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Pig Tutorial

The Pig tutorial shows you how to run two Pig scripts in Local mode and Hadoop mode. Local Mode: To run the scripts in local mode, no Hadoop or HDFS installation is required. All files are installed and run from your local host and file system. Hadoop Mode: To run the scripts in hadoop (mapreduce) mode, you need access to a Hadoop cluster and HDFS installation available through Hadoop Virtual Machine provided with this tutorial. Java Installation (Note: already set-up on the Hadoop VM.). 1Go to the /home...

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The Pig tutorial shows you how to run two Pig scripts in Local mode and Hadoop mode. Local Mode: To run the scripts in local mode, no Hadoop or HDFS installation is required. All files are installed and run from your local host and file system. Hadoop Mode: To run the scripts in hadoop (mapreduce) mode, you need access to a Hadoop cluster and HDFS installation available through Hadoop Virtual Machine provided with this tutorial. Java Installation (Note: already set-up on the Hadoop VM.). 1Go to the /home...

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Pig Tutorial | pig-tutorial.blogspot.com Reviews

https://pig-tutorial.blogspot.com

The Pig tutorial shows you how to run two Pig scripts in Local mode and Hadoop mode. Local Mode: To run the scripts in local mode, no Hadoop or HDFS installation is required. All files are installed and run from your local host and file system. Hadoop Mode: To run the scripts in hadoop (mapreduce) mode, you need access to a Hadoop cluster and HDFS installation available through Hadoop Virtual Machine provided with this tutorial. Java Installation (Note: already set-up on the Hadoop VM.). 1Go to the /home...

INTERNAL PAGES

pig-tutorial.blogspot.com

1

Pig Tutorial: April 2011

http://www.pig-tutorial.blogspot.com/2011_04_01_archive.html

The Pig tutorial shows you how to run two Pig scripts in Local mode and Hadoop mode. Local Mode: To run the scripts in local mode, no Hadoop or HDFS installation is required. All files are installed and run from your local host and file system. Hadoop Mode: To run the scripts in hadoop (mapreduce) mode, you need access to a Hadoop cluster and HDFS installation available through Hadoop Virtual Machine provided with this tutorial. Java Installation (Note: already set-up on the Hadoop VM.). 1Go to the /home...

2

Pig Tutorial: Pig Latin Data Types

http://www.pig-tutorial.blogspot.com/2011/04/pig-latin-data-types.html

Pig Latin Data Types. Values in Pig Latin can be expressed by four basic data types:. An atom is any atomic value (e.g., "fish"). A tuple is a record of multiple values with fixed arity. e.g., ("dog", "sparky"). A data bag is a collection of an arbitrary number of values. e.g., {("dog", "sparky"), ("fish", "goldie")}. Data bags support a scan operation for iterating through their contents. A data map is a collection with a lookup function translating keys to values. e.g., ["age" : 25].

3

Pig Tutorial: Pig Tutorial

http://www.pig-tutorial.blogspot.com/2011/04/pig-tutorial.html

The Pig tutorial shows you how to run two Pig scripts in Local mode and Hadoop mode. Local Mode: To run the scripts in local mode, no Hadoop or HDFS installation is required. All files are installed and run from your local host and file system. Hadoop Mode: To run the scripts in hadoop (mapreduce) mode, you need access to a Hadoop cluster and HDFS installation available through Hadoop Virtual Machine provided with this tutorial. Java Installation (Note: already set-up on the Hadoop VM.). 1Go to the /home...

4

Pig Tutorial: Pig Latin Operators

http://www.pig-tutorial.blogspot.com/2011/04/pig-latin-operators.html

Pig Latin provides a number of operators which filter, join, or otherwise organize data. FOREACH: The FOREACH command operates on each element of a data bag. This is useful, for instance, for processing each input record in a bag returned by a LOAD statement. FOREACH bagname GENERATE expression, expression. FOREACH queries GENERATE userId;. Expressions emitted by the GENERATE element are not limited to the names of fields; they can be fields (by name like userId or by position like $0), constants, algebr...

