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This article mainly explains "what is the method of establishing Hadoop database". Interested friends may wish to have a look at it. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn "what is the method of building a Hadoop database"?

Working with Hadoop data: doing what needs to be done

The good thing is that these challenges are easy to overcome. Here are seven steps to solve and avoid these problems:

1. Create a data classification method. The organization of data objects in the data lake depends on how they are classified. In the classification, you need to determine the key dimensions of the data, such as data type, content, usage scheme, possible user groups, and data sensitivity. The latter involves protecting personal and corporate data, such as the protection of personally identifiable information about customers, or the protection of intellectual property rights.

2. Design a proper data architecture. Apply defined classification methods to guide how to organize data in a Hadoop environment. The final plan should include things such as file hierarchies for data storage, file and folder naming conventions, access methods and controls for different datasets, and mechanisms to guide data distribution.

3. Use data analysis tools. In many cases, the problem of insufficient understanding of all the data entering the data lake can be partially alleviated by analyzing the contents of the data. Data profiling tools can help by collecting information about the contents of data objects, providing some insight into how to classify them. As part of the data Lake implementation programme, the analysis of the data also helps to identify data quality issues that should be assessed to identify possible fixes to ensure that the information being used by data scientists and other analysts is accurate.

4. Standardize the data access process. The difficulties in effectively using datasets stored in the Hadoop data lake often stem from the fact that different analysis teams use multiple data access methods, many of which are undocumented. Therefore, the establishment of a general and direct API can simplify data access and eventually allow more users to use the data.

5. Develop searchable data directories. A more hidden obstacle to effective data access and use is that potential users do not know what is in the data lake and the location of different data sets in the Hadoop environment, except for information about data inheritance, quality, and circulation. The collaborative data catalog allows you to record these and other details about each data asset. For example, it grabs structural and semantic metadata, provenance and kinship records, access information, and so on. The data catalog also provides a forum for user groups to share experiences, questions, and suggestions about using data.

6. Implement adequate data protection. In addition to the general considerations of IT security, such as network boundary defense and role-based access control, other methods need to be used to prevent the exposure of sensitive information contained in the data lake. This includes mechanisms such as data encryption and data masking, as well as automatic monitoring to generate alerts about unauthorized data access or transmission.

7. Improve the awareness of data internally. Finally, make sure that your data lake users are aware of the need to proactively manage the data assets they contain. Teach them how to use the data catalog to find available datasets and how to configure the analysis application to access the data they need. At the same time, they were impressed by the importance of using data correctly and improving data quality.

In order to achieve the ultimate goal of making the data lake accessible and available, it is critical to develop a well-designed data processing plan before migrating the data to a Hadoop environment or cloud-based big data architecture. Taking the steps outlined in this article will help simplify the implementation of the data lake. More importantly, the right combination of planning, organization, and governance will help maximize the organization's investment in the data lake and reduce the risk of deployment failure.

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