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.
Offert par
IBMÀ propos de ce cours
45 508 consultations récentes
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Niveau débutant
7 weeks of study, 1-2 hours / week
Approx. 9 heures pour terminer
Anglais
Sous-titres : Anglais
Ce que vous allez apprendre
Deep insight into the impact of Big Data including use cases, tools, and processing methods.
Knowledge of the Apache Hadoop architecture, ecosystem, and practices, and the use of applications including HDFS, HBase, Spark, and MapReduce.
Know-how to apply Spark programming basics, including parallel programming basics for DataFrames, data sets, and Spark SQL.
Proficiency with Spark’s RDDs, data sets, use of Catalyst and Tungsten to optimize SparkSQL, and Spark’s development and runtime environment options.
Compétences que vous acquerrez
Apache HadoopSparkSQLSparkMLBig DataApache Spark
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Niveau débutant
7 weeks of study, 1-2 hours / week
Approx. 9 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
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.
Programme du cours : ce que vous apprendrez dans ce cours
1 heure pour terminer
What is Big Data?
Begin your acquisition of Big Data knowledge with the most up-to-date definition of Big Data. You’ll explore the impact of Big Data on everyday personal tasks and business transactions with Big Data Use Cases. Learn how Big Data uses Parallel Processing, Scaling, and Data Parallelism. Learn about commonly used Big Data tools. Then, go beyond the hype and explore additional Big Data viewpoints.
1 heure pour terminer
7 vidéos (Total 42 min), 2 lectures, 2 quiz
1 heure pour terminer
Introduction to the Hadoop Ecosystem
In this module, you'll gain a fundamental understanding of the Apache Hadoop architecture, ecosystem, practices, and commonly used applications including Distributed File System (HDFS), MapReduce, HIVE and HBase. Gain practical skills in this module's lab when you launch a single node Hadoop cluster using Docker and run MapReduce jobs.
1 heure pour terminer
6 vidéos (Total 37 min), 1 lecture, 3 quiz
1 heure pour terminer
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 heure pour terminer
5 vidéos (Total 24 min), 1 lecture, 2 quiz
2 heures pour terminer
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 heures pour terminer
5 vidéos (Total 26 min), 1 lecture, 2 quiz
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