Apache Spark Advanced Topics E-learning
€159,00
Cursusinhoud/-informatie
Apache Spark Advanced Topics E-learning
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
- 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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Specificaties
Algemene eigenschappen
| 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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