Hadoop: Hive untuk Query SQL - Revision history

Onnowpurbo: /* How Hive Works */

2015-11-14T01:01:44Z

How Hive Works

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	==~~How~~ Hive ~~Works~~==		==Bagaimana Hive Bekerja?==

	The tables in Hive are similar to tables in a relational database, and data units are organized in a taxonomy from larger to more granular units. Databases are comprised of tables, which are made up of partitions. Data can be accessed via a simple query language and Hive supports overwriting or appending data.

	Within a particular database, data in the tables is serialized and each table has a corresponding Hadoop Distributed File System (HDFS) directory. Each table can be sub-divided into partitions that determine how data is distributed within sub-directories of the table directory. Data within partitions can be further broken down into buckets.

	Hive supports all the common primitive data formats such as BIGINT, BINARY, BOOLEAN, CHAR, DECIMAL, DOUBLE, FLOAT, INT, SMALLINT, STRING, TIMESTAMP, and TINYINT. In addition, analysts can combine primitive data types to form complex data types, such as structs, maps and arrays.






			Tabel di Hive mirip dengan tabel dalam database relasional, dan unit data yang diatur dalam taksonomi dari yang lebih besar untuk unit yang lebih rinci. Database terdiri dari tabel, yang dibangun dari partisi. Data dapat diakses melalui bahasa query sederhana dan Hive mendukung overwriting atau menambahkan data.
			Dalam database tertentu, data dalam tabel di-serial-kan dan masing-masing tabel memiliki direktori yang sesuai Hadoop Distributed File System (HDFS). Setiap tabel dapat dibagi menjadi partisi yang menentukan bagaimana data didistribusikan dalam sub-direktori dari direktori tabel. Data dalam partisi dapat lebih lanjut dipecah menjadi bucket.

			Hive mendukung semua format umum data primitif seperti BIGINT, BINARY, BOOLEAN, CHAR, DECIMAL, DOUBLE, FLOAT, INT, SMALLINT, STRING, TIMESTAMP, dan TINYINT. Selain itu, analis dapat menggabungkan tipe data primitif untuk membentuk tipe data yang kompleks, seperti struct, maps dan array.

	==Referensi==		==Referensi==

	* http://hortonworks.com/hadoop/hive/		* http://hortonworks.com/hadoop/hive/

Onnowpurbo: /* Apakah Hive */

2015-11-14T00:58:01Z

Apakah Hive

← Older revision		Revision as of 00:58, 14 November 2015
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	\|-		\|-
	\| Familiar		\| Familiar
	\| Query data ~~with a~~ SQL~~-based language~~		\| Query data dengan bahasa berbasis SQL
	\|-		\|-
	\| ~~Fast~~		\| Cepat
	\| ~~Interactive response times~~, ~~even over huge datasets~~		\| Waktu responds interaktif yang cepat, walaupun dengan dataset yang besar
	\|-		\|-
	\| Scalable ~~and~~ Extensible		\| Scalable dan Extensible
	\| As data ~~variety and~~ volume ~~grows~~, ~~more commodity machines can be added~~, ~~without a corresponding reduction in~~ performance		\| Karena variasi data dan volume data akan berkembang, mesin (komoditas) dapat di tambah, tanpa pengurangan performance.
	\|}		\|}

Onnowpurbo: /* Apakah Hive */

2015-11-14T00:56:09Z

Apakah Hive

← Older revision		Revision as of 00:56, 14 November 2015
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	Keuntungan menggunakan Hive untuk enterprise SQL di Hadoop:		Keuntungan menggunakan Hive untuk enterprise SQL di Hadoop:

	~~Feature Description~~		{\| border="1" cellpadding=2 style="border-collapse: collapse"
	Familiar Query data with a SQL-based language		! Fitur
	Fast Interactive response times, even over huge datasets		! Penjelasan
	Scalable and Extensible As data variety and volume grows, more commodity machines can be added, without a corresponding reduction in performance		\|-
			\| Familiar
			\| Query data with a SQL-based language
			\|-
			\| Fast
			\| Interactive response times, even over huge datasets
			\|-
			\| Scalable and Extensible
			\| As data variety and volume grows, more commodity machines can be added, without a corresponding reduction in performance
			\|}

