Skip to main content
Top

2021 | OriginalPaper | Chapter

2. Introduction to Big Data Technology

Authors : Bilal Abu-Salih, Pornpit Wongthongtham, Dengya Zhu, Kit Yan Chan, Amit Rudra

Published in: Social Big Data Analytics

Publisher: Springer Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Big data is no more “all just hype” but widely applied in nearly all aspects of our business, governments, and organizations with the technology stack of AI. Its influences are far beyond a simple technique innovation but involves all rears in the world. This chapter will first have historical review of big data; followed by discussion of characteristics of big data, i.e. from the 3V’s to up 10V’s of big data. The chapter then introduces technology stacks for an organization to build a big data application, from infrastructure/platform/ecosystem to constructional units and components. Finally, we provide some big data online resources for reference.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Footnotes
1
The term “Information overload” has been popularized by Alvin Toffler in his book, Future Shock, 1971.
 
2
Containers are packages of software that includes everything that it needs to run, such as code, dependencies, libraries, and more. Container differs from Virtual Machines because container shares OS kernel rather than have a full copy of OS kernel for each VM.
 
5
To avoid confusion, we always use AM to represent Application Master, which is per application based, and use full name of Application Manager, which is component of Resource manager.
 
15
Mob: Medium-sized Objects.
 
Literature
1.
go back to reference Dumbill, E. (2012). Planning for big data. Sebastopol: O’Reilly Media, Inc. Dumbill, E. (2012). Planning for big data. Sebastopol: O’Reilly Media, Inc.
2.
go back to reference Emrouznejad, A. (2016). Big data optimization: Recent developments and challenges (Studies in big data) (Vol. 18). Switzerland: Springer. Emrouznejad, A. (2016). Big data optimization: Recent developments and challenges (Studies in big data) (Vol. 18). Switzerland: Springer.
4.
go back to reference Gantz, J., & Reinsel, D. (2012). The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC Analyze the future, 2007(2012), 1–16. Gantz, J., & Reinsel, D. (2012). The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC Analyze the future, 2007(2012), 1–16.
5.
go back to reference Hudy, A. C. (2015). Turning the big data crush into an advantage. Information Management Journal, 49(1), 38–41. Hudy, A. C. (2015). Turning the big data crush into an advantage. Information Management Journal, 49(1), 38–41.
6.
go back to reference Lammerant, H., & De Hert, P. (2016). Visions of technology. In Data protection on the move (pp. 163–194). Switzerland: Springer. Lammerant, H., & De Hert, P. (2016). Visions of technology. In Data protection on the move (pp. 163–194). Switzerland: Springer.
7.
go back to reference Partners, N., Big data executive survey 2016: Big data business impact: Achieving business results through innovation and disruption. 2017. Partners, N., Big data executive survey 2016: Big data business impact: Achieving business results through innovation and disruption. 2017.
8.
go back to reference Chamorro-Premuzic, T. (2014). How the web distorts reality and impairs our judgement skills. The Guardian. Chamorro-Premuzic, T. (2014). How the web distorts reality and impairs our judgement skills. The Guardian.
9.
go back to reference Rogers, P., Puryear, R., & Root, J. (2013). Infobesity: The enemy of good decisions (Vol. 11). Insights: Bain Brief. Rogers, P., Puryear, R., & Root, J. (2013). Infobesity: The enemy of good decisions (Vol. 11). Insights: Bain Brief.
10.
go back to reference Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the ACM, 54(8), 88–98.CrossRef Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the ACM, 54(8), 88–98.CrossRef
11.
go back to reference Gantz, J., & Reinsel, D. (2010). The digital universe decade-are you ready (pp. 1–16). External publication of IDC (Analyse the Future) information and data. Gantz, J., & Reinsel, D. (2010). The digital universe decade-are you ready (pp. 1–16). External publication of IDC (Analyse the Future) information and data.
12.
go back to reference Joa, D., et al. (2012). Unstructured data integration with a data warehouse. Google Patents. Joa, D., et al. (2012). Unstructured data integration with a data warehouse. Google Patents.
