platforms
• Spark streaming library overview
• Streaming operations
• Sliding window operations
• Structured Streaming
• Continuous streaming
• Spark and Kafka streaming