Author: Matthew Topol
Publisher: Packt Publishing Ltd
ISBN: 183546968X
Category : Computers
Languages : en
Pages : 406

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Book Description
Harness the power of Apache Arrow to optimize tabular data processing and develop robust, high-performance data systems with its standardized, language-independent columnar memory format Key Features Explore Apache Arrow's data types and integration with pandas, Polars, and Parquet Work with Arrow libraries such as Flight SQL, Acero compute engine, and Dataset APIs for tabular data Enhance and accelerate machine learning data pipelines using Apache Arrow and its subprojects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionApache Arrow is an open source, columnar in-memory data format designed for efficient data processing and analytics. This book harnesses the author’s 15 years of experience to show you a standardized way to work with tabular data across various programming languages and environments, enabling high-performance data processing and exchange. This updated second edition gives you an overview of the Arrow format, highlighting its versatility and benefits through real-world use cases. It guides you through enhancing data science workflows, optimizing performance with Apache Parquet and Spark, and ensuring seamless data translation. You’ll explore data interchange and storage formats, and Arrow's relationships with Parquet, Protocol Buffers, FlatBuffers, JSON, and CSV. You’ll also discover Apache Arrow subprojects, including Flight, SQL, Database Connectivity, and nanoarrow. You’ll learn to streamline machine learning workflows, use Arrow Dataset APIs, and integrate with popular analytical data systems such as Snowflake, Dremio, and DuckDB. The latter chapters provide real-world examples and case studies of products powered by Apache Arrow, providing practical insights into its applications. By the end of this book, you’ll have all the building blocks to create efficient and powerful analytical services and utilities with Apache Arrow.What you will learn Use Apache Arrow libraries to access data files, both locally and in the cloud Understand the zero-copy elements of the Apache Arrow format Improve the read performance of data pipelines by memory-mapping Arrow files Produce and consume Apache Arrow data efficiently by sharing memory with the C API Leverage the Arrow compute engine, Acero, to perform complex operations Create Arrow Flight servers and clients for transferring data quickly Build the Arrow libraries locally and contribute to the community Who this book is for This book is for developers, data engineers, and data scientists looking to explore the capabilities of Apache Arrow from the ground up. Whether you’re building utilities for data analytics and query engines, or building full pipelines with tabular data, this book can help you out regardless of your preferred programming language. A basic understanding of data analysis concepts is needed, but not necessary. Code examples are provided using C++, Python, and Go throughout the book.

Author: MicroStrategy Product Manuals
Publisher: MicroStrategy, Inc.
ISBN:
Category : Computers
Languages : en
Pages : 451

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Book Description

Author: Gerardus Blokdyk
Publisher:
ISBN: 9780655351689
Category :
Languages : en
Pages : 0

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Book Description

Author: Gerardus Blokdyk
Publisher: 5starcooks
ISBN: 9780655301684
Category :
Languages : en
Pages : 126

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Book Description
How do we keep improving In-Memory Analytics? Is In-Memory Analytics currently on schedule according to the plan? What are the compelling business reasons for embarking on In-Memory Analytics? Are there any easy-to-implement alternatives to In-Memory Analytics? Sometimes other solutions are available that do not require the cost implications of a full-blown project? Will team members perform In-Memory Analytics work when assigned and in a timely fashion? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make In-Memory Analytics investments work better. This In-Memory Analytics All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth In-Memory Analytics Self-Assessment. Featuring 701 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which In-Memory Analytics improvements can be made. In using the questions you will be better able to: - diagnose In-Memory Analytics projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in In-Memory Analytics and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the In-Memory Analytics Scorecard, you will develop a clear picture of which In-Memory Analytics areas need attention. Your purchase includes access details to the In-Memory Analytics self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. Your exclusive instant access details can be found in your book.

