Hadoop Skill Overview
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- Category: Technical > Business intelligence and data analysis
Description
Hadoop is a powerful open-source framework that allows for the processing and storage of large data sets across clusters of computers. It's designed to scale up from single servers to thousands of machines, each offering local computation and storage. Hadoop skills involve understanding its core components like MapReduce for processing large data sets, HDFS for high-throughput access to application data, and YARN for job scheduling. Proficiency also includes working with tools in the Hadoop ecosystem such as Hive, Pig, and Spark for data analysis, Sqoop and Flume for data loading, and HBase for NoSQL databases. Advanced skills include optimizing performance, securing clusters, and implementing complex business solutions.
Expected Behaviors
Micro Skills
Understanding the basic principle of Big Data
Understanding the significance of Big Data in modern business
Familiarity with the challenges of traditional systems in handling Big Data
Knowledge of the types of Big Data: Structured, Semi-structured and Unstructured
Introduction to Hadoop as a Big Data solution
Understanding the basic components of Hadoop: HDFS, MapReduce, and YARN
Familiarity with the various tools in the Hadoop ecosystem: Hive, Pig, HBase, Sqoop, Flume, etc.
Awareness of the role and use cases of Hadoop in different industries
Understanding the basic principle of MapReduce
Awareness of the two phases in MapReduce: Mapping and Reducing
Basic knowledge of how MapReduce processes data in Hadoop
Introduction to HDFS as the storage unit of Hadoop
Understanding the distributed and scalable nature of HDFS
Familiarity with the concepts of Data Blocks and Replication in HDFS
Understanding the role of YARN in managing resources in a Hadoop cluster
Basic knowledge of the components of YARN: ResourceManager, NodeManager, and ApplicationMaster
Awareness of how YARN schedules and runs applications in Hadoop
Understanding system requirements for Hadoop installation
Installing Java Development Kit (JDK)
Setting up Hadoop user environment
Configuring Hadoop core components like HDFS and YARN
Starting and stopping Hadoop services
Understanding the MapReduce programming model
Writing simple MapReduce jobs in Java or Python
Debugging MapReduce jobs
Testing MapReduce jobs with sample data
Packaging and deploying MapReduce jobs to a Hadoop cluster
Understanding the role of Sqoop and Flume in Hadoop ecosystem
Importing data from relational databases into HDFS using Sqoop
Exporting data from HDFS to relational databases using Sqoop
Collecting, aggregating and moving large amounts of log data with Flume
Configuring Flume agents and channels
Understanding the role of Hive and Pig in Hadoop ecosystem
Creating and managing tables in Hive
Writing Hive queries for data analysis
Writing Pig scripts for data transformation
Running Hive and Pig jobs on Hadoop cluster
Understanding Hadoop cluster architecture
Adding and removing nodes in a Hadoop cluster
Monitoring Hadoop cluster health and performance
Troubleshooting common Hadoop cluster issues
Using Hadoop administration tools like Ambari and Cloudera Manager
Understanding of advanced MapReduce concepts
Ability to write complex MapReduce jobs
Knowledge of different types of Input and Output formats
Proficiency in using Counters in Hadoop MapReduce
Experience with Data Localization in Hadoop
Understanding of Spark architecture and its components
Ability to write Spark applications for data processing
Knowledge of Spark RDD (Resilient Distributed Dataset)
Experience with Spark SQL for structured data processing
Familiarity with Spark Streaming for real-time data processing
Advanced knowledge of HiveQL and Pig Latin scripting
Experience with complex data analysis tasks using Hive and Pig
Understanding of partitioning and bucketing in Hive
Ability to optimize Hive and Pig queries for performance
Familiarity with UDFs (User Defined Functions) in Hive and Pig
Understanding of HBase architecture and its components
Ability to create, update and delete operations in HBase
Knowledge of HBase schema design
Experience with HBase Shell and HBase API
Familiarity with data modeling in HBase
Understanding of ETL process and its importance in Big Data
Ability to implement ETL operations using Hadoop ecosystem tools
Experience with data extraction from various sources using Sqoop and Flume
Knowledge of data transformation using MapReduce, Hive and Pig
Familiarity with data loading into HDFS or HBase
Understanding of MapReduce job internals
Proficiency in using counters in Hadoop
Knowledge of MapReduce job tuning parameters
Ability to use compression in MapReduce jobs
Experience with different types of InputFormats and OutputFormats
Proficiency in using Spark SQL for data manipulation
Experience with Spark Streaming for real-time data processing
Ability to integrate Spark with Hadoop ecosystem tools
Knowledge of Spark performance tuning techniques
Understanding of Storm architecture and its components
Ability to create Storm topologies for data processing
Experience with Trident, a high-level abstraction for Storm
Knowledge of integrating Storm with other Hadoop ecosystem tools
Understanding of Storm performance tuning techniques
Understanding of Kerberos principles and operation
Ability to configure Kerberos for Hadoop
Experience with managing and troubleshooting Kerberos issues
Knowledge of integrating Kerberos with other Hadoop ecosystem tools
Understanding of best practices for securing Hadoop clusters
Ability to design and implement ETL pipelines
Experience with data warehousing solutions like Hive
Proficiency in using NoSQL databases like HBase
Understanding of machine learning algorithms with Mahout
Ability to integrate various Hadoop ecosystem tools to solve complex business problems
Understanding the detailed workings of HDFS and MapReduce
Designing robust Hadoop architectures with failover and recovery strategies
Planning and executing large scale data migrations to Hadoop
Optimizing data storage with techniques like data compression and serialization
Writing efficient Spark programs for complex data processing tasks
Tuning Spark parameters for optimal performance
Optimizing Spark code and data structures
Integrating Spark with Hadoop and other big data tools
Implementing various machine learning algorithms using Mahout
Optimizing machine learning models for performance
Applying machine learning techniques to real-world problems
Integrating Mahout with Hadoop for large scale machine learning tasks
Identifying and resolving node failures
Implementing disaster recovery strategies
Planning and executing data backup strategies
Understanding business requirements and translating them into Hadoop solutions
Designing and implementing ETL pipelines in Hadoop
Integrating Hadoop with existing enterprise systems
Ensuring data security and privacy in Hadoop solutions
Tech Experts
StackFactor Team
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