Learning Apache Spark 2. (2017)

• Record Type: Book
• Other Names: Abbasi, Muhammad Asif
• Contents: Cover; Copyright; Credits; About the Author; About the Reviewers; www.packtpub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Architecture and Installation; Apache Spark architecture overview; Spark-core; Spark SQL; Spark streaming; MLlib; GraphX; Spark deployment; Installing Apache Spark; Writing your first Spark program; Scala shell examples; Python shell examples; Spark architecture; High level overview; Driver program; Cluster Manager; Worker; Executors; Tasks; SparkContext; Spark Session; Apache Spark cluster manager types Building standalone applications with Apache SparkSubmitting applications; Deployment strategies; Running Spark examples; Building your own programs; Brain teasers; References; Summary; Chapter 2: Transformations and Actions with Spark RDDs; What is an RDD?; Constructing RDDs; Parallelizing existing collections; Referencing external data source; Operations on RDD; Transformations; Actions; Passing functions to Spark (Scala); Anonymous functions; Static singleton functions; Passing functions to Spark (Java); Passing functions to Spark (Python); Transformations; Map(func); Filter(func) FlatMap(func)Sample (withReplacement, fraction, seed); Set operations in Spark; Distinct(); Intersection(); Union(); Subtract(); Cartesian(); Actions; Reduce(func); Collect(); Count(); Take(n); First(); SaveAsXXFile(); foreach(func); PairRDDs; Creating PairRDDs; PairRDD transformations; reduceByKey(func); GroupByKey(func); reduceByKey vs. groupByKey -- Performance Implications; CombineByKey(func); Transformations on two PairRDDs; Actions available on PairRDDs; Shared variables; Broadcast variables; Accumulators; References; Summary; Chapter 3: ETL with Spark; What is ETL?; Exaction; Loading TransformationHow is Spark being used?; Commonly Supported File Formats; Text Files; CSV and TSV Files; Writing CSV files; Tab Separated Files; JSON files; Sequence files; Object files; Commonly supported file systems; Working with HDFS; Working with Amazon S3; Structured Data sources and Databases; Working with NoSQL Databases; Working with Cassandra; Obtaining a Cassandra table as an RDD; Saving data to Cassandra; Working with HBase; Bulk Delete example; Map Partition Example; Working with MongoDB; Connection to MongoDB; Writing to MongoDB; Loading data from MongoDB Working with Apache SolrImporting the JAR File via Spark-shell; Connecting to Solr via DataFrame API; Connecting to Solr via RDD; References; Summary; Chapter 4: Spark SQL; What is Spark SQL?; What is DataFrame API?; What is DataSet API?; What's new in Spark 2.0?; Under the hood -- catalyst optimizer; Solution 1; Solution 2; The Sparksession; Creating a SparkSession; Creating a DataFrame; Manipulating a DataFrame; Scala DataFrame manipulation -- examples; Python DataFrame manipulation -- examples; R DataFrame manipulation -- examples; Java DataFrame manipulation -- examples
• Publisher Details: Place of publication not identified : Packt Publishing
• Publication Date: 2017
• Extent: 1 online resource ()
• Subjects: 006.3; COMPUTERS -- Data Processing; COMPUTERS -- Databases -- Data Mining; COMPUTERS -- Desktop Applications -- Databases; Electronic books; Electronic books
• Languages: English
• ISBNs: 1785889583; 9781785889585
• Related ISBNs: 1785885138
• Notes: Note: Description based on print version record.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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