13.
go back to reference Tien, J. M. (2013). Big data: Unleashing information. Journal of Systems Science and Systems Engineering, 22(2), 127–151.CrossRef Tien, J. M. (2013). Big data: Unleashing information. Journal of Systems Science and Systems Engineering, 22(2), 127–151.CrossRef
14.
go back to reference Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. New York: Sage. Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. New York: Sage.
15.
go back to reference Gartner. (2015). Gartner survey shows more than 75 percent of companies are investing or planning to invest in big data in the next two years. Gartner Newsroom. [11/06/2017]. Gartner. (2015). Gartner survey shows more than 75 percent of companies are investing or planning to invest in big data in the next two years. Gartner Newsroom. [11/06/2017].
16.
go back to reference Hill, L., et al. (2015). Data-driven innovation for growth and well-being. Paris: OECD. Hill, L., et al. (2015). Data-driven innovation for growth and well-being. Paris: OECD.
17.
go back to reference Meneghello, J., et al. (2020). Unlocking social media and user generated content as a data source for knowledge management. International Journal of Knowledge Management (IJKM), 16(1), 101–122. Meneghello, J., et al. (2020). Unlocking social media and user generated content as a data source for knowledge management. International Journal of Knowledge Management (IJKM), 16(1), 101–122.
18.
go back to reference Abu-Salih, B., et al. (2020). Time-aware domain-based social influence prediction. Journal of Big Data, 7(1), 10.CrossRef Abu-Salih, B., et al. (2020). Time-aware domain-based social influence prediction. Journal of Big Data, 7(1), 10.CrossRef
19.
go back to reference Abu-Salih, B., et al. (2020). Relational learning analysis of social politics using knowledge graph embedding. arXiv, preprint arXiv:2006.01626. Abu-Salih, B., et al. (2020). Relational learning analysis of social politics using knowledge graph embedding. arXiv, preprint arXiv:2006.01626.
20.
go back to reference Abu-Salih, B., et al. (2019). Social credibility incorporating semantic analysis and machine learning: A survey of the state-of-the-art and future research directions. Cham: Springer. Abu-Salih, B., et al. (2019). Social credibility incorporating semantic analysis and machine learning: A survey of the state-of-the-art and future research directions. Cham: Springer.
21.
go back to reference Sallam, R., et al. (2017). Magic quadrant for business intelligence and analytics platforms. Stamford: Gartner. Sallam, R., et al. (2017). Magic quadrant for business intelligence and analytics platforms. Stamford: Gartner.
22.
go back to reference Phillipps, T. (2013). The analytics advantage we’re just getting started. New York: Deloitte. Phillipps, T. (2013). The analytics advantage we’re just getting started. New York: Deloitte.
23.
go back to reference Ghorbanian, M., Dolatabadi, S. H., & Siano, P. (2019). Big data issues in smart grids: A survey. IEEE Systems Journal, 13(4), 4158–4168.CrossRef Ghorbanian, M., Dolatabadi, S. H., & Siano, P. (2019). Big data issues in smart grids: A survey. IEEE Systems Journal, 13(4), 4158–4168.CrossRef
24.
go back to reference Chang, W.L. and N. Grady, NIST big data interoperability framework: Volume 1, big data definitions. 2015. Chang, W.L. and N. Grady, NIST big data interoperability framework: Volume 1, big data definitions. 2015.
25.
go back to reference Favaretto, M., et al. (2020). What is your definition of big data? Researchers’ understanding of the phenomenon of the decade. PLoS One, 15(2), e0228987.CrossRef Favaretto, M., et al. (2020). What is your definition of big data? Researchers’ understanding of the phenomenon of the decade. PLoS One, 15(2), e0228987.CrossRef
27.
go back to reference Diebold, F. (2003). Big data dynamic factor models. In Advances in economics and econometrics: Theory and applications, eighth world congress. Cambridge: Cambridge University Press. Diebold, F. (2003). Big data dynamic factor models. In Advances in economics and econometrics: Theory and applications, eighth world congress. Cambridge: Cambridge University Press.
