Apache
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Happy to share the earning of MCSD certification 🙂 Verification URL: https://verify.skilljar.com/c/5if4ohmy68dr Certifications solidify the conceptual knowledge. Spark carries a lot of concepts as well. The Learning Spark Book as well as certain YouTube Videos are a must to build conceptual knowledge. YouTube videos by Sameer Farooque and Brian Clapper are sources. These Certification exams
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. There are few type of UDFs that we can write in Hive. Functions that act on each column value passed to it, e.g. Select Length(name) From Customer Specific functions written for a specific data type (simple UDFs) Generic functions written to working with more than one data type Functions that act on a group
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. HCatalog is an extension of Hive and in a nutshell, it exposes the schema information in Hive Metastore such that applications outside of Hive can use it. The objective of HCatalog is to hold the following type of information about the data in HDFS – Location of the data Metadata about the data (e.g.
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. There are a few type of UDFs that we can write in Hive. Functions that act on each column value passed to it, e.g. Select Length(name) From Customer Specific functions written for a specific data type Generic functions written to working with more than one data type (GenericUDF) Functions that act on a group
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. UDFs (User Defined Functions) are ways in pig to extend its functionality. There are two type of UDFs that we can write in pig – Evaluate (extends from EvalFunc base class) Load/Store functions (extends from LoadFunc base class) Here we will stepwise develop an Evaluate UDF. Lets start by conceptualizing a UDF (named VowelCount)
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. Simple: INT and FLOAT are 32 bit signed numeric datatypes backed by java.lang.Integer and java.lang.Float Simple: LONG and DOUBLE are 64 bit signed numeric Java datatypes Simple: CHARARRAY (Unicode backed by java.lang.String) Simple: BYTEARRAY (Bytes / Blob, backed by Pig’s DataByteArray class that wraps byte[]) Simple: BOOLEAN (“true” or “false” case sensitive) Simple: DATETIME