The Issues with Using Data Lakes in Managing Big Data

A data lake is critical for speeding up data collection and processing. There is little debate about the obvious benefits of these lakes, but like with any technology, there are substantial negatives to using a data lake. If not properly implemented, the lake may end up causing more harm than good to the company. This article will attempt to address certain data lake-related difficulties.


Data Lakes Challenges in Data Management


Using data lakes presents several technical and business issues which include;


Security and Governance 


Data lakes are open knowledge sources that are used to streamline analytics pipelines. However, the lake's open aspect makes it impossible to apply security measures. Because the lake is open and rate data is inputted, it is difficult to regulate the data coming in. To address this issue, data lake designers should collaborate with data security teams to implement access control measures and secure data while not jeopardizing loading processes or governance initiatives.


Security isn't the only issue plaguing data lakes, but also quality. Since data lakes collect data from several sources and pool it in a single location, judging data quality becomes difficult. It is troublesome since it gives incorrect results when employed in commercial operations. When the data is wrong, the findings are wrong, which leads to a loss of faith in the data lake and ultimately in the company. To address this issue, increased coordination between data governance teams and data stewards is required so that data may be profiled, quality policies established, and action done to improve quality.


Meta-Management is Rendered Impossible.


One of the most significant aspects of data management is metadata management. Without metadata, data stewards would be forced to rely on non-automated programs such as Word and Excel. Data stewards mostly work with metadata rather than actual data as much as it's not incorporated on data lakes, which eventually adds to the data management issues. The lack of metadata makes it harder to execute critical large data management operations such as data verification and adoption of organizational rules. Lack of metadata management make data lakes lose dependability, lowering its value to the enterprise.


Conflict in the Organization Prevents the Realization of Full Value.


Data lakes are extremely beneficial, but they are not immune to internal conflicts. If the organization's structure is riddled with red tape and internal politics, the lake will be of little value. For example, if data analysts are unable to access the data without gaining permission, the process becomes slow and productivity suffers. Different departments may have different regulations for the same data source, resulting in variations in rules, policies, and standards. This problem can be alleviated slightly by implementing a strong data governance policy to maintain consistent data standards across the entire organization. While there's no doubt on the value of data lakes, better governance norms are required to increase management and transparency.


It's Challenging To Identify Data Sources.


Identifying data sources in a data lake is a common difficulty in big data management. Categorizing and labeling data sources is critical since it prevents problems such as data duplication. However, this is not done regularly, which is troublesome. At a minimum, the source of metadata should be documented and made available to consumers.


Taking on the Big Data Management Challenges


The use of data lakes simplifies big data administration significantly. However, there are several drawbacks to using the centralized repository. These difficulties can impede the utilization of the data lake because it is more difficult to identify relevant insights when the data is faulty. The biggest challenge in resolving these issues is applying multidisciplinary solutions. Resolving issues with data lakes necessitates complete technical solutions, changes to corporate regulations, and a shift in work culture. Organizations, on the other hand, must handle these difficulties. Businesses will not get the most out of their data lakes if this is not done.

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