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.
от партнера
IBMОб этом курсе
Недавно просмотрено: 45 085
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Начальный уровень
7 weeks of study, 1-2 hours / week
Прибл. 9 часов на выполнение
Английский
Субтитры: Английский
Чему вы научитесь
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.
Приобретаемые навыки
Apache HadoopSparkSQLSparkMLBig DataApache Spark
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Начальный уровень
7 weeks of study, 1-2 hours / week
Прибл. 9 часов на выполнение
Английский
Субтитры: Английский
от партнера
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.
Программа курса: что вы изучите
1 ч. на завершение
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 ч. на завершение
7 видео ((всего 42 мин.)), 2 материалов для самостоятельного изучения, 2 тестов
1 ч. на завершение
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 ч. на завершение
6 видео ((всего 37 мин.)), 1 материал для самостоятельного изучения, 3 тестов
1 ч. на завершение
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 ч. на завершение
5 видео ((всего 24 мин.)), 1 материал для самостоятельного изучения, 2 тестов
2 ч. на завершение
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 ч. на завершение
5 видео ((всего 26 мин.)), 1 материал для самостоятельного изучения, 2 тестов
Часто задаваемые вопросы
When will I have access to the lectures and assignments?
Что я получу, оформив подписку на сертификацию?
Остались вопросы? Посетите Центр поддержки учащихся.