28.
go back to reference Commission, E. (2015). The EU data protection reform and Big Data [Fact sheet]. Commission, E. (2015). The EU data protection reform and Big Data [Fact sheet].
30.
go back to reference Ward, J. S., & Barker, A. (2013). Undefined by data: a survey of big data definitions. arXiv, preprint arXiv:1309.5821. Ward, J. S., & Barker, A. (2013). Undefined by data: a survey of big data definitions. arXiv, preprint arXiv:1309.5821.
31.
go back to reference De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. In AIP conference proceedings. College Park: American Institute of Physics. De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. In AIP conference proceedings. College Park: American Institute of Physics.
32.
go back to reference Chan, K. Y., et al. (2018). Affective design using machine learning: A survey and its prospect of conjoining big data. International Journal of Computer Integrated Manufacturing, 1–25. Chan, K. Y., et al. (2018). Affective design using machine learning: A survey and its prospect of conjoining big data. International Journal of Computer Integrated Manufacturing, 1–25.
33.
go back to reference Abu-Salih, B., et al. (2018). CredSaT: Credibility ranking of users in big social data incorporating semantic analysis and temporal factor. Journal of Information Science, 45(2), 259–280.CrossRef Abu-Salih, B., et al. (2018). CredSaT: Credibility ranking of users in big social data incorporating semantic analysis and temporal factor. Journal of Information Science, 45(2), 259–280.CrossRef
34.
go back to reference Abu-Salih, B., Wongthongtham, P., & Chan, K. Y. (2018). Twitter mining for ontology-based domain discovery incorporating machine learning. Journal of Knowledge Management, 22(5), 949–981.CrossRef Abu-Salih, B., Wongthongtham, P., & Chan, K. Y. (2018). Twitter mining for ontology-based domain discovery incorporating machine learning. Journal of Knowledge Management, 22(5), 949–981.CrossRef
35.
go back to reference Abu-Salih, B. (2020). Domain-specific knowledge graphs: A survey. arXiv, preprint arXiv:2011.00235. Abu-Salih, B. (2020). Domain-specific knowledge graphs: A survey. arXiv, preprint arXiv:2011.00235.
36.
go back to reference Wongthongtham, P., & Abu-Salih, B. (2015). Ontology and trust based data warehouse in new generation of business intelligence: State-of-the-art, challenges, and opportunities. In Industrial Informatics (INDIN), 2015 IEEE 13th International Conference on. Cambridge: IEEE. Wongthongtham, P., & Abu-Salih, B. (2015). Ontology and trust based data warehouse in new generation of business intelligence: State-of-the-art, challenges, and opportunities. In Industrial Informatics (INDIN), 2015 IEEE 13th International Conference on. Cambridge: IEEE.
38.
go back to reference Khan, N., et al. (2018). The 10 Vs, issues and challenges of big data. In Proceedings of the 2018 International Conference on Big Data and Education. Khan, N., et al. (2018). The 10 Vs, issues and challenges of big data. In Proceedings of the 2018 International Conference on Big Data and Education.
39.
go back to reference Abu-Salih, B., Alsawalqah, H., Elshqeirat, B., Issa, T., & Wongthongtham, P. (2019). Toward a knowledge-based personalised recommender system for mobile app development. arXiv, preprint arXiv:1909.03733. Abu-Salih, B., Alsawalqah, H., Elshqeirat, B., Issa, T., & Wongthongtham, P. (2019). Toward a knowledge-based personalised recommender system for mobile app development. arXiv, preprint arXiv:1909.03733.
