This course will teach you to leverage Pig and Hive for big data to prepare & analyze large data sets on Hadoop to make more informed and timely business decisions. You will learn to increase productivity by avoiding low-level Java coding characteristic of MapReduce, and rapidly begin extracting business value for competitive advantage. In this Pig & Hive for Big Data training course, you will learn to gain access to previously inaccessible data, gather and feed data into Hadoop for storage, transform and filter data using Pig, and extract value using Hive and Spark SQL.
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Storing data in HDFS
Parallel processing with MapReduce
Automating data transfer
Describing characteristics of Apache Pig
Structuring unstructured data
Transforming data with Relational Operators
Filtering data with Pig
Leveraging business advantages of Hive
Organizing data in Hive Data Warehouse
Designing data layout for maximum performance
Performing joins on unstructured data
Pushing HiveQL to the limit
Deploying Hive in production
Streamlining storage management with HCatalog
Hadoop programming at the low level is done in Java. Pig and Hive provide ease of programming by allowing the programmer to write scripts in a simpler language, Pig Latin or HiveQL. Those scripts are compiled and optimized internally and equivalent Java code generated and executed without the programmer having to write the Java code.
Apache Pig is a platform for analyzing large data sets. Programs are written in a high-level, Pig Latin. They are converted by Pig's infrastructure into sequences of Java MapReduce programs which are then executed on Hadoop. Without writing Java one can use Pig to leverage Hadoop's ability to process data in parallel.
Apache Hive is data warehouse software that translates commands written in a SQL-like language, HiveQL, into Hadoop MapReduce jobs that are then executed on Hadoop. Without writing Java one can use Hive to leverage Hadoop's ability to process data in parallel.
Pig is typically used early in the data pipeline to clean and structure data. Hive is typically used later when there is structure and well-defined fields. Since Hive has the concepts of tables, rows and columns it integrates easily with BI tools.
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