Apache Spark Apache Spark Advanced Topics E-Learning
Apache Spark Advanced Topics E-Learning
€159,00
Apache Spark

Apache Spark Advanced Topics E-Learning

EUR 159,00 Excl. btw
  • Koop 2 voor €155,82 per stuk en bespaar 2%
  • Koop 3 voor €154,23 per stuk en bespaar 3%
  • Koop 5 voor €147,87 per stuk en bespaar 7%
  • Koop 10 voor €143,10 per stuk en bespaar 10%
  • Koop 25 voor €135,15 per stuk en bespaar 15%
  • Koop 50 voor €124,02 per stuk en bespaar 22%
  • Koop 100 voor €111,30 per stuk en bespaar 30%
  • Koop 200 voor €79,50 per stuk en bespaar 50%

Bestel deze unieke E-Learning cursus Apache Spark Advanced Topics online, 1 jaar 24/ 7 toegang tot rijke interactieve video’s, voortgangs door rapportage en testen.

  • E-Learning - Online toegang: 365 dagen
  • Taal: Engels (US)
  • Certificaat van deelname
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Bestel voor 16:00 uur en start vandaag.
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Productomschrijving

Apache Spark Advanced Topics E-Learning Training

Bestel deze unieke E-Learning cursus Apache Spark Advanced Topics online, 1 jaar 24/ 7 toegang tot rijke interactieve video’s, spraak, voortgangsbewaking door rapportages en testen.

Cursusinhoud 

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
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

Specificaties
Duur 11 uur
Taal Engels
Certificaat van deelname Ja
Online toegang 365 dagen
Voortgangsbewaking Ja
Award Winning E-learning Ja
Geschikt voor mobiel Ja
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