40.
go back to reference Wongthongtham, P., et al. (2018). State-of-the-art ontology annotation for personalised teaching and learning and prospects for smart learning recommender based on multiple intelligence and fuzzy ontology. International Journal of Fuzzy Systems, 20(4), 1357–1372.CrossRef Wongthongtham, P., et al. (2018). State-of-the-art ontology annotation for personalised teaching and learning and prospects for smart learning recommender based on multiple intelligence and fuzzy ontology. International Journal of Fuzzy Systems, 20(4), 1357–1372.CrossRef
41.
go back to reference Wongthongtham, P., & Abu-Salih, B. (2018). Ontology-based approach for identifying the credibility domain in social big data. Journal of Organizational Computing and Electronic Commerce, 28(4), 354–377.CrossRef Wongthongtham, P., & Abu-Salih, B. (2018). Ontology-based approach for identifying the credibility domain in social big data. Journal of Organizational Computing and Electronic Commerce, 28(4), 354–377.CrossRef
42.
go back to reference Nabipourshiri, R., Abu-Salih, B., & Wongthongtham, P. (2018). Tree-based classification to users’ trustworthiness in OSNs. In Proceedings of the 2018 10th International Conference on Computer and Automation Engineering (pp. 190–194). Brisbane: ACM.CrossRef Nabipourshiri, R., Abu-Salih, B., & Wongthongtham, P. (2018). Tree-based classification to users’ trustworthiness in OSNs. In Proceedings of the 2018 10th International Conference on Computer and Automation Engineering (pp. 190–194). Brisbane: ACM.CrossRef
43.
go back to reference Chan, K. Y., et al. (2018). Affective design using machine learning: A survey and its prospect of conjoining big data. International Journal of Computer Integrated Manufacturing, 33(7), 645–669.CrossRef Chan, K. Y., et al. (2018). Affective design using machine learning: A survey and its prospect of conjoining big data. International Journal of Computer Integrated Manufacturing, 33(7), 645–669.CrossRef
44.
go back to reference Sagiroglu, S., & Sinanc, D. (2013). Big data: A review. In 2013 International conference on collaboration technologies and systems (CTS). San Diego: IEEE. Sagiroglu, S., & Sinanc, D. (2013). Big data: A review. In 2013 International conference on collaboration technologies and systems (CTS). San Diego: IEEE.
45.
go back to reference Manyika, J., et al. (2011). Big data: The next frontier for innovation, competition, and productivity. Washington: McKinsey Global Institute. Manyika, J., et al. (2011). Big data: The next frontier for innovation, competition, and productivity. Washington: McKinsey Global Institute.
46.
go back to reference Jacobson, R. (2013). 2.5 quintillion bytes of data created every day. How does CPG & Retail manage it. In IBM. Jacobson, R. (2013). 2.5 quintillion bytes of data created every day. How does CPG & Retail manage it. In IBM.
47.
go back to reference Furht, B., & Villanustre, F. (2016). Introduction to big data. In Big data technologies and applications (pp. 3–11). Switzerland: Springer. Furht, B., & Villanustre, F. (2016). Introduction to big data. In Big data technologies and applications (pp. 3–11). Switzerland: Springer.
48.
go back to reference Hofmann, E. (2017). Big data and supply chain decisions: The impact of volume, variety and velocity properties on the bullwhip effect. International Journal of Production Research, 55(17), 5108–5126.CrossRef Hofmann, E. (2017). Big data and supply chain decisions: The impact of volume, variety and velocity properties on the bullwhip effect. International Journal of Production Research, 55(17), 5108–5126.CrossRef
49.
go back to reference Rubin, V., & Lukoianova, T. (2013). Veracity roadmap: Is big data objective, truthful and credible? Advances in Classification Research Online, 24(1), 4. Rubin, V., & Lukoianova, T. (2013). Veracity roadmap: Is big data objective, truthful and credible? Advances in Classification Research Online, 24(1), 4.
50.
go back to reference Demchenko, Y., et al. (2013). Addressing big data issues in scientific data infrastructure. In Collaboration Technologies and Systems (CTS), 2013 International Conference on. San Diego: IEEE. Demchenko, Y., et al. (2013). Addressing big data issues in scientific data infrastructure. In Collaboration Technologies and Systems (CTS), 2013 International Conference on. San Diego: IEEE.
