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Sinopsis
Carry out data analysis with PySpark SQL, graphframes, and graph data processing using a problem-solution approach. This book provides solutions to problems related to dataframes, data manipulation summarization, and exploratory analysis. You will improve your skills in graph data analysis using graphframes and see how to optimize your PySpark SQL code.
PySpark SQL Recipes starts with recipes on creating dataframes from different types of data source, data aggregation and summarization, and exploratory data analysis using PySpark SQL. You'll also discover how to solve problems in graph analysis using graphframes.
On completing this book, you'll have ready-made code for all your PySpark SQL tasks, including creating dataframes using data from different file formats as well as from SQL or NoSQL databases.
What You Will Learn
- Understand PySpark SQL and its advanced features
- Use SQL and HiveQL with PySpark SQL
- Work with structured streaming
- Optimize PySpark SQL
- Master graphframes and graph processing
Who This Book Is For
Data scientists, Python programmers, and SQL programmers.
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Acerca del autor
Raju Kumar Mishra has strong interests in data science and systems that have the capability of handling large amounts of data and operating complex mathematical models through computational programming. He was inspired to pursue an M. Tech in computational sciences from Indian Institute of Science in Bangalore, India. Raju primarily works in the areas of data science and its different applications. Working as a corporate trainer he has developed unique insights that help him in teaching and explaining complex ideas with ease. Raju is also a data science consultant solving complex industrial problems. He works on programming tools such as R, Python, scikit-learn, Statsmodels, Hadoop, Hive, Pig, Spark, and many others. His venture Walsoul Private Ltd provides training in data science, programming, and big data.
Sundar Rajan Raman is an artificial intelligence practitioner currently working at Bank of America. He holds a Bachelor of Technology degree from the National Institute of Technology, India. Being a seasoned Java and J2EE programmer he has worked on critical applications for companies such as AT&T, Singtel, and Deutsche Bank. He is also a seasoned big data architect. His current focus is on artificial intelligence space including machine learning and deep learning.
De la contraportada
Carry out data analysis with PySpark SQL, graphframes, and graph data processing using a problem-solution approach. This book provides solutions to problems related to dataframes, data manipulation summarization, and exploratory analysis. You will improve your skills in graph data analysis using graphframes and see how to optimize your PySpark SQL code.
PySpark SQL Recipes starts with recipes on creating dataframes from different types of data source, data aggregation and summarization, and exploratory data analysis using PySpark SQL. You ll also discover how to solve problems in graph analysis using graphframes.
On completing this book, you ll have ready-made code for all your PySpark SQL tasks, including creating dataframes using data from different file formats as well as from SQL or NoSQL databases.
You will:
- Understand PySpark SQL and its advanced features
- Use SQL and HiveQL with PySpark SQL
- Work with structured streaming
- Optimize PySpark SQL
- Master graphframes and graph processing
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PySpark SQL Recipes: With HiveQL, Dataframe and Graphframes
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Condición: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide. Nº de ref. del artículo: ABNR-209284
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Pyspark SQL Recipes : With Hiveql, Dataframe and Graphframes
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Pyspark SQL Recipes: With Hiveql, Dataframe and Graphframes (Paperback or Softback)
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ISBN 10: 148424334X
ISBN 13: 9781484243343
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PySpark SQL Recipes: With HiveQL, Dataframe and Graphframes
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PySpark SQL Recipes: With HiveQL, Dataframe and Graphframes
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PySpark SQL Recipes (Paperback)
Publicado por
APress, Berkley, 2019
ISBN 10: 148424334X
ISBN 13: 9781484243343
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Librería: Grand Eagle Retail, Mason, OH, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: new. Paperback. Carry out data analysis with PySpark SQL, graphframes, and graph data processing using a problem-solution approach. This book provides solutions to problems related to dataframes, data manipulation summarization, and exploratory analysis. You will improve your skills in graph data analysis using graphframes and see how to optimize your PySpark SQL code.PySpark SQL Recipes starts with recipes on creating dataframes from different types of data source, data aggregation and summarization, and exploratory data analysis using PySpark SQL. Youll also discover how to solve problems in graph analysis using graphframes.On completing this book, youll have ready-made code for all your PySpark SQL tasks, including creating dataframes using data from different file formats as well as from SQL or NoSQL databases.What You Will LearnUnderstand PySpark SQL and its advanced featuresUse SQL and HiveQL with PySpark SQLWork with structured streamingOptimize PySpark SQL Master graphframes and graph processingWho This Book Is ForData scientists, Python programmers, and SQL programmers. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781484243343
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PySpark SQL Recipes
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. Carry out data analysis with PySpark SQL, graphframes, and graph data processing using a problem-solution approach. This book provides solutions to problems related to dataframes, data manipulation summarization, and exploratory analysis. You will improve your skills in graph data analysis using graphframes and see how to optimize your PySpark SQL code.PySpark SQL Recipes starts with recipes on creating dataframes from different types of data source, data aggregation and summarization, and exploratory data analysis using PySpark SQL. You'll also discover how to solve problems in graph analysis using graphframes.On completing this book, you'll have ready-made code for all your PySpark SQL tasks, including creating dataframes using data from different file formats as well as from SQL or NoSQL databases.What You Will LearnUnderstand PySpark SQL and its advanced featuresUse SQL and HiveQL with PySpark SQLWork with structured streamingOptimize PySpark SQL Master graphframes and graph processingWho This Book Is ForData scientists, Python programmers, and SQL programmers. Nº de ref. del artículo: LU-9781484243343
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