Data Access
Copyright © 2012-2017 Hortonworks, Inc.
 | Except where otherwise noted, this document is licensed under Creative Commons Attribution ShareAlike 4.0 License |
2017-10-30
Abstract
The Hortonworks Data Platform, powered by Apache Hadoop, is a massively scalable and 100% open source platform for storing, processing and analyzing large volumes of data. It is designed to deal with data from many sources and formats in a very quick, easy and cost-effective manner. The Hortonworks Data Platform consists of the essential set of Apache Hadoop projects including YARN, Hadoop Distributed File System (HDFS), HCatalog, Pig, Hive, HBase, ZooKeeper and Ambari. Hortonworks is the major contributor of code and patches to many of these projects. These projects have been integrated and tested as part of the Hortonworks Data Platform release process and installation and configuration tools have also been included.
Unlike other providers of platforms built using Apache Hadoop, Hortonworks contributes 100% of our code back to the Apache Software Foundation. The Hortonworks Data Platform is Apache-licensed and completely open source. We sell only expert technical support, training and partner-enablement services. All of our technology is, and will remain, free and open source.
Please visit the Hortonworks Data Platform page for more information on Hortonworks technology. For more information on Hortonworks services, please visit either the Support or Training page. Feel free to Contact Us directly to discuss your specific needs.
Contents
- 1. What's New in Data Access for HDP 2.6
- 2. Data Warehousing with Apache Hive
- 3. Enabling Efficient Execution with Apache Pig and Apache Tez
- 4. Managing Metadata Services with Apache HCatalog
- 5. Persistent Read/Write Data Access with Apache HBase
- 6. Orchestrating SQL and APIs with Apache Phoenix
- 7. Real-Time Data Analytics with Druid
List of Figures
- 2.1. Example: Moving .CSV Data into Hive
- 2.2. Using Sqoop to Move Data into Hive
- 2.3. Data Ingestion Lifecycle
- 2.4. Dataset after the UNION ALL Command Is Run
- 2.5. Dataset in the View
- 5.1. HBase Read/Write Operations
- 5.2. Relationship among Different BlockCache Implementations and MemStore
- 5.3. Diagram of Configuring BucketCache
- 5.4. Intracluster Backup
- 5.5. Backup-Dedicated HDFS Cluster
- 5.6. Backup to Vendor Storage Solutions
- 5.7. Tables Composing the Backup Set
- 7.1. Batch Ingestion of Hive Data into Druid
- 7.2. Example of Column Categorization in Druid
List of Tables
- 2.1. Hive Content roadmap
- 2.2. CBO Configuration Parameters
- 2.3. Hive Compaction Types
- 2.4. Hive Transaction Configuration Parameters
- 2.5. Configuration Parameters for Standard SQL Authorization
- 2.6. HiveServer2 Command-Line Options
- 2.7. Trailing Whitespace Characters on Various Databases
- 2.8. Beeline Modes of Operation
- 2.9. HiveServer2 Transport Modes
- 2.10. Authentication Schemes with TCP Transport Mode
- 2.11. Most Common Methods to Move Data into Hive
- 2.12. Sqoop Command Options for Importing Data into Hive
- 5.1. HBase Content Roadmap in Other Sources
- 5.2. MOB Cache Properties
- 5.3. Quota Support Matrix
- 5.4. Scenario 1: Overlapping Quota Policies
- 5.5. Scenario 2: Overlapping Quota Policies
- 5.6. Scenario 3: Overlapping Quota Policies
- 5.7. Scenario 4: Overlapping Quota Policies
- 7.1. Druid Content Roadmap in Other Sources
- 7.2. Advanced Druid Identity Properties of Ambari Kerberos Wizard
- 7.3. Explanation of Table Properties for Hive to Druid ETL
- 7.4. Performance-Related Properties
List of Examples