Название: Taming Big Data Analytics
Автор: Tanveer A.
Издательство: Amazon.com Services LLC
Год: 2020
Страниц: 328
Язык: английский
Формат: pdf, azw3, epub
Размер: 23.0 MB

Data analytics (DA) is the process of examining data sets in order to find trends and draw conclusions about the information they contain. Increasingly data analytics is used with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions. It is also used scientists and researchers to verify or disprove scientific models, theories and hypotheses.

Big Data analytics is the process of gathering, managing, and analyzing large sets of data (Big Data) to uncover patterns and other useful information. These patterns are a minefield of information and analysing them provide several insights that can be used by organizations to make business decisions. This analysis is essential for large organizations like Facebook who manage over a billion users every day, and use the data collected to help provide a better user experience.

Python and Big Data is the new combination invading the market space NOW. Python is in great demand among Big Data companies. In this blog, we will discuss the major benefits of using Python and why Python for big data has become a preferred choice among businesses these days. Python programming involves fewer lines of codes as compared to other languages available for programming. It is able to execute programs in the least lines of code. Moreover, Python automatically offers assistance to identify and associate data types.

The Pydoop package( Python and Hadoop) provides you access to the HDFS API for Hadoop which allows you to write Hadoop MapReduce programs and applications. How is the HDFS API beneficial for you? So, here you go. The HDFS API lets you read and write information easily on files, directories, and global file system properties without facing any hurdles.

Instead of putting R to work in production, many enterprise users leverage R as an exploratory and investigative tool. Data scientists will use R to run complicated analyses on sample data and then, after identifying a meaningful correlation or cluster in the data, put the finding into product through enterprise-scale tools. There are also R packages for popular open source big data platforms, including Hadoop and Spark.

Apache Spark, an open source data processing engine for batch processing, machine learning, data streaming and other types of analytics applications, is very significant example of Scala usage. Spark is written in Scala, and the language is central to its support for distributed data sets that are handled as collective software objects to help boost resiliency. However, Spark applications can be programmed in Java and the Python language in addition to Scala.

Advanced analytics won't produce an ounce of business insight without models, the statistical and machine learning algorithms that tease patterns and relationships from data and express them as mathematical equations. The algorithms tend to be immensely complex, mathematicians and statisticians (think data scientists) are needed to create them and then tweak the models to better fit changing business needs and conditions.

But analytical modeling is not a wholly quantitative, left-brain endeavor. It's a science, certainly, but it's an art, too. The art of modeling involves selecting the right data sets, algorithms and variables and the right techniques to format data for a particular business problem. But there's more to it than model-building mechanics. No model will do any good if the business doesn't understand its results. Communicating the results to executives so they understand what the model discovered and how it can benefit the business is critical but challenging, it's the "last mile" in the whole analytical modeling process and often the most treacherous. Without that understanding, though, business managers might be loath to use the analytical findings to make critical business decisions.

Topics covered in this book include:

Big Data Analytics
Architectures for Big Data Analytics
Data Analytics and its type
Predictive Analytics
Descriptive Analytics
Prescriptive Analytics
Diagnostic Analytics
Tools to Mine Big Data Analytics
Data Analytics Programming Languages
R programming language
Python
Scala
Apache Spark
SQL
Apache Hive
Analytical modeling is both science and art
Data Analytics Visualization Tools
Differences between Data Analytics, AI, Machine & Deep Learning
Data Lakes vs. Data Warehouses
Advanced Analytics techniques fuel data-driven organization
Must-have features for Big Data Analytics Tools
Data-driven storytelling opens analytics to all
Use Cases of Big Data Analytics in Real World
Key Skills That Data Scientists Need
Data analytics and career opportunities