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The Self-Service Data Roadmap
Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can’t scale data science teams fast enough to keep up with the growing amounts of data to transform. What’s the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridge...
Agent Computing and Multi-Agent Systems
This book constitutes the refereed proceedings of the 9th Pacific Rim International Workshop on Multi-Agents, PRIMA 2006, held in Guilin, China, in August 2006. The book presents 39 revised full papers and 57 revised short papers together with 4 invited talks, addressing subjects from theoretical and methodological issues to applications. Topics include agent models, agent architectures, agent-oriented software engineering, semantic Web service, collaboration, coordination and negotiation, and more.
Self-Managing Distributed Systems
This book constitutes the refereed proceedings of the 14th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management, DSOM 2003, held in Heidelberg, Germany in October 2002. The 20 revised full papers and 6 revised short papers presented together with a keynote paper were carefully reviewed and selected from a total of 105 submissions. The papers are organized in topical sections on self-configuration, peer-to-peer management, self-optimization and performance management, utility management, self-protection and access control, manageability and instrumentation, and context-awareness.
97 Things Every Data Engineer Should Know
Take advantage of the sky-high demand for data engineers today. With this in-depth book, current and aspiring engineers will learn powerful, real-world best practices for managing data big and small. Contributors from Google, Microsoft, IBM, Facebook, Databricks, and GitHub share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey from MIT Open Learning, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Projects include: Building pipelines Stream processing Data privacy and security Data governance and lineage Data storage and architecture Ecosystem of modern tools Data team makeup and culture Career advice.
Architecting Data and Machine Learning Platforms
All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way. This handbook shows you how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, and multicloud tools like Snowflake and Databricks. Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle from ingestion to activation in a cloud environment using real-world enterprise architectures. You'll learn how to transform, secure, and modernize familiar solutions like data warehouses and data lakes, and you'll be able ...
Proceedings of the International Congress on Information and Communication Technology
This volume contains 69 papers presented at ICICT 2015: International Congress on Information and Communication Technology. The conference was held during 9th and 10th October, 2015, Udaipur, India and organized by CSI Udaipur Chapter, Division IV, SIG-WNS, SIG-e-Agriculture in association with ACM Udaipur Professional Chapter, The Institution of Engineers (India), Udaipur Local Centre and Mining Engineers Association of India, Rajasthan Udaipur Chapter. This volume contains papers mainly focused on ICT for Managerial Applications, E-governance, IOT and e-Mining.
NPC 2004
This book constitutes the refereed proceedings of the IFIP International Conference on Network and Parallel Computing, NPC 2004, held in Wuhan, China in October 2004. Also included are selected refereed papers from two workshops associated with NPC 2004. The 46 revised full papers and 23 revised short papers presented together with abstracts of 5 invited presentations were selected from a total of 338 submissions. The 25 workshop revised papers included also were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on grid computing, peer-to-peer computing, Web techniques, cluster computing, parallel programming environments, network architecture, network security, network storage, intelligent sensor networks, and multimedia modeling and security in next generation network information systems.
High Performance Computing – HiPC 2005
This book constitutes the refereed proceedings of the 12th International Conference on High-Performance Computing, HiPC 2005, held in Goa, India in December 2005. The 50 revised full papers presented were carefully reviewed and selected from 362 submissions. After the keynote section and the presentation of the 2 awarded best contributions the papers are organized in topical sections on algorithms, applications, architecture, systems software, communication networks, and systems and networks.
The Self-Service Data Roadmap
The world's most valuable resource is data. Companies across all industry verticals are using data-driven insights as a key competitive advantage. But the time required for transforming raw data to insights can take days or weeks when you want it in minutes or hours. Data scientists spend nearly 80% of their time in data engineering, rather than developing insights. And most organizations can't scale their data science teams fast enough to keep up with growing business needs for better, faster insights. This book will help data engineers, data scientists, and data team managers address these issues by building a self-service data science platform that democratizes the ability to extract insi...
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