Practical Big Data Analytics : Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R

Practical Big Data Analytics : Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R

Enregistré dans:

Détails bibliographiques
Auteur principal:	Dasgupta, Nataraj. (Auteur)
Support:	E-Book
Langue:	Anglais
Publié:	Birmingham : Packt Publishing, 2018.
Autres localisations:	Voir dans le Sudoc
Accès en ligne:	Accès à l'E-book

Documents similaires

Big data analytics : from strategic planning to enterprise integration with tools, techniques, NoSQL, and graph
par: Loshin, David.
Publié: (2014)

Big data analytics : a handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clusters.
par: Ankam, Venkat.
Publié: (2016)

Big Data and Analytics : The key concepts and practical applications of big data analytics
par: Kumar, Jugnesh.

Scala and Spark for big data analytics
par: Karim, Rezaul Md..
Publié: (2017)

Big data analytics with R
par: Walkowiak, Simon.
Publié: (2016)

Big data analytics
par: Rao, Calyampudi Radhakrishna (1920-2023).
Publié: (2015)

Big data analytics with Hadoop 3 : build highly effective analytics solutions to gain valuable insight into your big data
par: Alla, Sridhar.
Publié: (2018)

Hands-On Big Data Analytics with PySpark : Analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobs
par: Lai, Rudy.
Publié: (2019)

Big data analytics with Java
par: Mehta, Rajat.
Publié: (2017)

Big data analytics with SAS
par: Pope, David.
Publié: (2017)

Simplify Big Data Analytics with Amazon EMR : A beginner's guide to learning and implementing Amazon EMR for building data analytics solutions
par: Mishra, Sakti.
Publié: (2022)

Getting started with Greenplum for big data analytics : a hands-on guide on how to execute an analytics project from conceptualization to operationalization using Greenplum
par: Gollapudi, Sunila.
Publié: (2013)

Data Analytics: Principles, Tools, and Practices : A Complete Guide for Advanced Data Analytics Using the Latest Trends, Tools, and Technologies
par: Aroraa, Gaurav.

Analytics in a big data world : the essential guide to data science and its applications
par: Baesens, Bart (1975-....).
Publié: (2014)

Practical Real-time Data Processing and Analytics
par: Saxena, Shilpi.
Publié: (2017)

Real-time big data analytics : design, process, and analyze large sets of complex data in real time
par: Gupta, Sumit.
Publié: (2016)

SQL for Data Analytics : Harness the power of SQL to extract insights from data
par: Shan, Jun.

SQL for Data Analytics : Perform fast and efficient data analysis with the power of SQL
par: Malik, Upom.
Publié: (2019)

Big data forensics : learning Hadoop Investigations
par: Sremack, Joe.
Publié: (2015)

Big Data Using Hadoop and Hive
par: Kumar, Nitin.

Data Analytics for Marketing : A practical guide to analyzing marketing data using Python
par: Diaz-Bérrio, Guilherme.

Practical Predictive Analytics
par: Winters, Ralph.
Publié: (2017)

Data Analytics Made Easy : Use machine learning and data storytelling in your work without writing any code
par: Mauro, Andrea De.
Publié: (2021)

The data and analytics playbook : proven methods for governed data and analytic quality
par: Fryman, Lowell.
Publié: (2016)

Data Processing with Optimus : Supercharge big data preparation tasks for analytics and machine learning with Optimus using Dask and PySpark
par: Leon, Argenis.
Publié: (2021)

Big Data and Hadoop : Fundamentalscoco2 toolscoco2 and techniques for data-driven success
par: Bhushan, Mayank.

Predictive analytics and data mining : concepts and practice with Rapidminer
par: Kotu, Vijay.
Publié: (2015)

Data Analytics Using Splunk 9.x : A practical guide to implementing Splunk's features for performing data analysis at scale
par: Shillingford, Nadine.

Big Data Analysis with Python : Combine Spark and Python to unlock the powers of parallel computing and machine learning
par: Marin, Ivan (1973-....).
Publié: (2019)

Principles and Practice of Big Data : Preparing, Sharing, and Analyzing Complex Information
par: Berman, Jules J..
Publié: (2018)

Scaling Big Data with Hadoop and Solr
par: Karambelkar, Hrishikesh Vijay.
Publié: (2015)

Data Analytics : Systems Engineering - Cybersecurity - Project Management
par: Greco, Christopher.

Blockchain Data Analytics for Dummies
par: Solomon, Michael G..

Deep learning for data analytics : foundations, biomedical applications, and challenges
par: Das, Himansu.
Publié: (2020)

Data Science for Marketing Analytics : Achieve your marketing goals with the data analytics power of Python
par: Blanchard, Tommy.
Publié: (2019)

Essential PySpark for Scalable Data Analytics : A beginner's guide to harnessing the power and ease of PySpark 3
par: Nudurupati, Sreeram.
Publié: (2021)

Improving the user experience through practical data analytics : gain meaningful insight and increase your bottom line
par: Fritz, Mike.
Publié: (2015)

IoT Data Analytics using Python : Learn how to use Python to collect, analyze, and visualize IoT data (English Edition)
par: Hariharan, M S..

Mastering predictive analytics with R : machine learning techniques for advanced models
par: Miller, James D..
Publié: (2017)

Modern big data processing with Hadoop : expert techniques for architecting end-to-end big data solutions to get valuable insights
par: Kumar, V. Naresh.
Publié: (2018)

Université catholique de l'Ouest - Bibliothèques - 3 place André Leroy - BP10808 - 49008 ANGERS Cedex 1