Isbn: 9781492075257 - the self-service data roadmap: democratize data and reduce time to insight (35 resultados)

Paperback. Condición: Very Good. Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. 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.…

Paperback. Condición: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Paperback. Condición: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Paperback. Condición: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Condición: good. Signs of wear and consistent use.

Paperback. Condición: Very Good. Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. 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.…

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paperback. Condición: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

paperback. Condición: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

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Paperback. Condición: New. 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 bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service workBuild a self-service portal to support data discovery, quality, lineage, and governanceSelect the best approach for each self-service capability using open source cloud technologiesTailor self-service for the people, processes, and technology maturity of your data platformImplement capabilities to democratize data and reduce time to insightScale your self-service portal to support a large number of users within your organization.…

Paperback or Softback. Condición: New. The Self-Service Data Roadmap: Democratize Data and Reduce Time to Insight. Book.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: New. 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 bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service workBuild a self-service portal to support data discovery, quality, lineage, and governanceSelect the best approach for each self-service capability using open source cloud technologiesTailor self-service for the people, processes, and technology maturity of your data platformImplement capabilities to democratize data and reduce time to insightScale your self-service portal to support a large number of users within your organization.…

Paperback. Condición: new. Paperback. 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 cant scale data science teams fast enough to keep up with the growing amounts of data to transform. Whats 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 bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work.Build a self-service portal to support data discovery, quality, lineage, and governanceSelect the best approach for each self-service capability using open source cloud technologiesTailor self-service for the people, processes, and technology maturity of your data platformImplement capabilities to democratize data and reduce time to insightScale your self-service portal to support a large number of users within your organizationAbout the AuthorDr. Sandeep Uttamchandani is the Chief Data Officer and VP of Product Engineering at Unravel Data Systems. He brings nearly two decades of experience building enterprise data products as well as running petabyte-scale data platforms for business-critical analytics and ML applications. Most recently he was at Intuit, where he ran the data platform team powering analytics and ML for Intuit's financial accounting, payroll, and payments products.Previously in his career, Sandeep was co-founder and CEO of a startup using ML for managing security vulnerabilities of open-source products. He has played engineering leadership roles at VMware and IBM for 15+ years. Sandeep holds more than 40 issued patents, has 25+ publications in key technical conferences, and has received several product innovation and management excellence awards. He is a regular speaker in data conferences and a guest lecturer at universities. He advises startups and has served as a program/steering committee member for several conferences, including serving as Co-chair of Gartner's SF CDO Executive Summit, and Usenix Operational ML (OpML) conference. Sandeep holds a Ph.D and a Master's in Computer Science from the University of Illinois at Urbana-Champaign. Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New. In English.

Paperback. Condición: Brand New. 264 pages. 9.25x7.00x0.75 inches. In Stock.

Condición: New. 2020. Paperback. . . . . .

Paperback. Condición: New. 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 bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service workBuild a self-service portal to support data discovery, quality, lineage, and governanceSelect the best approach for each self-service capability using open source cloud technologiesTailor self-service for the people, processes, and technology maturity of your data platformImplement capabilities to democratize data and reduce time to insightScale your self-service portal to support a large number of users within your organization.…

Condición: NEW.

Condición: New. 2020. Paperback. . . . . . Books ship from the US and Ireland.

Taschenbuch. Condición: Neu. Neuware - 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 bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work.- Build a self-service portal to support data discovery, quality, lineage, and governance- Select the best approach for each self-service capability using open source cloud technologies- Tailor self-service for the people, processes, and technology maturity of your data platform- Implement capabilities to democratize data and reduce time to insight- Scale your self-service portal to support a large number of users within your organization.…