Understanding Data Lake Security
The successful Hadoop journey typically starts with data architecture optimization or new advanced analytic applications, which leads to the formation of what is known as a Data Lake. To prevent damage to the company’s business, customers, finances, and reputation, a Data Lake should meet the same high standards of security as any legacy data environment.
The general consensus in nearly every industry is that data is an essential new driver of competitive advantage. Hadoop plays a critical role in the modern data architecture by providing low-cost, large-scale data storage and processing. The successful Hadoop journey typically starts with data architecture optimization or new advanced analytic applications, which leads to the formation of what is known as a Data Lake. As new and existing types of data from machine sensors, server logs, clickstream data, and other sources flow into the Data Lake, it serves as a central repository based on shared Hadoop services that power deep organizational insights across a broad and diverse set of data.
The need to protect the Data Lake with comprehensive security is clear. As large and growing volumes of diverse data are channeled into the Data Lake, it will store vital and often highly sensitive business data. However, the external ecosystem of data and operational systems feeding the Data Lake is highly dynamic and can introduce new security threats on a regular basis. Users across multiple business units can access the Data Lake freely and refine, explore, and enrich its data, using methods of their own choosing, further increasing the risk of a breach. Any breach of this enterprise-wide data can result in catastrophic consequences: privacy violations, regulatory infractions, or the compromise of vital corporate intelligence. To prevent damage to the company’s business, customers, finances, and reputation, a Data Lake should meet the same high standards of security as any legacy data environment.
Piecemeal protections are no more effective for a Data Lake than they would be in a traditional repository. Effective Hadoop security depends on a holistic approach that revolves around five pillars of security: administration, authentication and perimeter security, authorization, auditing, and data protection.
Security administrators must address questions and provide enterprise-grade coverage across each of these areas as they design the infrastructure to secure data in Hadoop. If any of these pillars is vulnerable, it becomes a risk factor in the company’s Big Data environment. A Hadoop security strategy must address all five pillars, with a consistent implementation approach to ensure effectiveness.
You cannot achieve comprehensive protection across the Hadoop stack by using an assortment of point solutions. Security must be an integral part of the platform on which your Data Lake is built. This bottom-up approach makes it possible to enforce and manage security across the stack through a central point of administration, thereby preventing gaps and inconsistencies. This approach is especially important for Hadoop implementations in which new applications or data engines are always emerging in the form of new Open Source projects — a dynamic scenario that can quickly exacerbate any vulnerability.
Hortonworks helps customers maintain high levels of protection for enterprise data by building centralized security administration and management into the infrastructure of the Hortonworks Data Platform. HDP provides an enterprise-ready data platform with rich capabilities spanning security, governance, and operations. HDP includes powerful data security functionality that works across component technologies and integrates with preexisting EDW, RDBMS, and MPP systems. By implementing security at the platform level, Hortonworks ensures that security is consistently administered to all of the applications across the stack, simplifying the process of adding or removing Hadoop applications.