What did you learn about potential problems and difficulties that could arise or be experienced with this technology? Are there methods or techniques which can be applied to overcome or avoid this problems?
In the webinar of John Myers as well as Bob Muglia, they discussed how the organizations are adapting the approaches as well as technology for their data warehouse as well as data analytics. In the webinar, it is discussed the customer experiences that how the data warehouse within the cloud builds new opportunities for the business to gain cost savings. The topics that are covered in the seminar are such as the way that users can reduce the cost as well as the complexity of the data warehouses. It also reflects on the architecture that the Snowflake develops for their data warehouse based on the cloud infrastructure. Lastly, it discusses on the innovations that are required to make data warehouse within the cloud.
Due to a rapid adoption of the cloud infrastructure as well as software, services drive the tectonic shift within the IT enterprise. This shift is done to reexamine if the technology of data warehousing seeks solutions that are more than presently conservative software repackaged within the cloud (Kune et al., 2016). Due to a development of the new data warehouse for the cloud, Snowflake makes their enterprise data warehousing available due to rapid deployment of the cloud. It also delivers innovations in the scalability, ease of use as well as performance that goes beyond the conventional data warehousing. The Enterprise Management Associates (EMA) has shown that the traditional analytical architectures are not going away. It has been challenged by advanced data management technologies such as cloud (Mohanty, Jagadeesh, & Srivatsa, 2013). These challenges are forcing the organization to reexamine the traditional notions of the analytical environments such as data warehouses. Data warehouse requires adopting not only the advanced technologies, but it also requires a new way for the customers to use the data. According to the presentation by the Enterprise Management Associates, it is adopted that timely delivery of the data analytics as well as data warehouse are the primary motivation for the business to choose the cloud options.
Snowflake Elastic Data Warehouse is a new SQL data warehouse conveyed as a software-as-software (SaaS) within the cloud. This data warehouse handles the infrastructure, security as well as optimization such that the users can focus on loading as well as querying the data. John Myers, as well as Bob Muglia, discuss the innovations that are required to make data warehouse within the cloud. The key innovations are such that there is a native support as well as optimization of the diverse data (Clegg, 2015). Snowflake supports the semi-structured data within single database engine with no requirement of transformation as well as scarifying the performance. Snowflake can scale the data without affecting the performance. The other innovations include self-tuning service, enterprise-class security, extreme data as well as the availability of the service (Kim, 2015). The data warehouse service of Snowflake is fully dispersed transversely various data centers at all levels. Using the Snowflake Time Travel feature, the data are stored within fully replicated storage as well as recovery at that time.
The benefits of a data warehouse are that it provides with trusted infrastructure, which gives user confidence within the consistency of the data. It incorporates with wide varieties of data sources such as web logs, sensors, RFID, advanced analytics as well as third party data sources (Ohlhorst, 2012). The data warehouse can change track to identify the changes in configuration within the environment, which help to pinpoint the operational issues. The CEO of the Snowflake said that data warehousing is the market ripe for the purpose of innovation. The solution for the data warehouse that is discussed in the webinar is based on the architecture that is data back of almost 30 years. Due to a transformation of the data analytics, the requirements of the infrastructure are prohibitive (Sangupamba, Prat, & Comyn-Wattiau, 2014). From the first day, Snowflake focuses on creating a software service that can able to bring both transactional as well as machine-generated data for the business. Therefore, Snowflake reinvents the data warehouse for their customers (Zhang, 2016). The data warehouse of the Snowflake enables the service to access the data such that the analytics can focus to get assessment from the data rather than administration of the hardware as well as software.
The seminar by John Myers and Bob Muglia supports what have been learned in the class. It summarizes the use of the data warehouse in the cloud. It brings that the analysts can query the structured as well as semi-structured data within a single system. Due to change in the advanced technology, the traditional data warehouse remains a complex as well as expensive one. It is identified that Snowflake cloud service brings the power of the data warehousing that gives flexibility to the data warehouse stages. This cloud service can lower the cost of at least 90 percent than on-premises data warehouses. The seminar study materials help me a lot in my study. Overall, the seminar is good. In my point of view, I would like to give four rating to the study material of the seminar.
In this webinar, the speaker discusses about the how many types of IT systems are required to update, replacement, upgrade as well as extension due to changes in both technologies as well as business requirements. In most of the organizations, the enterprise software solutions as well as data integration requires modernization. Most of the organizations are scrambling to familiar with new technologies and advantages new data as well as platforms for the advantage of the business. Due to change in the technology, the technical organizations are rethinking of modernization of their infrastructure of data management (Jha, 2014). The data integration modernization is most important as it broads the role of data integration. The data integration plays an important role to capture process as well as move the data. With the use of modern data integration solutions, the organizations are not able to satisfy the future requirements of big data analytics as well as real data operations. The role of the new data platforms modernizes the environments of the data warehouse. Therefore, the databases of the business are to be built for both data warehousing as well as data analytics.
