Cloudera Developer Training: Spark and Hadoop (4 days)
€ 2,180.00 excl. VAT
This four-day hands-on training course delivers the key concepts and expertise participants need to ingest and process data on a Hadoop cluster using the most up-to-date tools and techniques.
This four-day hands-on training course delivers the key concepts and expertise participants need to ingest and process data on a Hadoop cluster using the most up-to-date tools and techniques. Employing Hadoop ecosystem projects such as Spark, Hive, Flume, Sqoop, and Impala, this training course is the best preparation for the real-world challenges faced by Hadoop developers. Participants learn to identify which tool is the right one to use in a given situation, and will gain hands-on experience in developing using those tools.
Through instructor-led discussion and interactive, hands-on exercises, participants will learn Apache Spark and how it integrates with the entire Hadoop ecosystem, learning:
- How data is distributed, stored, and processed in a Hadoop cluster
- How to use Sqoop and Flume to ingest data
- How to process distributed data with Apache Spark
- How to model structured data as tables in Impala and Hive
- How to choose the best data storage format for different data usage patterns
- Best practices for data storage
- This course is designed for developers and engineers who have programming experience.
- Apache Spark examples and hands-on exercises are presented in Scala and Python, so the ability to program in one of those languages is required.
- Basic familiarity with the Linux command line is assumed.
- Basic knowledge of SQL is helpful.
- Prior knowledge of Hadoop is not required.
Introduction to Hadoop and the Hadoop Ecosystem
- Problems with Traditional Large-scale Systems
- The Hadoop EcoSystem
Hadoop Architecture and HDFS
- Distributed Processing on a Cluster
- Storage: HDFS Architecture
- Storage: Using HDFS
- Resource Management: YARN
- Architecture Resource Management: Working with YARN
Importing Relational Data with Apache Sqoop Sqoop Overview
- Basic Imports and Exports
- Limiting Results
- Improving Sqoop’s Performance
- Sqoop 2
Introduction to Impala and Hive Introduction to Impala and Hive
- Why Use Impala and Hive?
- Comparing Hive to Traditional Databases
- Hive Use Cases
Modeling and Managing Data with Impala and Hive
- Data Storage Overview
- Creating Databases and Tables Loading Data into Tables
- Impala Metadata Caching
- Selecting a File Format
- Hadoop Tool Support for File Formats
- Avro Schemas
- Using Avro with Hive and Sqoop
- Avro Schema Evolution
- Partitioning Overview
- Partitioning in Impala and Hive
Capturing Data with Apache Flume
- What is Apache Flume?
- Basic Flume Architecture
- Flume Sources
- Flume Sinks
- Flume Channels
- Flume Configuration
- What is Apache Spark?
- Using the Spark Shell
- RDDs (Resilient Distributed Datasets)
- Functional Programming in Spark
Working with RDDs in Spark
- A Closer Look at RDDs
- Key-Value Pair RDDs
- Other Pair RDD Operations
Writing and Deploying Spark Applications
- Spark Applications vs. Spark Shell
- Creating the SparkContext
- Building a Spark Application (Scala and Java)
- Running a Spark Application
- The Spark Application Web UI
- Configuring Spark Properties
Parallel Programming with Spark Review: Spark on a Cluster
- RDD Partitions
- Partitioning of File-based RDDs
- HDFS and Data Locality
- Executing Parallel Operations
- Stages and Tasks
Spark Caching and Persistence
- RDD Lineage
- Caching Overview
- Distributed Persistence
Common Patterns in Spark Data Processing
- Common Spark Use Cases
- Iterative Algorithms in Spark
- Graph Processing and Analysis
- Machine Learning
- Example: k-means
Preview: Spark SQL
- Spark SQL and the SQL Context
- Creating DataFrames
- Transforming and Querying DataFrames
- Saving DataFrames
- Comparing Spark SQL with Impala
The participants will obtain certificates signed by Cloudera (training completion). Also this course is an excellent place to start for people working towards the CCP: Data Engineer certification. Although further study is required before passing the exam , this course covers many of the subjects tested in the CCP: Data Engineer exam.
Certified Cloudera Instructor.
Kraków, Warszawa, Prague, Budapest
Czech Republic, Hungaria, Poland
10 Jun 2019, 15 Apr 2019, 18 Mar 2019, 8 Apr 2019
- Store Name: Compendium Education Center
- Vendor: Jakub Frankowski
ul. Tatarska 5
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