"JDBC Metadata, MySQL, and Oracle Recipes" is the only book that focuses on metadata or annotation-based code recipes for JDBC API for use with Oracle and MySQL. It continues where the authors other book, "JDBC Recipes: A Problem-Solution Approach," leaves off.

This edition is also a Java EE 5-compliant book, perfect for lightweight Java database development. And it provides cut-and-paste code templates that can be immediately customized and applied in each developer's application development.

"JDBC Recipes" provides easy-to-implement, usable solutions to problems in relational databases that use JDBC. You will be able to integrate these solutions into your web-based applications, such as Java servlets, JavaServer Pages, and Java server-side frameworks. This handy book allows you to cut and paste the solutions without any code changes.

This book focuses on topics that have been ignored in most other JDBC books, such as database and result set metadata. It will help you develop database solutions, like adapters, connectors, and frameworks using Java/JDBC. The insightful solutions will enable you to handle all data types, including large binary objects. A unique feature of the book is that it presents JDBC solutions (result sets) in XML.

Apache Spark's speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples using PySpark. In each chapter, author Mahmoud Parsian shows you how to solve a data problem with a set of Spark transformations and algorithms. You'll learn how to tackle problems involving ETL, design patterns, machine learning algorithms, data partitioning, and genomics analysis. Each detailed recipe includes PySpark algorithms using the PySpark driver and shell script. With this book, you will: Learn how to select Spark transformations for optimized solutions Explore powerful transformations and reductions including reduceByKey(), combineByKey(), and mapPartitions() Understand data partitioning for optimized queries Build and apply a model using PySpark design patterns Apply motif-finding algorithms to graph data Analyze graph data by using the GraphFrames API Apply PySpark algorithms to clinical and genomics data Learn how to use and apply feature engineering in ML algorithms Understand and use practical and pragmatic data design patterns

First book to market on metadata specific recipes related to JDBC and its use with MySQL and Oracle, databases standard to Java. Compliant with the new Java EE 5. Provides cut and paste code templates that can be immediately customized and applied in each developer's application development.

* The only standard size JDBC "cookbook" in market with clear specification of problems and ready-to-be-used working code solutions (in a cut-and-paste fashion) that work for at least two leading databases such as MySQL and Oracle. * Most existing JDBC-related books provide only generic solutions, which might not work on any vendor's database. This book shows the importance of "vendor" factor for solving JDBC problems. * Complete coverage of database and result set "metadata" (which is missing from most JDBC books).

If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You'll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactions Data mining algorithms (K-means, KNN, and Naive Bayes) Using huge genomic data to sequence DNA and RNA Naive Bayes theorem and Markov chains for data and market prediction Recommendation algorithms and pairwise document similarity Linear regression, Cox regression, and Pearson correlation Allelic frequency and mining DNA Social network analysis (recommendation systems, counting triangles, sentiment analysis)