Spark Fundamentals II
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Spark is a fast and general engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a wide range of data sources including HDFS, Cassandra, HBase, S3.
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Intro to Spark and Spark SQL
Apache Spark is a fast and general cluster computing system that improves efficiency through in-memory computing and usability through rich APIs. Spark SQL provides a way to work with structured data and transform RDDs using SQL. It can read data from sources like Parquet and JSON files, Hive, and write query results to Parquet for efficient querying. Spark SQL also allows machine learning pipelines to be built by connecting SQL queries to MLlib algorithms.
Using HBase for Real-time Access to your Big Data
HBase is an open-source, non-relational, distributed database that provides real-time read/write access to large datasets stored on Hadoop clusters. It is modeled after Google's Bigtable and is written in Java. Dinesh Kumar V discusses how HBase can be used to provide real-time access to big data stored on Hadoop for applications that need low-latency reads and writes to large datasets.
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BigSheets is a new tool for spreadsheet-like analytics. It allows users to analyze large datasets in a familiar spreadsheet interface without the limitations of traditional spreadsheets. BigSheets handles massive datasets efficiently by using a distributed backend that scales horizontally across clusters of machines.
SQL Access for Hadoop (Big SQL v4)
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Moving Data into Hadoop
Moving data into Hadoop involves transferring large amounts of data from various sources into the Hadoop distributed file system (HDFS). This process, known as data ingestion, typically uses tools like Sqoop and Flume to import data from relational databases, social media streams, and other systems into HDFS files where it can then be processed by MapReduce, Spark, and other Hadoop applications. Data ingestion is an important first step for any big data project utilizing Hadoop's distributed processing capabilities.
Introduction to Pig
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Introduction to Mapreduce Programming
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Introduction to Jaql
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Intro to Spark and Spark SQL
Apache Spark is a fast and general cluster computing system that improves efficiency through in-memory computing and usability through rich APIs. Spark SQL provides a way to work with structured data and transform RDDs using SQL. It can read data from sources like Parquet and JSON files, Hive, and write query results to Parquet for efficient querying. Spark SQL also allows machine learning pipelines to be built by connecting SQL queries to MLlib algorithms.
Using HBase for Real-time Access to your Big Data
HBase is an open-source, non-relational, distributed database that provides real-time read/write access to large datasets stored on Hadoop clusters. It is modeled after Google's Bigtable and is written in Java. Dinesh Kumar V discusses how HBase can be used to provide real-time access to big data stored on Hadoop for applications that need low-latency reads and writes to large datasets.
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BigSheets is a new tool for spreadsheet-like analytics. It allows users to analyze large datasets in a familiar spreadsheet interface without the limitations of traditional spreadsheets. BigSheets handles massive datasets efficiently by using a distributed backend that scales horizontally across clusters of machines.
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Moving Data into Hadoop
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Introduction to Pig
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Hadoop Fundamentals 1
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Spark is an open-source cluster computing framework that allows data processing across clusters of computers using in-memory techniques. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. Spark is also used for streaming data, machine learning, and graph processing.
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Spark is an open-source cluster computing framework that allows data processing across clusters of computers using in-memory techniques. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. Spark is also used for streaming data, machine learning, and graph processing.
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Spark Fundamentals II
- 1. Dinesh Kumar V Spark Fundamentals II September 26, 2015