By Padma Priya Chitturi
- Use Apache Spark for facts processing with those hands-on recipes
- Implement end-to-end, large-scale info research higher than ever before
- Work with strong libraries comparable to MLLib, SciPy, NumPy, and Pandas to realize insights out of your data
Spark has emerged because the so much promising monstrous info analytics engine for info technological know-how pros. the genuine energy and cost of Apache Spark lies in its skill to execute info technology projects with pace and accuracy. Spark's promoting aspect is that it combines ETL, batch analytics, real-time movement research, computer studying, graph processing, and visualizations. It allows you to take on the complexities that include uncooked unstructured information units with ease.
This advisor gets you cozy and assured appearing information technological know-how initiatives with Spark. you'll know about implementations together with disbursed deep studying, numerical computing, and scalable laptop studying. you can be proven potent options to tricky thoughts in information technological know-how utilizing Spark's information technological know-how libraries comparable to MLLib, Pandas, NumPy, SciPy, and extra. those basic and effective recipes will enable you enforce algorithms and optimize your work.
What you are going to learn
- Explore the subjects of knowledge mining, textual content mining, usual Language Processing, details retrieval, and laptop learning.
- Solve real-world analytical issues of huge facts sets.
- Address information technology demanding situations with analytical instruments on a allotted method like Spark (apt for iterative algorithms), which deals in-memory processing and extra flexibility for information research at scale.
- Get hands-on adventure with algorithms like category, regression, and advice on actual datasets utilizing Spark MLLib package.
- Learn approximately numerical and clinical computing utilizing NumPy and SciPy on Spark.
- Use Predictive version Markup Language (PMML) in Spark for statistical facts mining models.
About the Author
Padma Priya Chitturi is Analytics Lead at Fractal Analytics Pvt Ltd and has over 5 years of expertise in colossal information processing. presently, she is a part of potential improvement at Fractal and liable for resolution improvement for analytical difficulties throughout a number of enterprise domain names at huge scale. sooner than this, she labored for an airways product on a real-time processing platform serving a million person requests/sec at Amadeus software program Labs. She has labored on figuring out large-scale deep networks (Jeffrey dean's paintings in Google mind) for photo type at the gigantic information platform Spark. She works heavily with sizeable info applied sciences reminiscent of Spark, hurricane, Cassandra and Hadoop. She used to be an open resource contributor to Apache Storm.
Table of Contents
- Big info Analytics with Spark
- Tricky data with Spark
- Data research with Spark
- Clustering, class, and Regression
- Working with Spark MLlib
- NLP with Spark
- Working with glowing Water - H2O
- Data Visualization with Spark
- Deep studying on Spark
- Working with SparkR
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Additional info for Apache Spark for Data Science Cookbook
Apache Spark for Data Science Cookbook by Padma Priya Chitturi