5

Pig Tutorial: Loading Data Into Pig

http://www.pig-tutorial.blogspot.com/2011/04/loading-data-into-pig.html

Loading Data Into Pig. The first step in using Pig is to load data into a program. Pig provides a LOAD statement for this purpose. Its format is: result = LOAD 'filename' USING fn() AS (field1, field2, .). An example data loading command (taken from this paper on Pig) is:. Queries = LOAD 'query log.txt'. AS (userId, queryString, timestamp). Data = LOAD 'tab delim data.txt' USING PigStorage(' t') AS (user, time, query). May 19, 2011 at 3:21 PM. Great But the interesting thing is to write a class to load d...

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oracle-datawarehousing.blogspot.com

Compare OLTP and Data Warehousing Environments ~ ORACLE DATAWAREHOUSE

http://oracle-datawarehousing.blogspot.com/2011/02/compare-oltp-and-data-warehousing.html

All about Data Warehousing with Oracle. Error Logging and Handling. Compare OLTP and Data Warehousing Environments. Following figure shows the key differences between OLAP and DataWarehousing Environments. One major difference between the types of system is that data warehouses are not. Usually in third normal form (3NF), a type of data normalization common in OLTP. Data warehouses and OLTP systems have very different requirements. Here are some. 9632; Data modifications. 9632; Schema design. OLTP system...

oracle-datawarehousing.blogspot.com

Data Warehouse Architectures ~ ORACLE DATAWAREHOUSE

http://oracle-datawarehousing.blogspot.com/2011/02/data-warehouse-architectures.html

All about Data Warehousing with Oracle. Error Logging and Handling. Data warehouses and their architectures vary depending upon the specifics of an. Organization's situation. Three common architectures are:. 9632; Data Warehouse Architecture (Basic). 9632; Data Warehouse Architecture (with a Staging Area). 9632; Data Warehouse Architecture (with a Staging Area and Data Marts). Data Warehouse Architecture (Basic). Warehouses because they pre-compute long operations in advance. For example, a. Your warehou...

mapreduce-tutorial.blogspot.com

MapReduce Tutorial: April 2011

http://mapreduce-tutorial.blogspot.com/2011_04_01_archive.html

Using Amazon Web Services. Hadoop's power comes from its ability to perform work on a large number of machines simultaneously. What if you want to experiment with Hadoop, but do not have many machines? While operations on a two or four-node cluster are functionally equivalent to those on a 40 or 100-node cluster, processing larger volumes of data will require a larger number of nodes. After the cluster has been started, you can log in to the head node over ssh with the bin/hadoop-ec2 login script, and pe...

oracle-datawarehousing.blogspot.com

Hardware and I/O Considerations in Data Warehouses ~ ORACLE DATAWAREHOUSE

http://oracle-datawarehousing.blogspot.com/2011/02/hardware-and-io-considerations-in-data.html

All about Data Warehousing with Oracle. Error Logging and Handling. Hardware and I/O Considerations in Data Warehouses. I/O performance should always be a key consideration for data warehouse designers. And administrators. The typical workload in a data warehouse is especially I/O. Intensive, with operations such as large data loads and index builds, creation of. Materialized views, and queries over large volumes of data. The underlying I/O. 9632; Configure I/O for Bandwidth not Capacity. As an example, ...

oracle-datawarehousing.blogspot.com

Data Warehousing Schemas ~ ORACLE DATAWAREHOUSE

http://oracle-datawarehousing.blogspot.com/2011/02/data-warehousing-schemas.html

All about Data Warehousing with Oracle. Error Logging and Handling. A schema is a collection of database objects, including tables, views, indexes, and. Synonyms. You can arrange schema objects in the schema models designed for data. Warehousing in a variety of ways. Most data warehouses use a dimensional model. The model of your source data and the requirements of your users help you design the. Data warehouse schema. You can sometimes get the source model from your. Dimension tables, as shown in Figure.

