Data governance represents a set of procedures to ensure important data are formally managed through the company.
It provides trust in the datasets as well as user responsibility in case of low data quality. This is of particular importance inside a Big Data platform fully integrated inside the company where multiple dataset, multiple treatments and multiple users coexist.
The right organization for the people eases the communication and the comprehension between teams while promoting an agile and data-centric culture. The concept of a single point of accountability is a major principle to achieve an effective project governance and it establishes new responsibilities (Data Council, Data Steward…).
Each components of the cluster offer by nature their own rules for access control. Each of the components of the cluster inherently has its own access control mechanisms. Fine grained access rules on a file system are not managed the same way as the one of a relational database. These rules can be based on roles (RBAC), on tags or even on the geolocation of IP address
Identity management includes user information and their existence, their group membership and the management rules applied to them. It is shared accross the company with the integration of the target platform to the company's LDAP server or its Active Directory.
The company is responsible to define a set of naming rules to ensure the integrity and the coherence of the system. The purpose is to guaranty to business and technical users the comprehension of names while enforcing coherent conventions, structures and names. Attribution of names must: be meaningful, be comprehensible without external explanations, reflects the targeted resource usages, differentiates itself from other names as much as possible, maximizes full name when possible, uses the same abbreviation, be singular.
Metadata, Data Lineage
The usage of tags enables the traceability of the data accross its data lifecycle: collect, qualification, enrichment, consumption. This process inform about where does the data come from, where it went through, who are the people or the application who access it and how was it altered. Having all those information systematically collected allows for data classification, user and application behavior captur, follow and analyse data related actions, ensure the respect usage according to the security policies in place.
Data qualification is the responsibility of the development teams. Unique interlocutor must be identified to be accountable and endorse responsibilities. It is crucial to constituate a readable responsiblity chain in which roles are not shared. Teams can rely on an existing toolset to validate and apply the relevant schema to each and every record. Moreover, the core components must prevent against a potential corruption of the data at rest and in motion.
Inside a multi-tenant environment, YARN carries the responsibility to ensure the availability of allocated resources to its users and groups of users. The resources traditionally managed by YARN are the memory and the CPU. Lately, the latest evolution of YARN reports the management of the network and disks. Through its ownership, process execution is associated to scheduling queues with a dedicated amount of cluster resources. Yarn enforces the disponibility of allocated resources for each user.
Information Lifecycle Management (ILM) encompasses the overall collect and traitment chain. It purposes is to plan the processing of data accross one or several clusters, to store and archive data while securing and preserving retention time.
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