These principles assist teams in understanding the goals and we then need to go through the various options available and ensure they are in alignment with our goals, principles, and policies. There are too many possible data lake variations to provide specific steps for every security use case however, just as with a server, a network, or any other IT infrastructure component, we can outline security principles. Given the importance of “Big Data” analytics and applications to a company’s financial performance, securing data lakes is a critical priority for security teams. Essentially, we are securing an app at scale with enormous requirements for stored data, incoming data, data interactions, and network connections. However, data lakes add additional elements such as data feeds, data analysis (data lake house, third-party analysis tools, etc.) which increase the complexity of interactions beyond a simple storage container. Of course, since most commercial data lakes build off of existing cloud infrastructure, this should be the case. Many of the basic principles for securing a data lake will be familiar to anyone who has secured a cloud security storage container. We may make money when you click on links to our partners. ESecurityPlanet content and product recommendations are editorially independent.
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