| Book Detail | |
| What's special / Useful in this book | This version of the book on the analytics of big data illustrates various emerging technologies available on various distinct platforms. The reader will find valuable and substantial information on the following- Data warehousing and mining technologies dealing with Big Data, Hadoop Ecosystem, Tableau data visualization software, a platform on Google Cloud. Two Apache open-source distributed products have also been discussed. Kafka-an event streaming platform, Storm—a real-time computation system and the recent data platform on Google Cloud, used by whatsapp i.e. Qubole are also explained. Alongwith these, this book provides information on two data warehousing applications-Presto (including PrestoDB and PrestoSQL/Trino) and Teradata Enterprise access for Hadoop. Following applications also find place in the book: SAP HANA, a platform for ERP (Enterprise Resource Planning) software, InfoSphere Biginsights 2.1.2, Amazor Elastic MapReduce Hadoop Distribution, Hewlett Packard Enterprise's HPE), a big data platform- Vertica Statistical Analysis System (SAS) viz. one of the best tools for creating statistical modelling used by data analysts. Contents |
| Publication Year | 2024 |
| ISBN-13 | 9789392549519 |
| Language | English |
| Edition | 1st (2024) |
| Pages | 432 |
| Preface | |
| Preface | Big data analytics is the often complex process of examining big data to uncover information such as hidden patterns, correlations, market trends and customer preferences that can help organizations make informed business decisions. Big Data Analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers. There's no single technology that encompasses big data analytics. Of course, there's advanced analytics that can be applied to big data, but in reality several types of technology work together to help you get the most value from your information. Artificial intelligence (AI), mobile, social and the Internet of Things (loT) are driving data complexity through new forms and sources of data. For example, big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media much of it generated in real time and at a very large scale. |
| Table of Contents | |
| Table of Contents | 1. Big Data and Data Science 2. Hadoop and its Ecosytem 3.Various BIg Data and Hadoop Umbrella Systems 4. Tableu: The interactive Data Visualization Platform 5. Apache Storm Distributed Real-Time Computation Systems 6.Apache Spark Unified Analytics Engine for Large-scale data Processing 7.Apache Kafka 8.Qubole 9.Presto (SQL Query Engine) 10. SAP HANA 11.Misc other Hadoop based Platforms 12.Introduction to Big Data Analytics 13.Statistical Descriptive Data Analysis 14.Introduction to R Programming Language & Software environment 15.Diagnostic or Inferential Analytics 16.Predictive Modelling & Linear Regression 17. Time Series Analysis & Forecasting 18.Prescriptive Analytics 19.Data Repositories & Mining 20.Text Analysis |