Spark Streaming with Python and Kafka Treningskurs

Last updated

Kurskode

sparkstreaming

Varighet

7 timer (vanligvis 1 dag inkuldert pauser)

Krav

  • Experience with Python and Apache Kafka
  • Familiarity with stream-processing platforms

Audience

  • Data engineers
  • Data scientists
  • Programmers

Oversikt

Apache Spark Streaming er et skalbart, åpen kildesystem som gjør det mulig for brukerne å behandle data i sanntid fra støttede kilder. Spark Streaming muliggjør feiltolerant behandling av datastrømmer.

Denne instruktørledede, live-trening (online eller on-site) er rettet mot data ingeniører, data forskere og programmerere som ønsker å bruke Spark Streaming funksjoner i behandling og analyse av realtidsdata.

Ved slutten av denne treningen vil deltakerne kunne bruke Spark Streaming til å behandle live datastrømmer for bruk i databaser, filsystemer og live dashboards.

Format av kurset

    Interaktiv forelesning og diskusjon. Mange øvelser og praksis. Hands-on implementering i et live-lab miljø.

Kurs tilpassingsalternativer

    For å be om en tilpasset opplæring for dette kurset, vennligst kontakt oss for å arrangere.

Machine Translated

Kursplan

Introduction

Overview of Spark Streaming Features and Architecture

  • Supported data sources
  • Core APIs

Preparing the Environment

  • Dependencies
  • Spark and streaming context
  • Connecting to Kafka

Processing Messages

  • Parsing inbound messages as JSON
  • ETL processes
  • Starting the streaming context

Performing a Windowed Stream Processing

  • Slide interval
  • Checkpoint delivery configuration
  • Launching the environment

Prototyping the Processing Code

  • Connecting to a Kafka topic
  • Retrieving JSON from data source using Paw
  • Variations and additional processing

Streaming the Code

  • Job control variables
  • Defining values to match
  • Functions and conditions

Acquiring Stream Output

  • Counters
  • Kafka output (matched and non-matched)

Troubleshooting

Summary and Conclusion

Testimonials

★★★★★
★★★★★

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