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1. |
Record Nr. |
UNINA9910731415103321 |
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Autore |
Esposito Edoardo |
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Titolo |
Indagini sul Novecento / / Edoardo Esposito |
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Pubbl/distr/stampa |
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Milano, : Ledizioni, 2023 |
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ISBN |
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Descrizione fisica |
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1 online resource (230 p.) |
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Soggetti |
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Literature (General) |
Littérature du XXe siècle |
texte poétique |
modernisme |
intertextualité dans Verga |
Gadda |
Pasolini |
Fortini |
traductions |
Montale et Yeats |
Sereni et Pound |
Spoon River Anthology |
style de traduction |
Roman Jakobson |
éditions Einaudi |
letteratura del Novecento |
testo poetico |
modernismo |
intertestualità in Verga |
traduzioni |
Montale e Yeats |
Sereni e Pound |
Antologia di Spoon River |
stile da traduzione |
edizioni Einaudi |
twentieth-century literature |
poetic text |
modernism |
intertextuality in Verga |
translations |
Montale and Yeats |
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Sereni and Pound |
style from translation |
Einaudi editions |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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Edoardo Esposito, già docente di Letterature comparate e di Teoria della letteratura, raccoglie in questo volume alcuni dei saggi rappresentativi del suo percorso di attraversamento e del suo modo di intendere la letteratura del Novecento. |
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2. |
Record Nr. |
UNINA9910427044503321 |
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Autore |
Mucchetti Mark |
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Titolo |
BigQuery for Data Warehousing : Managed Data Analysis in the Google Cloud / / by Mark Mucchetti |
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Pubbl/distr/stampa |
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (539 pages) |
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Disciplina |
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Soggetti |
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Database management |
Database Management |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Part I. Building a Warehouse -- 1. Settling into BigQuery -- 2. Starting Your Warehouse Project -- 3. All My Data -- 4. Managing BigQuery Costs -- Part II. Filling the Warehouse -- 5. Loading Data Into the Warehouse -- 6. Streaming Data Into the Warehouse -- 7. Dataflow -- Part III. Using the Warehouse -- 8. Care and Feeding of Your Warehouse -- 9. Querying the Warehouse -- 10. Scheduling Jobs -- 11. Serverless Functions with GCP -- 12. Cloud Logging -- Part IV. Maintaining the |
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Warehouse -- 13. Advanced BigQuery -- 14. Data Governance -- 15. Adapting to Long-Term Change -- Part V. Reporting On and Visualizing Your Data -- 16. Reporting -- 17. Dashboards and Visualization -- 18. Google Data Studio -- Part VI. Enhancing Your Data's Potential -- 19. BigQuery ML -- 20. Jupyter Notebooks and Public Datasets -- 21. Conclusion -- 22. Appendix A: Cloud Shell and Cloud SDK -- 23. Appendix B: Sample Project Charter. |
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Sommario/riassunto |
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Create a data warehouse, complete with reporting and dashboards using Google’s BigQuery technology. This book takes you from the basic concepts of data warehousing through the design, build, load, and maintenance phases. You will build capabilities to capture data from the operational environment, and then mine and analyze that data for insight into making your business more successful. You will gain practical knowledge about how to use BigQuery to solve data challenges in your organization. BigQuery is a managed cloud platform from Google that provides enterprise data warehousing and reporting capabilities. Part I of this book shows you how to design and provision a data warehouse in the BigQuery platform. Part II teaches you how to load and stream your operational data into the warehouse to make it ready for analysis and reporting. Parts III and IV cover querying and maintaining, helping you keep your information relevant with other Google Cloud Platform services and advanced BigQuery. Part V takes reporting to the next level by showing you how to create dashboards to provide at-a-glance visual representations of your business situation. Part VI provides an introduction to data science with BigQuery, covering machine learning and Jupyter notebooks. You will: Design a data warehouse for your project or organization Load data from a variety of external and internal sources Integrate other Google Cloud Platform services for more complex workflows Maintain and scale your data warehouse as your organization grows Analyze, report, and create dashboards on the information in the warehouse Become familiar with machine learning techniques using BigQuery ML. |
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