By Venkat Ankam
- This e-book is predicated at the most up-to-date 2.0 model of Apache Spark and 2.7 model of Hadoop built-in with most typically used tools.
- Learn all Spark stack parts together with most up-to-date issues equivalent to DataFrames, DataSets, GraphFrames, established Streaming, DataFrame established ML Pipelines and SparkR.
- Integrations with frameworks reminiscent of HDFS, YARN and instruments akin to Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector, GraphFrames, H2O and Hivemall.
Big info Analytics ebook goals at supplying the basics of Apache Spark and Hadoop. All Spark parts – Spark middle, Spark SQL, DataFrames, facts units, traditional Streaming, based Streaming, MLlib, Graphx and Hadoop center elements – HDFS, MapReduce and Yarn are explored in larger intensity with implementation examples on Spark + Hadoop clusters.
It is relocating clear of MapReduce to Spark. So, merits of Spark over MapReduce are defined at nice intensity to harvest advantages of in-memory speeds. DataFrames API, facts resources API and new info set API are defined for construction titanic info analytical purposes. Real-time info analytics utilizing Spark Streaming with Apache Kafka and HBase is roofed to assist development streaming functions. New based streaming inspiration is defined with an IOT (Internet of items) use case. desktop studying strategies are lined utilizing MLLib, ML Pipelines and SparkR and Graph Analytics are coated with GraphX and GraphFrames elements of Spark.
Readers also will get a chance to start with net established notebooks similar to Jupyter, Apache Zeppelin and knowledge move software Apache NiFi to research and visualize data.
What you'll learn
- Find out and enforce the instruments and strategies of huge facts analytics utilizing Spark on Hadoop clusters with large choice of instruments used with Spark and Hadoop
- Understand the entire Hadoop and Spark surroundings components
- Get to grasp all of the Spark parts: Spark middle, Spark SQL, DataFrames, DataSets, traditional and established Streaming, MLLib, ML Pipelines and Graphx
- See batch and real-time info analytics utilizing Spark center, Spark SQL, and traditional and dependent Streaming
- Get to grips with info technology and computing device studying utilizing MLLib, ML Pipelines, H2O, Hivemall, Graphx, SparkR and Hivemall.
About the Author
Venkat Ankam has over 18 years of IT adventure and over five years in huge info applied sciences, operating with buyers to layout and improve scalable sizeable facts functions. Having labored with a number of consumers globally, he has great adventure in massive facts analytics utilizing Hadoop and Spark.
He is a Cloudera qualified Hadoop Developer and Administrator and likewise a Databricks qualified Spark Developer. he's the founder and presenter of some Hadoop and Spark meetup teams globally and likes to proportion wisdom with the community.
Venkat has introduced enormous quantities of trainings, displays, and white papers within the sizeable information sphere. whereas this is often his first try out at writing a publication, many extra books are within the pipeline.
Table of Contents
- Big information Analytics at 10,000 foot view
- Getting begun with Apache Hadoop and Apache Spark
- Deep Dive into Apache Spark
- Big info Analytics with Spark SQL, DataFrames, and Datasets
- Real-Time Analytics with Spark Streaming and based Streaming
- Notebooks and Dataflows with Spark and Hadoop
- Machine studying with Spark and Hadoop
- Building advice platforms with Spark and Mahout
- Graph Analytics with GraphX
- Interactive Analytics with SparkR
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