77 Hadoop courses

***24 Hour Limited Time Flash Sale*** Software and Web Development Admission Gifts FREE PDF & Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter How about launching your software engineering or web development career through comprehensive training across today's most in-demand programming languages? As technology reshapes industries across the UK, skilled coders are in short supply, with over 300,000 developer roles needed by 2025. This Software and Web Development bundle equips you with the skills to join this exciting field, whether you're a complete beginner or looking to upskill. Designed to cater to the needs of both seasoned professionals and aspiring newcomers, our Software and Web Development bundle is a comprehensive program that will equip you with the essential skills and knowledge you need to succeed. 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Duration 4 Days 24 CPD hours This course is intended for This course is appropriate for developers and administrators who intend to use HBase. Overview Skills learned on the course include:The use cases and usage occasions for HBase, Hadoop, and RDBMSUsing the HBase shell to directly manipulate HBase tablesDesigning optimal HBase schemas for efficient data storage and recoveryHow to connect to HBase using the Java API, configure the HBase cluster, and administer an HBase clusterBest practices for identifying and resolving performance bottlenecks Cloudera University?s four-day training course for Apache HBase enables participants to store and access massive quantities of multi-structured data and perform hundreds of thousands of operations per second. INTRODUCTION TO HADOOP & HBASE * What Is Big Data? * Introducing Hadoop * Hadoop Components * What Is HBase? * Why Use HBase? * Strengths of HBase * HBase in Production * Weaknesses of HBase HBASE TABLES * HBase Concepts * HBase Table Fundamentals * Thinking About Table Design THE HBASE SHELL * Creating Tables with the HBase Shell * Working with Tables * Working with Table Data HBASE ARCHITECTURE FUNDAMENTALS * HBase Regions * HBase Cluster Architecture * HBase and HDFS Data Locality HBASE SCHEMA DESIGN * General Design Considerations * Application-Centric Design * Designing HBase Row Keys * Other HBase Table Features BASIC DATA ACCESS WITH THE HBASE API * Options to Access HBase Data * Creating and Deleting HBase Tables * Retrieving Data with Get * Retrieving Data with Scan * Inserting and Updating Data * Deleting Data MORE ADVANCED HBASE API FEATURES * Filtering Scans * Best Practices * HBase Coprocessors HBASE ON THE CLUSTER * How HBase Uses HDFS * Compactions and Splits HBASE READS & WRITES * How HBase Writes Data * How HBase Reads Data * Block Caches for Reading HBASE PERFORMANCE TUNING * Column Family Considerations * Schema Design Considerations * Configuring for Caching * Dealing with Time Series and Sequential Data * Pre-Splitting Regions HBASE ADMINISTRATION AND CLUSTER MANAGEMENT * HBase Daemons * ZooKeeper Considerations * HBase High Availability * Using the HBase Balancer * Fixing Tables with hbck * HBase Security HBASE REPLICATION & BACKUP * HBase Replication * HBase Backup * MapReduce and HBase Clusters USING HIVE & IMPALA WITH HBASE * Using Hive and Impala with HBase APPENDIX A: ACCESSING DATA WITH PYTHON AND THRIFT * Thrift Usage * Working with Tables * Getting and Putting Data * Scanning Data * Deleting Data * Counters * Filters APPENDIX B: OPENTSDB

