This book places a strong emphasis on optimal practices to guarantee efficient, cogent growth. The importance of end-to-end, flexible, adjustable, high-performance data pipeline systems with analytical components and appropriate visualization outcomes is also emphasized in the book. The target audience for this book is software engineers, architects, and data scientists who are interested in designing and implementing big data analytical systems that use Hadoop, the Hadoop ecosystem, and other related technologies. You’ll discover ways to facilitate and improve development. You’ll learn the value of hybrid systems, which combine multiple analytical components into a single application. The examples will emphasize this hybrid strategy heavily. In order to develop analytical systems that go beyond the fundamentals of classification, clustering, and recommendation, this book offers the ideal combination of architecture, design, and implementation information.


Basic descriptive statistics, linear algebra, matrices, vectors, and polynomials are all concepts that college students—at least those who sought science degrees—learn about in order to grasp analytics. You will never comprehend what a K-Entails classification model or even linear regression means without any knowledge of that. One reason why normal programmers can’t always handle data science is due to this. Data science is analytics.

Analytics is the use of arithmetic, statistics, and artificial intelligence to analyze large amounts of data. Machine learning and artificial intelligence are related terms. These are operations in mathematics. Understanding the conceptual underpinnings of each algorithm is crucial for appropriately interpreting the outcomes. Analytics demands distributed scalable infrastructure since it makes use of matrices and linear algebra; otherwise, you risk drawing the wrong conclusions. Hadoop is a distributed computing environment that can run analysis wherever the data resides in addition to being an efficient distributed storage system for big amounts of data. By enabling customers to perform a distributed sophisticated analysis on Hadoop data, RapidMiner takes full advantage of all the opportunities that Hadoop presents.

It is clear from looking at the runtimes for analytical algorithms that today’s restrictions on the size of data sets have disappeared, albeit at the cost of longer runtimes. In all circumstances, this is prohibitive for interactive reports, but it’s also probably true for predictive analytics if the model generation needs to be quick or done in real-time. An in-memory engine is still the fastest choice in certain circumstances. The Hadoop engine is cumbersome. When dealing with extremely large data sets, the in-Hadoop engine is sometimes the only choice and is the fastest when dealing with smaller data sets.

A particular engine will always be better than others depending on the application at hand, hence RapidMiner supports both in-memory and in-Hadoop engines to give customers the flexibility to address all of their analytical challenges. The firm wants customers of RapidMiner to always be able to choose the best engine for their particular application and receive the best results in the shortest amount of time.

Topics covered by this book:

  • In chapter 1 we learn about Building Data Analytic Systems with Hadoop in detail After this we will move to Chapter 2 which is about  Scala and python Refresher.

  • Next is Chapter 3 which is Standard Toolkits for Hadoop and Analytics. We learn about toolkits in detail. Then we to next chapter 4  Relational, NoSQL, and Graph Databases .we learn the database and graphs  concepts.

  • Chapter 5 Data Pipelines in which is we get to know that How to Construct Data Pipelines. Then chapter 6 is about Advanced Search Techniques with Hadoop, Lucene.

  • After this we move to the part 2 of book which is Architectures and Algorithms chapter 7 gives An Overview of Analytical Techniques and Algorithm. Then next we learn about Rule Engines, System Control, and System Orchestration.

  • Chapter 9 is about  Putting It All Together as well as Designing a Complete Analytical Systems. Then moving to part 3 which is Components and Systems . This part starts from Visualizers which is about Seeing and Interacting with the Analysis.

  • Then part 4 of book is Case Studies and Applications. Chapter 11 is about A Case Study in Bioinformatics: Analyzing Microscope Slide Data and Chapter 12 is about A Bayesian Analysis Component: Identifying Credit Card Fraud.

  • Chapter 13 is Searching for Oil as well as Geographical Data Analysis with Apache Mahout. Next move to Chapter 14  Image As Big Data” Systems these are Some Case Studies.

  • In the last Chapter 15 is all about Building a General Purpose Data Pipelines and Chapter 16 Conclusions and the Future of Big Data Analysis.

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