4.3
estrellas
184 calificaciones

Karthik Muthuraman +1 instructores más

23.143 ya inscrito

Ofrecido Por

Habilidades en redes de IBM

Acerca de este Curso

194.824 vistas recientes

Bernard Marr defines Big Data as the digital trace that we are generating in this digital era. In this course, you will learn about the characteristics of Big Data and its application in Big Data Analytics. You will gain an understanding about the features, benefits, limitations, and applications of some of the Big Data processing tools. You’ll explore how Hadoop and Hive help leverage the benefits of Big Data while overcoming some of the challenges it poses. 
Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. Hive, a data warehouse software, provides an SQL-like interface to efficiently query and manipulate large data sets residing in various databases and file systems that integrate with Hadoop.

Apache Spark is an open-source processing engine that provides users new ways to store and make use of big data. It is an open-source processing engine built around speed, ease of use, and analytics. In this course, you will discover how to leverage Spark to deliver reliable insights. The course provides an overview of the platform, going into the different components that make up Apache Spark. 

In this course, you will also learn about Resilient Distributed Datasets, or RDDs, that enable parallel processing across the nodes of a Spark cluster.

Fechas límite flexibles

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Certificado para compartir

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100 % en línea

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Nivel principiante

7 weeks of study, 1-2 hours / week

Aprox. 12 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Qué aprenderás

Explainthe impact of Big Data including use cases, tools, and processing methods.
ExplainApache Hadoop architecture, ecosystem, and practices, and userelatedapplications including HDFS, HBase, Spark, and MapReduce.
Apply Spark programming basics, including parallel programming basics forDataFrames, data sets, and Spark SQL.
UseSpark’s RDDsanddata sets, optimizingSparkSQLusing Catalyst and Tungsten, anduseSpark’s development and runtime environment options.

Habilidades que obtendrás

Apache Hadoop
SparkSQL
SparkML
Big Data
Apache Spark

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Nivel principiante

7 weeks of study, 1-2 hours / week

Aprox. 12 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Instructores

Calificación del instructor

4,42/5 (42 calificaciones)

Karthik Muthuraman

Software Engineer (Machine Learning)

IBM Center for Open-Source Data and AI Technologies

24.928 alumnos

2 cursos

Aije Egwaikhide

Senior Data Scientist

IBM

426.729 alumnos

6 cursos

Ofrecido por

Habilidades en redes de IBM

IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.

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Programa - Qué aprenderás en este curso

Semana1

Semana 1

1 hora para completar

What is Big Data?

1 hora para completar

7 videos (Total 42 minutos), 2 lecturas, 2 cuestionarios

Semana2

Semana 2

2 horas para completar

Introduction to the Hadoop Ecosystem

2 horas para completar

6 videos (Total 37 minutos), 1 lectura, 4 cuestionarios

Semana3

Semana 3

1 hora para completar

Apache Spark

Build your skills when you turn your attention to the popular Apache Spark platform. Explore attribute and benefits of Apache Spark and distributed computing. You'll gain key insights about functional programming and Lambda functions. Explore Resilient Distributed Datasets (RDDs), Parallel Programming, resilience in Apache Spark and relate RDDs and Parallel Programming with Apache Spark. Dive into additional Apache Spark components and learn how Apache Spark scales with Big Data. Working with Big Data signals the need for working with queries, including structured queries using SQL. Learn about the functions, parts and benefits of Spark SQL and DataFrame queries, and discover how DataFrames work with SparkSQL.

1 hora para completar

5 videos (Total 24 minutos), 1 lectura, 3 cuestionarios

Semana4

Semana 4

2 horas para completar

DataFrames and SparkSQL

Learn about Resilient Distributed Datasets (RDDs), their uses in Apache Spark, and RDD transformations and actions. You'll compare the use of datasets with Spark's latest data abstraction, DataFrames. You'll learn to identify and apply basic DataFrame operations. Explore Apache Spark SQL optimization. Learn how Spark SQL and memory optimization benefit from using Catalyst and Tungsten. Learn how to create a table view and apply data aggregation techniques. Fortify your skills guided via the hands-on lab.

2 horas para completar

5 videos (Total 26 minutos), 1 lectura, 4 cuestionarios

Semana5

Semana 5

4 horas para completar

Development and Runtime Environment Options

Explore how Spark processes the requests that your application submits. Learn how you can track work using the Spark Application UI. Because Spark application work happens on the cluster, you need be able to identify Apache Cluster Managers, their components, benefits, and know how to connect with each cluster manager and how and when you might want to set up a local, standalone Spark instance. Next, learn about Apache Spark application submission, including use of Spark’s unified interface, ‘spark-submit’ and learn about options and dependencies. Developers now have the option of AIOps. Discover how to use Spark within AIOps and with Apache Spark application submission, including use of Spark’s unified interface, ‘spark-submit’, describe and apply options for submitting applications, identify external application dependency management techniques and list Spark Shell benefits. View and see recommended practices for Spark's static and dynamic configuration options. Round out your development knowledge with insights about Spark on Kubernetes. This module features hands-on Spark labs using IBM Cloud and Kubernetes.

4 horas para completar

6 videos (Total 33 minutos), 2 lecturas, 7 cuestionarios

6 videos

Apache Spark Architecture5mOverview of Apache Spark Cluster Modes6mHow to Run an Apache Spark Application6mUsing Apache Spark on IBM Cloud4mSetting Apache Spark Configuration5mRunning Spark on Kubernetes 4m

2 lecturas

Summary & Highlights2mSummary & Highlights2m

4 ejercicios de práctica

Practice Quiz: Spark Architecture6mGraded Quiz: Spark Architecture9mPractice Quiz: Spark Runtime Environments6mGraded Quiz: Spark Runtime Environments9m

Semana6

Semana 6

3 horas para completar

Monitoring & Tuning

Platforms and applications require monitoring and tuning to manage issues that inevitably happen. In this module you'll learn about connecting the Apache Spark user interface web server and using the same UI web server to manage application processes. Identify common Apache Spark application issues. Learn about debugging issues using the application UI and locating related log files. Discover and gain real-world knowledge about how Spark manages memory and processor resources via videos and in the available hands-on lab.

3 horas para completar

5 videos (Total 30 minutos), 4 lecturas, 4 cuestionarios

5 videos

The Apache Spark User Interface5mMonitoring Application Progress7mDebugging Apache Spark Application Issues5mUnderstanding Memory Resources5mUnderstanding Processor Resources5m

4 lecturas

Summary & Highlights2mInstructions for the Final Exam2mCongrats & Next Steps2m Team & Acknowledgements2m

3 ejercicios de práctica

Practice Quiz: Introduction to Monitoring & Tuning10mGraded Quiz: Introduction to Monitoring & Tuning15mFinal Exam 1h 40m

Reseñas

4.3

35 reseña

5 stars
62,90 %
4 stars
20,43 %
3 stars
9,67 %
2 stars
2,68 %
1 star
4,30 %

Principales reseñas sobre INTRODUCTION TO BIG DATA WITH SPARK AND HADOOP

por RS7 de may. de 2022

Fantastic blend of theory and practical (labs). The labs are short and have concise material.

por AP7 de mar. de 2022

the lecture was clearly understandible and I feel very gratefull to have this lecture

thank you it was phenomenal😊

por RR1 de may. de 2022

hands on lab and quizzes at the end of each session was very helpful

por ND8 de nov. de 2022

All the thinks I need to know about Big Data, Spark, Hadoop and Hive and explained in details

Ver todas las reseñas

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Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
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Introduction to Big Data with Spark and Hadoop

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Qué aprenderás

Habilidades que obtendrás