| 000 | a | ||
|---|---|---|---|
| 999 |
_c31348 _d31348 |
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| 008 | 230220b xxu||||| |||| 00| 0 eng d | ||
| 020 | _a9789352137060 | ||
| 082 |
_a006.31 _bCHA |
||
| 100 | _aChambers, Bill | ||
| 245 | _aSpark : the definintive guide : big data processing made simple | ||
| 260 |
_bShroff Publishers, _c2018 _aMumbai : |
||
| 300 |
_axxvi, 574 p.; _bill. _c24 cm |
||
| 365 |
_b1800.00 _cINR _d1.00 |
||
| 504 | _aIncludes index. | ||
| 520 | _aLearn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Spark’s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation. | ||
| 650 | _aBig data | ||
| 650 | _aComputer Science | ||
| 650 | _aData Processing | ||
| 650 | _aHardware General | ||
| 650 | _aInformation Technology | ||
| 650 | _aData mining | ||
| 650 | _aInformation retrieval | ||
| 650 | _aSpark | ||
| 650 | _aAdvanced analytics | ||
| 650 | _a Aggregations | ||
| 650 | _a Apache Hive | ||
| 650 | _a Cluster manager | ||
| 650 | _a Configuration options | ||
| 650 | _aDataframe | ||
| 650 | _a Datasets | ||
| 650 | _a Decision trees | ||
| 650 | _aGraphframes | ||
| 650 | _aHadoop distributed file system | ||
| 650 | _a Hyperparameters | ||
| 650 | _aJSON data | ||
| 650 | _aLinear regression | ||
| 650 | _aLogistic regression | ||
| 650 | _aMachine learning | ||
| 650 | _a MLib | ||
| 650 | _aNullIf fiunction | ||
| 650 | _a Python | ||
| 650 | _aRandom forests | ||
| 650 | _a RDD method | ||
| 650 | _aStream processing | ||
| 650 | _a Timestamp type class | ||
| 650 | _aUnsupervised learning | ||
| 650 | _aWatermarks | ||
| 700 | _aZaharia, Matei | ||
| 942 |
_2ddc _cBK |
||