oracle-datawarehousing.blogspot.com

ETL Tools for Data Warehouses ~ ORACLE DATAWAREHOUSE

http://oracle-datawarehousing.blogspot.com/2011/02/etl-tools-for-data-warehouses.html

All about Data Warehousing with Oracle. Error Logging and Handling. ETL Tools for Data Warehouses. Designing and maintaining the ETL process is often considered one of the most. Difficult and resource-intensive portions of a data warehouse project. Many data. Warehousing projects use ETL tools to manage this process. Oracle Warehouse Builder. OWB), for example, provides ETL capabilities and takes advantage of inherent. Database abilities. Other data warehouse builders create their own ETL tools and.

oracle-datawarehousing.blogspot.com

Overview of Materialized View Management Tasks ~ ORACLE DATAWAREHOUSE

http://oracle-datawarehousing.blogspot.com/2011/02/overview-of-materialized-view.html

All about Data Warehousing with Oracle. Error Logging and Handling. Overview of Materialized View Management Tasks. The motivation for using materialized views is to improve performance, but the. Overhead associated with materialized view management can become a significant. System management problem. When reviewing or evaluating some of the necessary. Materialized view management activities, consider some of the following:. 9632; Identifying what materialized views to create initially. 9632; Refreshing ...

oracle-datawarehousing.blogspot.com

Types of Materialized Views ~ ORACLE DATAWAREHOUSE

http://oracle-datawarehousing.blogspot.com/2011/02/types-of-materialized-views.html

All about Data Warehousing with Oracle. Error Logging and Handling. Types of Materialized Views. The SELECT clause in the materialized view creation statement defines the data that. Subqueries, and materialized views can all be joined or referenced in the SELECT. Clause. You cannot, however, define a materialized view with a subquery in the. SELECT list of the defining query. You can, however, include subqueries elsewhere in. The defining query, such as in the WHERE clause. 9632; Nested Materialized Views.

oracle-datawarehousing.blogspot.com

Creating Materialized Views ~ ORACLE DATAWAREHOUSE

http://oracle-datawarehousing.blogspot.com/2011/02/creating-materialized-views.html

All about Data Warehousing with Oracle. Error Logging and Handling. A materialized view can be created with the CREATE MATERIALIZED VIEW statement. Or using Enterprise Manager. Example illustrates creating an materialized view. Called cust sales mv. Example Creating a Materialized View. CREATE MATERIALIZED VIEW cust sales mv. PCTFREE 0 TABLESPACE demo. STORAGE (INITIAL 16k NEXT 16k PCTINCREASE 0). ENABLE QUERY REWRITE AS. SELECT c.cust last name, SUM(amount sold) AS sum amount sold. 2 Use the CREATE MATE...

oracle-datawarehousing.blogspot.com

Data Warehousing Objects ~ ORACLE DATAWAREHOUSE

http://oracle-datawarehousing.blogspot.com/2011/02/data-warehousing-objects.html

All about Data Warehousing with Oracle. Error Logging and Handling. Fact tables and dimension tables are the two types of objects commonly used in. Dimensional data warehouse schemas. Fact tables are the large tables in your data warehouse schema that store business. Measurements. Fact tables typically contain facts and foreign keys to the dimension. Tables. Fact tables represent data, usually numeric and additive, that can be analyzed and examined. Examples include sales, cost, and profit. Dimension dat...

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Pig Tutorial

The Pig tutorial shows you how to run two Pig scripts in Local mode and Hadoop mode. Local Mode: To run the scripts in local mode, no Hadoop or HDFS installation is required. All files are installed and run from your local host and file system. Hadoop Mode: To run the scripts in hadoop (mapreduce) mode, you need access to a Hadoop cluster and HDFS installation available through Hadoop Virtual Machine provided with this tutorial. Java Installation (Note: already set-up on the Hadoop VM.). 1Go to the /home...

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