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Apache Spark Apache Spark Advanced Topics E-learning
Apache Spark Advanced Topics E-learning

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Apache Spark Advanced Topics E-learning

Brand: Apache Spark
€159,00 Excl. tax
€192,39 Incl. tax
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Apache Spark Advanced Topics Elearning

Order this unique E-learning Apache Spark Advanced Topics course online, 1 year 24/7 access to rich interactive videos, voice, progress monitoring through reports tests.


Spark Core

  • start the course
  • recall what is included in the Spark Stack
  • define lazy evaluation as it relates to Spark
  • recall that RDD is an interface comprised of a set of partitions, list of dependencies, and functions to compute
  • pre-partition an RDD for performance
  • store RDDS in serialized form
  • perform numeric operations on RDDs
  • create custom accumulators
  • use broadcast functionality for optimization
  • pipe to external applications
  • adjust garbage collection settings
  • perform batch import on a Spark cluster
  • determine memory consumption
  • tune data structures to reduce memory consumption
  • use Spark's different shuffle operations to minimize memory usage of reduce tasks
  • set the levels of parallelism for each operation
  • create DataFrames
  • interoperate with RDDs
  • describe the generic load and save functions
  • read and write Parquet files
  • use JSON Dataset as a DataFrame
  • read and write data in Hive tables
  • read and write data using JDBC
  • run the Thrift JDBC/OCBC server
  • show the different ways to tune up Spark for better performance

Spark Streaming

  • start the course
  • describe what a DStream is
  • recall how TCP socket input streams are ingested
  • describe how file input streams are read
  • recall how Akka Actor input streams are received
  • describe how Kafka input streams are consumed
  • recall how Flume input streams are ingested
  • set up Kinesis input streams
  • configure Twitter input streams
  • implement custom input streams
  • describe receiver reliability
  • use the UpdateStateByKey operation
  • perform transform operations
  • perform Window operations
  • perform join operations
  • use output operations on Streams
  • use DataFrame and SQL operations on streaming data
  • use learning algorithms with MLlib
  • persist stream data in memory
  • enable and configure checkpointing
  • deploy applications
  • monitor applications
  • reduce batch processing times
  • set the right batch interval
  • tune memory usage
  • describe fault tolerance semantics
  • perform transformations on Dstreams

MLlib, GraphX, and R

  • start the course
  • describe data types
  • recall the basic statistics
  • describe linear SVMs
  • perform logistic regression
  • use naïve bayes
  • create decision trees
  • use collaborative filtering with ALS
  • perform clustering with K-means
  • perform clustering with LDA
  • perform analysis with frequent pattern mining
  • describe the property graph
  • describe the graph operators
  • perform analytics with neighborhood aggregation
  • perform messaging with Pregel API
  • build graphs
  • describe vertex and edge RDDs
  • optimize representation through partitioning
  • measure vertices with PageRank
  • install SparkR
  • run SparkR
  • use existing R packages
  • expose RDDs as distributed lists
  • convert existing RDDs into DataFrames
  • read and write parquet files
  • run SparkR on a cluster
  • use the algorithms and utilities in MLlib
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General properties
Availabilty: 11 hours
Language: English
Certificate of participation: Yes
Online access: 90 days
Progress monitoring: Yes
Award Winning E-learning: Yes
Suitable for mobile: Yes
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