Cisco-opplæring

Insoft Services er en av få opplæringsleverandører i EMEAR som tilbyr hele spekteret av Cisco-sertifisering og spesialisert teknologiopplæring.

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Cisco Sertifisering

Opplev en blandet læringstilnærming som kombinerer det beste av instruktørledet opplæring og e-læring i eget tempo for å hjelpe deg med å forberede deg til sertifiseringseksamen.

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Cisco Learning Credits

Cisco Learning Credits (CLC) er forhåndsbetalte opplæringskuponger innløst direkte med Cisco som gjør planleggingen for suksessen din enklere når du kjøper Cisco-produkter og -tjenester.

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Etterutdanning

Cisco Continuing Education Program tilbyr alle aktive sertifiseringsinnehavere fleksible alternativer for å resertifisere ved å fullføre en rekke kvalifiserte opplæringselementer.

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Cisco Digital Learning

Sertifiserte ansatte er verdsatte eiendeler. Utforsk Ciscos offisielle digitale læringsbibliotek for å utdanne deg gjennom innspilte økter.

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Cisco Business Enablement

Cisco Business Enablement Partner Program fokuserer på å skjerpe forretningsferdighetene til Cisco Channel Partners og kunder.

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Cisco opplæringskatalog

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Fortinet Sertifisering

Fortinet Network Security Expert (NSE)-programmet er et opplærings- og sertifiseringsprogram på åtte nivåer for å lære ingeniører om nettverkssikkerheten for Fortinet FW-ferdigheter og -erfaring.

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Fortinet-opplæring

Insoft er anerkjent som Fortinet Autorisert Opplæringssenter på utvalgte steder i EMEA.

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Fortinet opplæringskatalog

Utforsk et bredt utvalg av Fortinet Schedule på tvers av forskjellige land så vel som online kurs.

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ATC-status

Sjekk atc-statusen vår på tvers av utvalgte land i Europa.

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Pakker for Fortinet-tjenester

Insoft Services har utviklet en spesifikk løsning for å effektivisere og forenkle prosessen med å installere eller migrere til Fortinet-produkter.

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Microsoft-opplæring

Insoft Services gir Microsoft opplæring i EMEAR. Vi tilbyr Microsofts tekniske opplærings- og sertifiseringskurs som ledes av instruktører i verdensklasse.

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Extreme-opplæring

Lær eksepsjonell kunnskap og ferdigheter i ekstreme nettverk.

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Teknisk sertifisering

Vi tilbyr omfattende læreplan over tekniske kompetanseferdigheter om sertifiseringsprestasjonen.

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Extreme opplæringskatalog

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ATP-akkreditering

Som autorisert opplæringspartner (ATP) sørger Insoft Services for at du får de høyeste utdanningsstandardene som er tilgjengelige.

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Løsninger og tjenester

Vi tilbyr innovativ og avansert støtte for design, implementering og optimalisering av IT-løsninger. Vår kundebase inkluderer noen av de største Telcos globalt.

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Globalt anerkjent team av sertifiserte eksperter hjelper deg med å gjøre en jevnere overgang med våre forhåndsdefinerte konsulent-, installasjons- og migrasjonspakker for et bredt spekter av Fortinet-produkter.

Om oss

Insoft Tilbyr autoriserte opplærings- og konsulenttjenester for utvalgte IP-leverandører. Finn ut hvordan vi revolusjonerer bransjen.

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  • +47 23 96 21 03
  • Building Data Analytics Solutions Using Amazon Redshift

    Duration
    1 Dag
    Delivery
    (Online Og På stedet)
    Price
    Pris på forespørsel

    In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift.

