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01a: Convert XML file To Sequence File – writing & reading – Local File System

Sequence files are good for saving raw data into HDFS. Sequence files are compressible and splittable. It is also useful for combining a number of smaller files into a single say 64MB or larger sequence file as HDFS is more suited for larger files. … Read more ›...

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01b: Convert XML file To Sequence File – writing & reading – Hadoop File System (i.e HDFS)

This extends Convert XML file To Sequence File – writing & reading – Local File System. Step 1: Upload “report.xml” onto HDFS. E.g using the Cloudera HUE on to path “/user/cloudera/report-data”. You need to create the “report-data” folder. The uploaded file on Hue: Step 2: Change the code to read...

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02: Convert XML file To Sequence File with Apache Spark – writing & reading

This extends the Convert XML file To Sequence File With Hadoop libaries, by using Apache Spark. Step 1: The pom.xml file should include the Apache Spark libraries as shown below. Step 2: The XML file report.xml. Step 3: The Java class “ … Read more ›...

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03: Convert XML file To an Avro File – writing & reading

This extends the Convert XML file To Sequence File With Hadoop libaries. Avro files are schema driven & support schema evolution, which means you can add new columns & modify existing columns. Step 1: The pom.xml file should include the Apache Spark libraries as shown below. … Read more ›...

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04: Convert XML file To an Avro File with Apache Spark – writing & reading

This extends Convert XML file To an Avro File – writing & reading. Step 1: The pom.xml file should include the Apache Spark & Avro libraries as shown below. Step 2: The report.xml file under “src/main/resources/data”. … Read more ›...

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05: Convert XML file To an Avro File with avro-maven-plugin & Apache Spark

This extends 04: Convert XML file To an Avro File with Apache Spark – writing & reading. Instead of using the GenericRecord, let’s generate an avro schema object from the avro schema. Step 1: The pom.xml file should include the Apache Spark & … Read more ›...

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