| 000 | a | ||
|---|---|---|---|
| 999 |
_c31327 _d31327 |
||
| 008 | 221014b xxu||||| |||| 00| 0 eng d | ||
| 020 | _a9780134846019 | ||
| 082 |
_a006.312 _bAVE |
||
| 100 | _aAven, Jeffrey | ||
| 245 | _aData analytics with spark using python | ||
| 260 |
_bAddison-Wesley, _c2018 _aBoston : |
||
| 300 |
_ax, 306 p. ; _bill. _c24 cm |
||
| 365 |
_b475.00 _cINR _d01 |
||
| 504 | _aIncludes bibliographical references and index. | ||
| 520 | _aSpark for Data Professionals introduces and solidifies the concepts behind Spark 2.x, teaching working developers, architects, and data professionals exactly how to build practical Spark solutions. Jeffrey Aven covers all aspects of Spark development, including basic programming to SparkSQL, SparkR, Spark Streaming, Messaging, NoSQL and Hadoop integration. Each chapter presents practical exercises deploying Spark to your local or cloud environment, plus programming exercises for building real applications. Unlike other Spark guides, Spark for Data Professionals explains crucial concepts step-by-step, assuming no extensive background as an open source developer. It provides a complete foundation for quickly progressing to more advanced data science and machine learning topics. This guide will help you: Understand Spark basics that will make you a better programmer and cluster “citizen” Master Spark programming techniques that maximize your productivity Choose the right approach for each problem Make the most of built-in platform constructs, including broadcast variables, accumulators, effective partitioning, caching, and checkpointing Leverage powerful tools for managing streaming, structured, semi-structured, and unstructured data. | ||
| 650 | _aPython | ||
| 650 | _aAnonymous function | ||
| 650 | _a Apache | ||
| 650 | _a Big data | ||
| 650 | _aBroadcast method | ||
| 650 | _aCheckpointing | ||
| 650 | _a Create Direct Stream method | ||
| 650 | _aData frames | ||
| 650 | _a Dsteams | ||
| 650 | _aEnvironment variables | ||
| 650 | _aFlatMap | ||
| 650 | _aHadoop | ||
| 650 | _aHigher-order function | ||
| 650 | _a JSON | ||
| 650 | _a JDBC | ||
| 650 | _a Lamda syntax | ||
| 650 | _a MapReduce | ||
| 650 | _a NOSQL systems | ||
| 650 | _aPySpark | ||
| 650 | _a Spark cluster architecture | ||
| 650 | _a Tuple function | ||
| 650 | _a Unpersist method | ||
| 650 | _aYARN | ||
| 942 |
_2ddc _cBK |
||