     

    • Course level: Intermediate

    In this course, you will learn to:

    • Compare the features and benefits of data warehouses, data lakes, and modern data architectures
    • Design and implement a data warehouse analytics solution
    • Identify and apply appropriate techniques, including compression, to optimize data storage
    • Select and deploy appropriate options to ingest, transform, and store data
    • Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case
    • Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
    • Secure data at rest and in transit
    • Monitor analytics workloads to identify and remediate problems
    • Apply cost management best practices

    Module A: Overview of Data Analytics and the Data Pipeline

    • Data analytics use cases
    • Using the data pipeline for analytics

    Module 1: Using Amazon Redshift in the Data Analytics Pipeline

    • Why Amazon Redshift for data warehousing?
    • Overview of Amazon Redshift

    Module 2: Introduction to Amazon Redshift

    • Amazon Redshift architecture
    • Interactive Demo 1: Touring the Amazon Redshift console
    • Amazon Redshift features
    • Practice Lab 1: Load and query data in an Amazon Redshift cluster

    Module 3: Ingestion and Storage

    • Ingestion
    • Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API
    • Data distribution and storage
    • Interactive Demo 3: Analyzing semi-structured data using the SUPER data type
    • Querying data in Amazon Redshift
    • Practice Lab 2: Data analytics using Amazon Redshift Spectrum

    Module 4: Processing and Optimizing Data

    • Data transformation
    • Advanced querying
    • Practice Lab 3: Data transformation and querying in Amazon Redshift
    • Resource management
    • Interactive Demo 4: Applying mixed workload management on Amazon Redshift
    • Automation and optimization
    • Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus

    Module 5: Security and Monitoring of Amazon Redshift Clusters

    • Securing the Amazon Redshift cluster
    • Monitoring and troubleshooting Amazon Redshift clusters

    Module 6: Designing Data Warehouse Analytics Solutions

    • Data warehouse use case review
    • Activity: Designing a data warehouse analytics workflow

    Module B: Developing Modern Data Architectures on AWS

    • Modern data architectures

    This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.

    Students with a minimum one-year experience managing data warehouses will benefit from this course.
     

    We recommend that attendees of this course have:

    • Completed either AWS Technical Essentials or Architecting on AWS
    • Completed Building Data Lakes on AWS

    In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift.

     

    • Course level: Intermediate

    In this course, you will learn to:

    • Compare the features and benefits of data warehouses, data lakes, and modern data architectures
    • Design and implement a data warehouse analytics solution
    • Identify and apply appropriate techniques, including compression, to optimize data storage
    • Select and deploy appropriate options to ingest, transform, and store data
    • Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case
    • Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
    • Secure data at rest and in transit
    • Monitor analytics workloads to identify and remediate problems
    • Apply cost management best practices

    Module A: Overview of Data Analytics and the Data Pipeline

    • Data analytics use cases
    • Using the data pipeline for analytics

    Module 1: Using Amazon Redshift in the Data Analytics Pipeline

    • Why Amazon Redshift for data warehousing?
    • Overview of Amazon Redshift

    Module 2: Introduction to Amazon Redshift

    • Amazon Redshift architecture
    • Interactive Demo 1: Touring the Amazon Redshift console
    • Amazon Redshift features
    • Practice Lab 1: Load and query data in an Amazon Redshift cluster

    Module 3: Ingestion and Storage

    • Ingestion
    • Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API
    • Data distribution and storage
    • Interactive Demo 3: Analyzing semi-structured data using the SUPER data type
    • Querying data in Amazon Redshift
    • Practice Lab 2: Data analytics using Amazon Redshift Spectrum

    Module 4: Processing and Optimizing Data

    • Data transformation
    • Advanced querying
    • Practice Lab 3: Data transformation and querying in Amazon Redshift
    • Resource management
    • Interactive Demo 4: Applying mixed workload management on Amazon Redshift
    • Automation and optimization
    • Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus

    Module 5: Security and Monitoring of Amazon Redshift Clusters

    • Securing the Amazon Redshift cluster
    • Monitoring and troubleshooting Amazon Redshift clusters

    Module 6: Designing Data Warehouse Analytics Solutions

    • Data warehouse use case review
    • Activity: Designing a data warehouse analytics workflow

    Module B: Developing Modern Data Architectures on AWS

    • Modern data architectures

    This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.

    Students with a minimum one-year experience managing data warehouses will benefit from this course.
     

    We recommend that attendees of this course have:

    • Completed either AWS Technical Essentials or Architecting on AWS
    • Completed Building Data Lakes on AWS
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