Author: Sven Weinzierl
Publisher: GRIN Verlag
ISBN: 3656970807
Category : Computers
Languages : en
Pages : 26

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Book Description
Research Paper (undergraduate) from the year 2015 in the subject Computer Science - Commercial Information Technology, grade: 1,0, University of Applied Sciences Ansbach, course: Wissentschaftliches Arbeiten, language: English, abstract: Nowadays, people produce large amounts of data with talking via smartphones, reading e-mails or using platforms to find the appropriate partner. Conventional technologies no longer cope with the increasing amount of data and come to their limits. Therefore new technologies of Big Data are required for data processing to overcome the data flood. At the beginning, this paper clarifies what Big Data is, the technologies of Big Data, how Big Data differs from Business Intelligence and a distinction is made between Data Warehouse and Business Intelligence. Furthermore, the theory of the Big Data technology in-memory analytics is explained and an implementation of this technology called “SAP HANA” is consulted and reviewed. In conclusion, the potential of in-memory analytics will be classified.

Author: Gerardus Blokdyk
Publisher: Createspace Independent Publishing Platform
ISBN: 9781717577559
Category :
Languages : en
Pages : 112

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Book Description
What are your current levels and trends in key measures or indicators of In-Memory Analytics product and process performance that are important to and directly serve your customers? how do these results compare with the performance of your competitors and other organizations with similar offerings? Does In-Memory Analytics systematically track and analyze outcomes for accountability and quality improvement? Does In-Memory Analytics appropriately measure and monitor risk? What are the disruptive In-Memory Analytics technologies that enable our organization to radically change our business processes? To what extent does management recognize In-Memory Analytics as a tool to increase the results? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make In-Memory Analytics investments work better. This In-Memory Analytics All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth In-Memory Analytics Self-Assessment. Featuring 489 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which In-Memory Analytics improvements can be made. In using the questions you will be better able to: - diagnose In-Memory Analytics projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in In-Memory Analytics and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the In-Memory Analytics Scorecard, you will develop a clear picture of which In-Memory Analytics areas need attention. Your purchase includes access details to the In-Memory Analytics self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. Your exclusive instant access details can be found in your book.

Author:
Publisher:
ISBN:
Category :
Languages : de
Pages :

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Book Description

Author: Pethuru Raj
Publisher: Springer
ISBN: 331920744X
Category : Computers
Languages : en
Pages : 443

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Book Description
This book presents a detailed review of high-performance computing infrastructures for next-generation big data and fast data analytics. Features: includes case studies and learning activities throughout the book and self-study exercises in every chapter; presents detailed case studies on social media analytics for intelligent businesses and on big data analytics (BDA) in the healthcare sector; describes the network infrastructure requirements for effective transfer of big data, and the storage infrastructure requirements of applications which generate big data; examines real-time analytics solutions; introduces in-database processing and in-memory analytics techniques for data mining; discusses the use of mainframes for handling real-time big data and the latest types of data management systems for BDA; provides information on the use of cluster, grid and cloud computing systems for BDA; reviews the peer-to-peer techniques and tools and the common information visualization techniques, used in BDA.

Author: Hasso Plattner
Publisher: Springer Science & Business Media
ISBN: 3642193633
Category : Business & Economics
Languages : en
Pages : 245

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Book Description
In the last 50 years the world has been completely transformed through the use of IT. We have now reached a new inflection point. Here we present, for the first time, how in-memory computing is changing the way businesses are run. Today, enterprise data is split into separate databases for performance reasons. Analytical data resides in warehouses, synchronized periodically with transactional systems. This separation makes flexible, real-time reporting on current data impossible. Multi-core CPUs, large main memories, cloud computing and powerful mobile devices are serving as the foundation for the transition of enterprises away from this restrictive model. We describe techniques that allow analytical and transactional processing at the speed of thought and enable new ways of doing business. The book is intended for university students, IT-professionals and IT-managers, but also for senior management who wish to create new business processes by leveraging in-memory computing.

Author: Benjamin Bengfort
Publisher: "O'Reilly Media, Inc."
ISBN: 1491913762
Category : Computers
Languages : en
Pages : 288

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Book Description
Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce. Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data. Understand core concepts behind Hadoop and cluster computing Use design patterns and parallel analytical algorithms to create distributed data analysis jobs Learn about data management, mining, and warehousing in a distributed context using Apache Hive and HBase Use Sqoop and Apache Flume to ingest data from relational databases Program complex Hadoop and Spark applications with Apache Pig and Spark DataFrames Perform machine learning techniques such as classification, clustering, and collaborative filtering with Spark’s MLlib