Big Data
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In an elastic cluster, there are many entities that makes up a functioning cluster. Nodes, Primary Shards and Replicas are the ones frequently getting referred in documentation and articles.
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Log file analysis is popular usecase in Big Data world. Log files contains evidence of historical events that an application witnessed under their execution environment. Monitoring applications intend to find out traces of actual events that happened during program execution. Several analysis usecases are possible from simply counting occurrence of some event to specific processing.
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. Analysis and Analyzers Elastic Search breaks (tokenizes) data in the document and build index of words (tokens). For each token, it points to the documents that matches the token. The words (tokens) are transformed a particular manner before being stored in the index. This process of breaking the document in a set of words
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. EMP and DEPT tables are pretty popular between Oracle users. These tables were very handy in quickly trying new queries. Also, there exists a DUAL table in Oracle that was pretty useful in evaluate expressions, like- “Select (SYSDATE + 1/24) as OneHourFromNow FROM DUAL“. These tables doesn’t exists in Hive, but we can create
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. Happy to share the earning of CCDH certification 🙂 Verification URL: http://certification.cloudera.com/verify (with License # 100-013-285) . Loads of conceptual as well as programming questions that include multiple choice questions as well. Reading Hadoop: The Definitive Guide and Programming Hive a couple of times and practicing Map-Reduce programming model rigorously, was instrumental in clearing the exams. Setting up
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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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. emp = LOAD ‘/path/to/data/file/on/hdfc/Employees.txt’ [ USING PigStorage(‘ ‘) ] AS ( emp_id: INT, name: CHARARRAY, joining_date: DATETIME, department: INT, salary: FLOAT, mgr_id: INT, residence: BAG { b:(addr1: CHARARRAY, addr2: CHARARRAY, city: CHARARRAY) }) ; The Alias for data in file “Employees.txt” is emp and using emp,
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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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. — emp = LOAD ‘Employees.txt’ … Data in text file resembles the “EMP” table in Oracle — dept = LOAD ‘Dept.txt’ …….. Data in text file resembles the “DEPT” table in Oracle — Filter data in emp to only those whose job is Clerk. Filtered_Emp = FILTER emp BY (job == ‘CLERK’); — Supports
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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
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. Pig Statements — Load command loads the data — Every placeholder like “A_Rel” and “Filter_A” are called Alias, and they are useful — in holding the relation returned by pig statements. Aliases are relations (not variables). A_Rel = LOAD ‘/hdfs/path/to/file’ [AS (col_1[: type], col_2[: type], col_3[: type], …)] ; — Record set returned by
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Pig is a data flow language developed at Yahoo and is a high level language. Pig programs are translated into a lower level instructions supported by underlying execution engine. Pig is designed for working on complex operations with speed.
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MapReduce default settings
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. Sqoop is a utility that can be used to transfer data between SQL based relational data stores tO/from hadoOP. The main operation this utility carry out is performing a data Import to Hadoop from supported relational data sources and Exporting data back to them. Sqoop uses connectors as extensions to connect to data stores.
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. Apache Hive is an abstraction on top of HDFS data, that allow querying the data using the familiar SQL like language, called HiveQL (Hive Query Language). Hive was developed at Facebook to allow data analysts to query data using an SQL like language. Hive has limited commands and is similar to basic SQL (advance SQL options
Vipul Pathak 