Duration 4 Days 24 CPD hours This course is intended for This course is best suited to developers, engineers, and architects who want to use use Hadoop and related tools to solve real-world problems. Overview Skills learned in this course include:Creating a data set with Kite SDKDeveloping custom Flume components for data ingestionManaging a multi-stage workflow with OozieAnalyzing data with CrunchWriting user-defined functions for Hive and ImpalaWriting user-defined functions for Hive and ImpalaIndexing data with Cloudera Search Cloudera University?s four-day course for designing and building Big Data applications prepares you to analyze and solve real-world problems using Apache Hadoop and associated tools in the enterprise data hub (EDH). INTRODUCTION APPLICATION ARCHITECTURE * Scenario Explanation * Understanding the Development Environment * Identifying and Collecting Input Data * Selecting Tools for Data Processing and Analysis * Presenting Results to the Use DEFINING & USING DATASETS * Metadata Management * What is Apache Avro? * Avro Schemas * Avro Schema Evolution * Selecting a File Format * Performance Considerations USING THE KITE SDK DATA MODULE * What is the Kite SDK? * Fundamental Data Module Concepts * Creating New Data Sets Using the Kite SDK * Loading, Accessing, and Deleting a Data Set IMPORTING RELATIONAL DATA WITH APACHE SQOOP * What is Apache Sqoop? * Basic Imports * Limiting Results * Improving Sqoop?s Performance * Sqoop 2 CAPTURING DATA WITH APACHE FLUME * What is Apache Flume? * Basic Flume Architecture * Flume Sources * Flume Sinks * Flume Configuration * Logging Application Events to Hadoop DEVELOPING CUSTOM FLUME COMPONENTS * Flume Data Flow and Common Extension Points * Custom Flume Sources * Developing a Flume Pollable Source * Developing a Flume Event-Driven Source * Custom Flume Interceptors * Developing a Header-Modifying Flume Interceptor * Developing a Filtering Flume Interceptor * Writing Avro Objects with a Custom Flume Interceptor MANAGING WORKFLOWS WITH APACHE OOZIE * The Need for Workflow Management * What is Apache Oozie? * Defining an Oozie Workflow * Validation, Packaging, and Deployment * Running and Tracking Workflows Using the CLI * Hue UI for Oozie PROCESSING DATA PIPELINES WITH APACHE CRUNCH * What is Apache Crunch? * Understanding the Crunch Pipeline * Comparing Crunch to Java MapReduce * Working with Crunch Projects * Reading and Writing Data in Crunch * Data Collection API Functions * Utility Classes in the Crunch API WORKING WITH TABLES IN APACHE HIVE * What is Apache Hive? * Accessing Hive * Basic Query Syntax * Creating and Populating Hive Tables * How Hive Reads Data * Using the RegexSerDe in Hive DEVELOPING USER-DEFINED FUNCTIONS * What are User-Defined Functions? * Implementing a User-Defined Function * Deploying Custom Libraries in Hive * Registering a User-Defined Function in Hive EXECUTING INTERACTIVE QUERIES WITH IMPALA * What is Impala? * Comparing Hive to Impala * Running Queries in Impala * Support for User-Defined Functions * Data and Metadata Management UNDERSTANDING CLOUDERA SEARCH * What is Cloudera Search? * Search Architecture * Supported Document Formats INDEXING DATA WITH CLOUDERA SEARCH * Collection and Schema Management * Morphlines * Indexing Data in Batch Mode * Indexing Data in Near Real Time PRESENTING RESULTS TO USERS * Solr Query Syntax * Building a Search UI with Hue * Accessing Impala through JDBC * Powering a Custom Web Application with Impala and Search

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. OVERVIEW OF DATA SCIENCE AND MACHINE LEARNING AT SCALE OVERVIEW OF THE HADOOP ECOSYSTEM WORKING WITH HDFS DATA AND HIVE TABLES USING HUE INTRODUCTION TO CLOUDERA DATA SCIENCE WORKBENCH OVERVIEW OF APACHE SPARK 2 READING AND WRITING DATA INSPECTING DATA QUALITY CLEANSING AND TRANSFORMING DATA SUMMARIZING AND GROUPING DATA COMBINING, SPLITTING, AND RESHAPING DATA EXPLORING DATA CONFIGURING, MONITORING, AND TROUBLESHOOTING SPARK APPLICATIONS OVERVIEW OF MACHINE LEARNING IN SPARK MLLIB EXTRACTING, TRANSFORMING, AND SELECTING FEATURES BUILDING AND EVAUATING REGRESSION MODELS BUILDING AND EVALUATING CLASSIFICATION MODELS BUILDING AND EVALUATING CLUSTERING MODELS CROSS-VALIDATING MODELS AND TUNING HYPERPARAMETERS BUILDING MACHINE LEARNING PIPELINES DEPLOYING MACHINE LEARNING MODELS ADDITIONAL COURSE DETAILS: Nexus Humans Cloudera Data Scientist Training training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Cloudera Data Scientist Training course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

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A hands-on course to learn about different tools and scenarios to ingest data into Data Lake. This course includes end-to-end demonstrations to ingest, process, and export data using Databricks and HDInsight, which will help you have a good understanding of Azure Data Lake Services.

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