Data integration modernization can take place in the business based on the current state of infrastructure of the business as well as based on the new data platforms that they currently use. In the seminar, some of the recommendations are discussed based on the modernization efforts that the vendor selects to update the solution designs for the business (Krishnan, 2013). The recommendations are such that firstly with the use of data integration technique, it complements the high latency of the older data integration practices. Secondly, it embraces new data prep practices as well as a tool for the purpose of speed, ease of use, agility as well as simplicity (Stimmel, 2014). Thirdly, the new data integration modernization integrates the data in such a way that it enables the self-service access to big data for wide range of users. Fourthly, the purpose of modernization of the data integration gets the business as well as analytic values from multi-structured data. Fifthly, it is required to add right time functions at the time of modernization of the data integration solutions (Alkazemi, Nour, & Meelud, 2013). Sixthly, the data integration infrastructure is modernized using new types of the data platform. Lastly, it is considered to modernize the data integration tool with an integrated platform of various data management tools.
From the above seven recommendations, it is seen that the functionality of the new vendor product types can contribute to the modernization of the data integration (Raj, 2015). The requirements of data warehouses, as well as its requirements, are required to evolve. The modernization of the business is required to keep the business competitive as well as highly aligned with the business goals. The organizations are struggling to become educate with the current trends in order to modernize the data integration requirements. By adopting the modern development methods, most of the organizations are up-gradating as well as extending their existing data warehouse environments (Yu, 2015). The purpose of this webinar is to accelerate the audiences to understand the use of old as well as new technology to modernize the data warehouse. It also helps the readers to about newly available technologies, real world practices as well as products. It focuses on extending the business value of the existing data warehouses. The users can learn about the successful strategies that are used to modernize the existing warehouse programs as well as the data platforms (Stimmel, 2015). Even some of the organizations are not yet started to establish the data integration functions for the purpose of virtualization, real time operations, virtualization as well as in-memory processing.
The seminar by Philip Russom supports what have been learned in the class. From the seminar, the audiences learned about the IT trends that drive the requirement of the data integration modernization, about the modernization strategies, new data platforms, as well as tools, use in data modernization projects and future of the modern data integration (Jha, 2014). The TDWI’s Philip Russom bases the content of the research on the findings of new best practices report. As the users are begun to expand as well as modernize their business, therefore they require improving over the simple data models, its accuracy as well as quality. It is good news for the audiences present at the seminar that the data integration modernization is not only one of the ways to increase the speed within the data warehouse environment. However, there is a way to rethink about the save of money such as storage, maintenance, upgrades as well as administration. The seminar study materials help me a lot in my study. Overall, the seminar is good. In my point of view, I would like to give four rating to the study material of the seminar.
References
Alkazemi, B. Y., Nour, M. K., & Meelud, A. Q. (2013, October). Towards a Framework to Assess Legacy Systems. In Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on (pp. 924-928). IEEE.
Clegg, D. (2015). Evolving data warehouse and BI architectures: The big data challenge. TDWI ONSITE EDUCATION, 20(1), 19.
Jha, S., Jha, M., O’Brien, L., & Wells, M. (2014, November). Integrating legacy system into big data solutions: Time to make the change. InComputer Science and Engineering (APWC on CSE), 2014 Asia-Pacific World Congress on (pp. 1-10). IEEE.
Kim, H. (2015). Big Data: The Structure and Value of Big Data Analytics.
Krishnan, K. (2013). Data warehousing in the age of big data. Newnes.
Kune, R., Konugurthi, P. K., Agarwal, A., Chillarige, R. R., & Buyya, R. (2016). The anatomy of big data computing. Software: Practice and Experience, 46(1), 79-105.
Mohanty, S., Jagadeesh, M., & Srivatsa, H. (2013). Big Data imperatives: enterprise Big Data warehouse, BI implementations and analytics. Apress.
Ohlhorst, F. J. (2012). Big data analytics: turning big data into big money. John Wiley & Sons.
Raj, P., Raman, A., Nagaraj, D., & Duggirala, S. (2015). Big and Fast Data Analytics Yearning for High-Performance Computing. In High-Performance Big-Data Analytics (pp. 67-99). Springer International Publishing.
Sangupamba, O. M., Prat, N., & Comyn-Wattiau, I. (2014). Business intelligence and big data in the cloud: Opportunities for design-science researchers. In Advances in Conceptual Modeling (pp. 75-84). Springer International Publishing.
Stimmel, C. L. (2014). Big data analytics strategies for the smart grid. CRC Press.
Stimmel, C. L. (2015). Building the Foundation for Data Analytics. EDPACS,52(1), 1-13.
Yu, Y., Qiang, X., Sharma, H., & Madiradu, S. (2015, May). An Enterprise Application Architecture Assessment Framework: Driven by Business Value and Focusing on IT Supportability. In Proceedings of the 5th International Conference on IS Management and Evaluation 2015: ICIME 2015 (p. 179). Academic Conferences Limited.
Zhang, J., Zhang, L., Huang, H., Jiang, Z. L., & Wang, X. (2016). Key based data analytics across data centers considering bi-level resource provision in cloud computing. Future Generation Computer Systems, 62, 40-50
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