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Data Lakes

Dynamic Insights: Data Lakes


Data lakes, also called data hubs, offer a way to manage big data and make it available for analysis. A data lake combines massive storage capabilities for any type of data in any format with the processing power needed to transform and analyze the data. Often data lakes are implemented using Hadoop, object stores, data warehouse technology or a combination of these. They may be implemented on-premises, in the cloud or as a hybrid. In addition to handling volumes and varieties of information, data lakes enable faster access to information as it is generated. This dynamic insights research examines the state of data lake deployments in organizations’ big data processes today. Please answer the 13 questions below plus some demographic questions and you'll instantly receive our customized Dynamic Insights.

1. For how long has your organization had a data lake deployed?
2. Are you satisfied with the results of your organization’s data lake deployments?
3. Which departments or functions within your organization benefit or expect to benefit the most from your use of data lakes? (Select up to three)
4. Data from which of the following operational data sources does your organization store or plan to store in its data lake(s)? (Select all that apply)
5. Which is the primary data platform used by your organization to store large volumes of information for further operational or analytical use?
6. In addition to the platforms above, what are the primary technologies you use or plan to use to help manage and govern your data lake? (Select all that apply)
7. What is the relationship between current or planned data lake deployments and data warehouses or data marts in your organization?
8. How confident are you in your organization’s ability to analyze very large amounts of both structured and unstructured data?
9. How do you assess the level of expertise in your organization for creating and managing data lakes?
10. Which of the following are the most significant benefits your organization has realized or expects to realize from its data lake implementation? (Select all that apply.)
11. Which of the following is the most significant challenge in your data lake processes?
12. How can end users combine information from the data lake with other information and data sources?
13. Does your data lake technology provide virtualized data access to incorporate data sources without making a copy of the data?