51.
go back to reference Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.CrossRef Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.CrossRef
52.
go back to reference Fan, W., & Bifet, A. (2013). Mining big data. ACM SIGKDD Explorations Newsletter, 14(2), 1.CrossRef Fan, W., & Bifet, A. (2013). Mining big data. ACM SIGKDD Explorations Newsletter, 14(2), 1.CrossRef
53.
go back to reference Jukić, N., et al. (2015). Augmenting data warehouses with big data. Information Systems Management, 32(3), 200–209.CrossRef Jukić, N., et al. (2015). Augmenting data warehouses with big data. Information Systems Management, 32(3), 200–209.CrossRef
54.
go back to reference Kacfah Emani, C., Cullot, N., & Nicolle, C. (2015). Understandable big data: A survey. Computer Science Review, 17, 70–81.CrossRef Kacfah Emani, C., Cullot, N., & Nicolle, C. (2015). Understandable big data: A survey. Computer Science Review, 17, 70–81.CrossRef
55.
go back to reference Hitzler, P., & Janowicz, K. (2013). Linked data, big data, and the 4th paradigm. Semantic Web, 4(3), 233–235.CrossRef Hitzler, P., & Janowicz, K. (2013). Linked data, big data, and the 4th paradigm. Semantic Web, 4(3), 233–235.CrossRef
56.
go back to reference Wasser, T., et al. (2015). Using ‘big data’to validate claims made in the pharmaceutical approval process. Journal of Medical Economics, 18(12), 1013–1019.CrossRef Wasser, T., et al. (2015). Using ‘big data’to validate claims made in the pharmaceutical approval process. Journal of Medical Economics, 18(12), 1013–1019.CrossRef
57.
go back to reference Uddin, M. F., & Gupta, N. (2014). Seven V’s of Big Data understanding Big Data to extract value. In Proceedings of the 2014 zone 1 conference of the American Society for Engineering Education. Bridgeport: IEEE. Uddin, M. F., & Gupta, N. (2014). Seven V’s of Big Data understanding Big Data to extract value. In Proceedings of the 2014 zone 1 conference of the American Society for Engineering Education. Bridgeport: IEEE.
58.
go back to reference Hackenberger, B. K. (2019). Data by data, Big Data. Croatian Medical Journal, 60(3), 290.CrossRef Hackenberger, B. K. (2019). Data by data, Big Data. Croatian Medical Journal, 60(3), 290.CrossRef
60.
go back to reference Armerding, T. (2018). The 17 biggest data breaches of the 21st century. CSO online, 26. Armerding, T. (2018). The 17 biggest data breaches of the 21st century. CSO online, 26.
61.
go back to reference Asokan, G., & Asokan, V. (2015). Leveraging “big data” to enhance the effectiveness of “one health” in an era of health informatics. Journal of Epidemiology and Global Health, 5(4), 311–314.CrossRef Asokan, G., & Asokan, V. (2015). Leveraging “big data” to enhance the effectiveness of “one health” in an era of health informatics. Journal of Epidemiology and Global Health, 5(4), 311–314.CrossRef
62.
go back to reference Sun, G., Li, F., & Jiang, W. (2019). Brief talk about big data graph analysis and visualization. Journal on Big Data, 1(1), 25. Sun, G., Li, F., & Jiang, W. (2019). Brief talk about big data graph analysis and visualization. Journal on Big Data, 1(1), 25.
63.
go back to reference Elgendy, N., & Elragal, A. (2014). Big data analytics: A literature review paper. In Industrial conference on data mining. Shenzhen: Springer. Elgendy, N., & Elragal, A. (2014). Big data analytics: A literature review paper. In Industrial conference on data mining. Shenzhen: Springer.
64.
go back to reference Armbrust, M., et al. (2010). A view of cloud computing. Communication of the ACM, 53(4), 50–58.CrossRef Armbrust, M., et al. (2010). A view of cloud computing. Communication of the ACM, 53(4), 50–58.CrossRef
65.
go back to reference Mell, P., & Grance, T. (2011). The NIST definition of cloud computing (p. 7). Gaithersburg: Information Technology Laboratory National Institute of Standards and Technology. Mell, P., & Grance, T. (2011). The NIST definition of cloud computing (p. 7). Gaithersburg: Information Technology Laboratory National Institute of Standards and Technology.