	==How Hive Works==		==How Hive Works==

Onnowpurbo: /* What Hive Does */

2015-11-14T00:52:37Z

What Hive Does

← Older revision		Revision as of 00:52, 14 November 2015
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	Sumber: http://hortonworks.com/hadoop/hive/		Sumber: http://hortonworks.com/hadoop/hive/

	==~~What~~ Hive ~~Does~~==		==Apakah Hive==

	Hadoop ~~was built to organize and store massive amounts of~~ data ~~of all shapes~~, ~~sizes and formats~~. ~~Because of Hadoop’s “schema~~ on ~~read” architecture~~, a Hadoop ~~cluster is a perfect~~ reservoir ~~of heterogeneous data—structured and unstructured—from a multitude of sources~~.		Hadoop dibangun untuk mengatur dan menyimpan sejumlah besar data dari berbagai bentuk, ukuran dan format. Karena arsitektur Hadoop "schema on read" arsitektur, cluster Hadoop adalah reservoir sempurna data-terstruktur dan tidak terstruktur-dari banyak sumber yang heterogen.

	~~Data analysts use~~ Hive ~~to explore~~, ~~structure and analyze that~~ data, ~~then turn it into actionable business insight~~.		Analis data yang menggunakan Hive untuk mengeksplorasi, struktur dan menganalisis data itu, lalu mengubahnya menjadi wawasan bisnis yang dapat ditindaklanjuti.

	~~Advantages of using~~ Hive ~~for~~ enterprise SQL in Hadoop:		Keuntungan menggunakan Hive untuk enterprise SQL di Hadoop:

	Feature Description		Feature Description
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	Fast Interactive response times, even over huge datasets		Fast Interactive response times, even over huge datasets
	Scalable and Extensible As data variety and volume grows, more commodity machines can be added, without a corresponding reduction in performance		Scalable and Extensible As data variety and volume grows, more commodity machines can be added, without a corresponding reduction in performance


	==How Hive Works==		==How Hive Works==

Onnowpurbo: Hadopp: Hive untuk Query SQL moved to Hadoop: Hive untuk Query SQL

2015-11-13T22:21:40Z

Hadopp: Hive untuk Query SQL moved to Hadoop: Hive untuk Query SQL

← Older revision	Revision as of 22:21, 13 November 2015
(No difference)

Onnowpurbo: New page: Sumber: http://hortonworks.com/hadoop/hive/ ==What Hive Does== Hadoop was built to organize and store massive amounts of data of all shapes, sizes and formats. Because of Hadoop’s “s...

2015-11-13T22:20:32Z

New page: Sumber: http://hortonworks.com/hadoop/hive/ ==What Hive Does== Hadoop was built to organize and store massive amounts of data of all shapes, sizes and formats. Because of Hadoop’s “s...

New page

Sumber: http://hortonworks.com/hadoop/hive/

==What Hive Does==

Hadoop was built to organize and store massive amounts of data of all shapes, sizes and formats. Because of Hadoop’s “schema on read” architecture, a Hadoop cluster is a perfect reservoir of heterogeneous data—structured and unstructured—from a multitude of sources.

Data analysts use Hive to explore, structure and analyze that data, then turn it into actionable business insight.

Advantages of using Hive for enterprise SQL in Hadoop:

Feature Description
Familiar Query data with a SQL-based language
Fast Interactive response times, even over huge datasets
Scalable and Extensible As data variety and volume grows, more commodity machines can be added, without a corresponding reduction in performance

==How Hive Works==

The tables in Hive are similar to tables in a relational database, and data units are organized in a taxonomy from larger to more granular units. Databases are comprised of tables, which are made up of partitions. Data can be accessed via a simple query language and Hive supports overwriting or appending data.

Within a particular database, data in the tables is serialized and each table has a corresponding Hadoop Distributed File System (HDFS) directory. Each table can be sub-divided into partitions that determine how data is distributed within sub-directories of the table directory. Data within partitions can be further broken down into buckets.

Hive supports all the common primitive data formats such as BIGINT, BINARY, BOOLEAN, CHAR, DECIMAL, DOUBLE, FLOAT, INT, SMALLINT, STRING, TIMESTAMP, and TINYINT. In addition, analysts can combine primitive data types to form complex data types, such as structs, maps and arrays.

==Referensi==

* http://hortonworks.com/hadoop/hive/