K Labs mette a tua disposizione i propri Trainers Certificati, i Laboratori Didattici, i Simulatori di Esame, il proprio Test Center e un Tutor a te dedicato per la preparazione all'esame.
Grazie al nostro supporto la percentuale di candidati che ottengono la certificazione al primo tentativo è prossima al 100%.
.jpeg "fabio-oyxis2kalvg-unsplash (1).jpeg")
Rimani aggiornato sulle novità formative.
Lasciaci la tua e-mail!
Rimani aggiornato sulle novità formative.
Lasciaci la tua e-mail!
RICERCA CORSI
08-KLA-ATRWS - Network Analysis and Troubleshooting Through Wireshark (ATRWS) 
NewCondition € 2281.40
Pre-Order
Pre-Order
01-KLA-DVBTV - DVB: Architectures, Management and Testing for Digital Television (DVBTV) 
NewCondition € 2281.40
Pre-Order
Pre-Order
1 2 3 4 5 › 18
HCIP-Big Data Developer
HUAWEI BIG DATA CLOUD, Huawei, bigdata, mapreduce
NewCondition € 1300.00
Pre-Order
Pre-Order
OBJECTIVES
On completion of this course, you will be able to:
1. Master advanced theories and practice methods of big data development.
2. Master advanced usage methods of big data development on HUAWEI CLOUD.
3. Master the database routine management methods based on DWS.
PREREQUISITES
Be familiar with SQL basics, Big data basics and basic development on Linux operating systems.
WHO SHOULD ATTEND
1. Those who hope to become big data Developer engineers
2. Those who hope to obtain an HCIP-Big Data Developer certificate
3. Senior engineers of big data Developer
TOPICS
Chapter 1 Big Data Application Development Guide (0.5 Day)
1. Mainstream big data technologies
2. Scenario-based big data solution
3. Big data application development
Chapter 2 Big Data Scenario-based Solution — Offline Processing (1.5 Days)
1. Offline batch processing solution
2. Introduction, technical principles, parameter attributes, and important configurations of the offline batch processing framework, including data storage (HDFS), data warehouse (Hive), offline analysis tool (SparkSQL), and data collection tools (Loader, Sqoop, and Kettle)
3. Offline batch processing cases
Chapter 3 Big Data Scenario-based Solution — Real-Time Retrieval (2 Days)
1. Real-time retrieval solution
2. Introduction, technical principles, parameter attributes, and important configurations of distributed databases, including HBase, ElasticSearch, and GES
3. Real-time retrieval cases
Chapter 4 Big Data Scenario-based Solution — Real-Time Stream Processing (2 Days)
1. Real-time stream computing application solution
2. Introduction, technical principles, parameter attributes, and important configurations of the real-time stream computing components, including Flume, Kafka, Flink, SparkStreaming, and Redis
3. Real-time stream processing configuration solution and success cases
Chapter 5 Big Data Scenario-based Solution — Converged Data Warehouse (2 Days)
1. Background of Data Warehouse
2. Introduction to DWS
3. Converged data warehouse cases
On completion of this course, you will be able to:
1. Master advanced theories and practice methods of big data development.
2. Master advanced usage methods of big data development on HUAWEI CLOUD.
3. Master the database routine management methods based on DWS.
PREREQUISITES
Be familiar with SQL basics, Big data basics and basic development on Linux operating systems.
WHO SHOULD ATTEND
1. Those who hope to become big data Developer engineers
2. Those who hope to obtain an HCIP-Big Data Developer certificate
3. Senior engineers of big data Developer
TOPICS
Chapter 1 Big Data Application Development Guide (0.5 Day)
1. Mainstream big data technologies
2. Scenario-based big data solution
3. Big data application development
Chapter 2 Big Data Scenario-based Solution — Offline Processing (1.5 Days)
1. Offline batch processing solution
2. Introduction, technical principles, parameter attributes, and important configurations of the offline batch processing framework, including data storage (HDFS), data warehouse (Hive), offline analysis tool (SparkSQL), and data collection tools (Loader, Sqoop, and Kettle)
3. Offline batch processing cases
Chapter 3 Big Data Scenario-based Solution — Real-Time Retrieval (2 Days)
1. Real-time retrieval solution
2. Introduction, technical principles, parameter attributes, and important configurations of distributed databases, including HBase, ElasticSearch, and GES
3. Real-time retrieval cases
Chapter 4 Big Data Scenario-based Solution — Real-Time Stream Processing (2 Days)
1. Real-time stream computing application solution
2. Introduction, technical principles, parameter attributes, and important configurations of the real-time stream computing components, including Flume, Kafka, Flink, SparkStreaming, and Redis
3. Real-time stream processing configuration solution and success cases
Chapter 5 Big Data Scenario-based Solution — Converged Data Warehouse (2 Days)
1. Background of Data Warehouse
2. Introduction to DWS
3. Converged data warehouse cases
