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Apache Hive Essentials

By : Dayong Du

4.5 (4)

Apache Hive Essentials

4.5 (4)

By: Dayong Du

Overview of this book

If you are a data analyst, developer, or simply someone who wants to use Hive to explore and analyze data in Hadoop, this is the book for you. Whether you are new to big data or an expert, with this book, you will be able to master both the basic and the advanced features of Hive. Since Hive is an SQL-like language, some previous experience with the SQL language and databases is useful to have a better understanding of this book.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Overview of Big Data and Hive

1. Overview of Big Data and Hive

A short history

Introducing big data

Relational and NoSQL database versus Hadoop

Batch, real-time, and stream processing

Overview of the Hadoop ecosystem

Hive overview

Summary

2. Setting Up the Hive Environment

2. Setting Up the Hive Environment

Installing Hive from Apache

Installing Hive from vendor packages

Starting Hive in the cloud

Using the Hive command line and Beeline

The Hive-integrated development environment

Summary

3. Data Definition and Description

3. Data Definition and Description

Understanding Hive data types

Data type conversions

Hive Data Definition Language

Hive database

Hive internal and external tables

Hive partitions

Hive buckets

Hive views

Summary

4. Data Selection and Scope

4. Data Selection and Scope

The SELECT statement

The INNER JOIN statement

The OUTER JOIN and CROSS JOIN statements

Special JOIN – MAPJOIN

Set operation – UNION ALL

Summary

5. Data Manipulation

5. Data Manipulation

Data exchange – LOAD

Data exchange – INSERT

Data exchange – EXPORT and IMPORT

ORDER and SORT

Operators and functions

Transactions

Summary

6. Data Aggregation and Sampling

6. Data Aggregation and Sampling

Basic aggregation – GROUP BY

Advanced aggregation – GROUPING SETS

Advanced aggregation – ROLLUP and CUBE

Aggregation condition – HAVING

Analytic functions

Sampling

Summary

7. Performance Considerations

7. Performance Considerations

Performance utilities

Design optimization

Data file optimization

Job and query optimization

Summary

8. Extensibility Considerations

8. Extensibility Considerations

User-defined functions

Streaming

SerDe

Summary

9. Security Considerations

9. Security Considerations

Authentication

Authorization

Encryption

Summary

10. Working with Other Tools

10. Working with Other Tools

JDBC / ODBC connector

HBase

Hue

HCatalog

ZooKeeper

Oozie

Hive roadmap

Summary

Index

Index

Encryption

For sensitive and legally protected data such as personal identity information (PII), it is required to store the data in encrypted format in the filesystem. However, Hive does not natively support encryption and decryption yet (see https://issues.apache.org/jira/browse/HIVE-5207).

Alternatively, we can look for third-party tools to encrypt and decrypt data after exporting it from Hive, but this requires additional postprocessing. The new HDFS encryption (see https://issues.apache.org/jira/browse/HDFS-6134) offers great transparent encryption and decryption of data on HDFS. It will satisfy our request if we want to encrypt the whole dataset in HDFS. However, it cannot be applied to the selected column and row level in the table of Hive, where most PII that is encrypted is only a part of raw data. In this case, the best solution for now is to use Hive UDF to plug in encryption and decryption implementations on selected columns or partial data in the Hive tables.

Sample UDF implementations...

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Tech Concepts

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Programming languages

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