Beginning Apache Pig: Big Data Processing Made Easy by Balaswamy Vaddeman

By Balaswamy Vaddeman

Learn to take advantage of Apache Pig to improve light-weight large information purposes simply and speedy. This publication indicates you several optimization suggestions and covers each context the place Pig is utilized in huge facts analytics. Beginning Apache Pig indicates you ways Pig is simple to benefit and calls for really little time to increase great facts applications.The e-book is split into 4 elements: the entire good points of Apache Pig; integration with different instruments; tips to remedy advanced company difficulties; and optimization of tools.You'll detect issues similar to MapReduce and why it can't meet each enterprise desire; the beneficial properties of Pig Latin resembling information forms for every load, shop, joins, teams, and ordering; how Pig workflows might be created; filing Pig jobs utilizing Hue; and dealing with Oozie. you are going to additionally see the way to expand the framework via writing UDFs and customized load, shop, and clear out capabilities. ultimately you are going to disguise assorted optimization options resembling amassing records a couple of Pig script, becoming a member of innovations, parallelism, and the function of knowledge codecs in stable performance.

What you'll Learn• Use all of the gains of Apache Pig• combine Apache Pig with different instruments• expand Apache Pig• Optimize Pig Latin code• resolve diversified use instances for Pig LatinWho This e-book Is ForAll degrees of IT pros: architects, tremendous information fanatics, engineers, builders, and large information administrators

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Beginning Apache Pig: Big Data Processing Made Easy

Discover ways to use Apache Pig to improve light-weight mammoth info purposes simply and fast. This ebook exhibits you several optimization strategies and covers each context the place Pig is utilized in giant info analytics. starting Apache Pig indicates you ways Pig is straightforward to profit and calls for really little time to enhance giant information functions.

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Jar in the classpath so that the Pig API in the Java program is resolved. The following command compiles Java file. jar StoreEmp. java. 3. Write the command to run the Java program. 0-3485/pig/lib/*:. csv dumpempout If Pig cannot find its dependent JARs, the Java program might fail and throw a “class not found” exception. To avoid such exceptions, include all the required JARs in the class path using the -cp option. The following are other special characters that can be used after the –cp option.

Tuples can have any number of fields. If the field value is not found, a null is returned. } Here’s an example: {(Bala, 1972, Software Engineer)} Data for this example can be loaded using the following statement: emp = load '/data/employees' as (B: bag {T: tuple (ename:chararray, empid:int, desg:charray)} ); or the following: emp = load '/data/employees' as (B: {T: desg:charray)}); (ename:chararray, empid:int, There are two types of bags: outer bag and inner bag. Here is an example of data with an inner bag: (1,{( Bala, 1972, Software Engineer)}) You can convert fields with simple data types into bag data types using the TOBAG function.

If the field value is not found, a null is returned. } Here’s an example: {(Bala, 1972, Software Engineer)} Data for this example can be loaded using the following statement: emp = load '/data/employees' as (B: bag {T: tuple (ename:chararray, empid:int, desg:charray)} ); or the following: emp = load '/data/employees' as (B: {T: desg:charray)}); (ename:chararray, empid:int, There are two types of bags: outer bag and inner bag. Here is an example of data with an inner bag: (1,{( Bala, 1972, Software Engineer)}) You can convert fields with simple data types into bag data types using the TOBAG function.

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