SMACK Stack for Data Science Treningskurs

Last updated

Kurskode

smackstack

Varighet

14 timer (vanligvis 2 dag inkludert pauser)

Krav

  • An understanding of data processing systems

Audience

  • Data Scientists

Oversikt

SMACK er en samling av dataplatform programvare, nemlig Apache Spark, Apache Mesos, Apache Akka, Apache Cassandra, og Apache Kafka. Ved hjelp av SMACK stack kan brukere lage og skala databehandlingsplattformer.

Denne instruktørledede, live-trening (online eller on-site) er rettet mot data forskere som ønsker å bruke SMACK stack til å bygge databehandlingsplattformer for store data løsninger.

Ved slutten av denne treningen vil deltakerne være i stand til å:

    Implementere en data pipeline-arkitektur for behandling av store data. Utvikle en klusterinfrastruktur med Apache Mesos og Docker. Analyse data med Spark og Scala. Å håndtere ustrukturerte data med Apache Cassandra.

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

SMACK Stack Overview

  • What is Apache Spark? Apache Spark features
  • What is Apache Mesos? Apache Mesos features
  • What is Apache Akka? Apache Akka features
  • What is Apache Cassandra? Apache Cassandra features
  • What is Apache Kafka? Apache Kafka features

Scala Language

  • Scala syntax and structure
  • Scala control flow

Preparing the Development Environment

  • Installing and configuring the SMACK stack
  • Installing and configuring Docker

Apache Akka

  • Using actors

Apache Cassandra

  • Creating a database for read operations
  • Working with backups and recovery

Connectors

  • Creating a stream
  • Building an Akka application
  • Storing data with Cassandra
  • Reviewing connectors

Apache Kafka

  • Working with clusters
  • Creating, publishing, and consuming messages

Apache Mesos

  • Allocating resources
  • Running clusters
  • Working with Apache Aurora and Docker
  • Running services and jobs
  • Deploying Spark, Cassandra, and Kafka on Mesos

Apache Spark

  • Managing data flows
  • Working with RDDs and dataframes
  • Performing data analysis

Troubleshooting

  • Handling failure of services and errors

Summary and Conclusion

Testimonials

★★★★★
★★★★★

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