66.
go back to reference Modi, R. (2017). Azure for architects. Birmingham, Mumbai: Packt. Modi, R. (2017). Azure for architects. Birmingham, Mumbai: Packt.
67.
go back to reference Vidwans, R., & Wessler, M. (2013). IDaaS for dummies – A Wiley brand. Hoboken: Wiley. Vidwans, R., & Wessler, M. (2013). IDaaS for dummies – A Wiley brand. Hoboken: Wiley.
68.
go back to reference Carey, S. (2020). AWS vs Azure vs Google Cloud: What’s the best cloud platform for enterprise? In Computer World. New York: IDG Communications Ltd. Carey, S. (2020). AWS vs Azure vs Google Cloud: What’s the best cloud platform for enterprise? In Computer World. New York: IDG Communications Ltd.
69.
go back to reference Baum, D. (2020). Could data lakes for dummies – Snowflake special edition (p. 44). Hoboken: Wiley. Baum, D. (2020). Could data lakes for dummies – Snowflake special edition (p. 44). Hoboken: Wiley.
70.
go back to reference Codd, E. F. (1970). A relational model of data for large shared data banks. Communication of the ACM, 13(6), 377–387.CrossRef Codd, E. F. (1970). A relational model of data for large shared data banks. Communication of the ACM, 13(6), 377–387.CrossRef
71.
go back to reference Joe, K., & Baum, D. (2020). Cloud data warehousing for dummies – 2nd snowflake special edition. Hoboken: Wiley. Joe, K., & Baum, D. (2020). Cloud data warehousing for dummies – 2nd snowflake special edition. Hoboken: Wiley.
74.
go back to reference White, T. (2015). Hadoop: The definitive guide (4th ed., p. 727). Sebastopol: O’Reilly Media, Inc. White, T. (2015). Hadoop: The definitive guide (4th ed., p. 727). Sebastopol: O’Reilly Media, Inc.
75.
go back to reference Engle, C., et al. (2020). Shark: Fast data analysis using coarse-grained distributed memory. In SIGMOD ‘12: Proceedings of the 2012 ACM SIGMOD international conference on management of data (pp. 689–692). Scottsdale: ACM. Engle, C., et al. (2020). Shark: Fast data analysis using coarse-grained distributed memory. In SIGMOD ‘12: Proceedings of the 2012 ACM SIGMOD international conference on management of data (pp. 689–692). Scottsdale: ACM.
76.
go back to reference Karau, H., et al. (2015). Learning spark – Lighting-fast data analysis (1st ed.). Sebastopol: O’Reilly Media, Inc. Karau, H., et al. (2015). Learning spark – Lighting-fast data analysis (1st ed.). Sebastopol: O’Reilly Media, Inc.
77.
go back to reference Armbrust, M., et al. (2015). Spark SQL: Relational data processing in spark. In SIGMOD ‘15: Proceedings of the 2015 ACM SIGMOD international conference on management of data (pp. 1383–1394). Melbourne: ACM.CrossRef Armbrust, M., et al. (2015). Spark SQL: Relational data processing in spark. In SIGMOD ‘15: Proceedings of the 2015 ACM SIGMOD international conference on management of data (pp. 1383–1394). Melbourne: ACM.CrossRef
78.
go back to reference George, L. (2011). HBase: The definitive guide (p. 522). Sebastopol: O’Reilly Media, Inc. George, L. (2011). HBase: The definitive guide (p. 522). Sebastopol: O’Reilly Media, Inc.
Metadata
Title
Introduction to Big Data Technology
Authors
Bilal Abu-Salih
Pornpit Wongthongtham
Dengya Zhu
Kit Yan Chan
Amit Rudra
Copyright Year
2021
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-33-6652-7_2

Premium Partner