Big Data Course Cloudera Company Lecturer CCDH Ccah CCP

March Shanghai Opening Hours:

Administrator (March 1-4th) Cloudera certified Administrator for Apache Hadoop;

Developer (March 23-26th) Cloudera certifed Developer for Spark and Hadoop;

"For other courses, please ask" 15601685012 (Mr. Gong)

QQ Group: Cloudera Big Data Training 308453209

Course Content:

"Cloudera certified Administrator for Apache Hadoop Administrator Course"

Hours: 4 days

Learn the concepts of system management and best practices for Apache Hadoop;

From installation and configuration to load balancing and tuning;

This 4-day course gives you deployment experience through hands-on time;

As well as more security aspects of experience and troubleshooting;

At the end of the course, participants are encouraged to participate in the Cloudera and Apache Hadoop Administrator (CCAH) exams, which can be registered as agents;

"Course content"

1. Hadoop Distributed File System (HDFS)

2. Working principle of Yarn/mapreduce

3. How to optimize the hardware configuration required by the Hadoop fleet

4. Network factors to be considered in building a Hadoop cluster

5. Hadoop Cluster maintenance and control

6. How to use Hadoop configuration options for system performance tuning

7, how to use Fairscheduler to provide service level protection for multi-users

8. Hadoop Cluster maintenance and monitoring

9. How to load a dynamically generated file into Hadoop using flume

10. How to use Sqoop to load data from relational data

11. Hadoop Ecosystem tools (such as Hive, Impala, pig, and base)

"Student Base"

Experience with basic Linux system management. No prior knowledge of Hadoop is required.

"Teaching Style"

Case teaching + on-machine practice

"Cloudera certifed Developer for Spark and Hadoop developer Course"

Hours: 4 days

Learn about the Hadoop Distributed File System (HDFS) Foundation and the MapReduce framework and how to use its APIs to write programs.

Discuss designing techniques for larger workflows.

This 4-day course covers the advanced techniques needed to fix bugs and optimize performance for the MapReduce program.

The programmer's program also introduces Apache ecological projects such as Hive, Pig, HBase, Flume, and Oozie.

"Course content"

1. MapReduce and HDFs kernel knowledge and how to write a MapReduce program

2, the best practice of Hadoop development, debugging, implementation workflow and general algorithm

3. How to use Hive, Pig, Sqoop, Flume, Oozie and other Hadoop components

4, on-demand custom writablecomparables and inputformats processing complex data types

5. Use MapReduce to write and perform connection operations to integrate different data sets

6. Advanced Hadoopapi topics for real-world data analysis

7, write the MapReduce program with Java, write the MapReduce program with streaming

8, debugging the MapReduce code strategy, using Localjobrunner to test the MapReduce code locally

9, Partitioners and reducers How to work together, customized Partitioners

10. Custom Writable and Writablecomparable

11. Storing binary data with Sequencefile and Avro data files

"Student Base"

This course is suitable for program developers with some programming experience. Familiarity with Java is preferred due to the need to complete Hadoop-related programming exercises in the course

"Teaching Style"

Case teaching + on-machine practice

"Cloudera Data Analysis Course"

Hours: 4 days

For anyone who needs to manage, manipulate, and query large, complex data in real time through SQL and familiar scripts on Hadoop.

Learn how Apache Pig, Apache Hive, and Cloudera Impala can be filtered by joins and other user-defined features

To support the transformation and analysis of data.

"Course content"

1. Hadoop ecosystem, Introduction to experimental scenarios, importing data with Hadoop tools

2, pig characteristics, use cases, and pig interaction, pig Latin syntax, field definition, with pig to perform ETL process

3, pig processing complex data, complex/nested nested data types, with pig analysis of advertising campaign data

4, Pig's majority group operation, pig link multi-data group, with pig analysis discrete Data group

5. Use stream processing and UDFs to expand Pig,macros and imports, contributed functions, and process data with pig in other languages

6, Pig troubleshooting and optimization, with the Web interface to troubleshoot a fault task, data sampling and troubleshooting, Understanding the execution plan, improve pig task performance

7. Hive table structure and data storage, contrast hive and traditional database, hive Vs.pig, hive use case

8. Hive's relational data analysis, data management, text processing, optimization and extension, running hive queries on shells, scripts, and hue

9. Different Impala and hive, pig, relational database, using Impala shell

10. Sampling Impala analysis data, filtering, sorting and limiting results, enhancing impala performance, Impala Interactive analysis

11. Compare map reduce, pig, hive, Impala, and relational database

"Student Base"

This course is suitable for data analysts, business analysts, and administrators with SQL experience and basic UNIX and Linux commands

No prior experience with Java and Apache Hadoop

"Teaching Style"

Case teaching + on-machine practice

==============================================================

Teacher Shaocheng (Cloudera, pre-sales technical manager)

• Cloudera Company System Engineer
• Cloudera Administrator Certified Instructor
• Prior to joining Cloudera, we led the Intel Big Data team to successfully implement the first batch of big data platform construction projects in China, including ABC, Pacific Insurance and Shanghai Telecom Big Data platform, as the technology leader of Intel Big Data Division Solution Department. Prior to joining Intel, Shaocheng was an advanced software engineer for ebay, responsible for billing and payment system construction on ebay's e-commerce website.

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