Data Analytics with Hadoop

Index

A

• accumulators, Programming with RDDs
• actions, Resilient Distributed Datasets
• add operator, Interactive Spark Using PySpark
• agents, Flume Data Flows-Flume Data Flows
• aggregation, Aggregation
• alternating least squares (ALS) algorithm, Collaborative Filtering
• analytics, with higher-level APIs, Analytics with Higher-Level APIs-Conclusion
• anonymous functions (closures), Programming with RDDs
• Apache Flume (see Flume)
• Apache HBase (see HBase)
• Apache Hive (see Hive)
• Apache Spark (see Spark)
• Apache Sqoop (see Sqoop)
• Apache Storm, Hadoop Streaming vs., Hadoop Streaming
• APIs, higher-level, Analytics with Higher-Level APIs-Conclusion
• architecture, distributed, Hadoop Architecture-YARN, Data Ingestion
• Avro, Ingesting Product Impression Data with Flume

B

• big data
  • as term, Preface
  • data science vs., Who This Book Is For
• Big Data
  • Hadoop as OS for, An Operating System for Big Data-Conclusion
• bigrams, Counting Bigrams-Other Frameworks
• blocks, HDFS, Blocks
• bloom filtering, Bloom filtering-Bloom filtering
• broadcast variables, Programming with RDDs
• build phase, Conclusion
• byte array, NoSQL and Column-Oriented Databases

C

• cat command, Basic File System Operations, Executing Streaming Jobs
• centroids, Clustering
• classification, Scalable Machine Learning with Spark, Classification-Logistic regression classification: An example
• closures, Programming with RDDs
• Cloudera, Quick Start
• cluster-based systems, MapReduce: Implemented on a Cluster-MapReduce examples, Data Product Lifecycle
• clustering, unsupervised, Scalable Machine Learning with ...