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Thank you to The Doyle Group for sponsoring our July 2024 meeting!
Risk reduction and business growth are the primary drivers of data security activities. Ensuring that an organization’s data is secure reduces risk and adds competitive advantage. Security itself is a valuable asset.
Data security risks are associated with regulatory compliance, fiduciary responsibility for the enterprise and stockholders, reputation, and a legal and moral responsibility to protect the private and sensitive information of employees, business partners, and customers. Organizations can be fined for failure to comply with regulations and contractual obligations. Data breaches can cause a loss of reputation and customer confidence. (See Chapter 2.)
Business growth includes attaining and sustaining operational business goals. Data security issues, breaches, and unwarranted restrictions on employee access to data can directly impact operational success.
The goals of mitigating risks and growing the business can be complementary and mutually supportive if they are integrated into a coherent strategy of information management and protection.
June 2024 Newsletter.pdf
Data Security includes the planning, development, and execution of security policies and procedures to provide proper authentication, authorization, access, and auditing of data and information assets. The specifics of data security (which data needs to be protected, for example) differ between industries and countries. Nevertheless, the goal of data security practices is the same: To protect information assets in alignment with privacy and confidentiality regulations, contractual agreements, and business requirements. These requirements come from:
Effective data security policies and procedures ensure that the right people can use and update data in the right way, and that all inappropriate access and update is restricted (Ray, 2012) (see this figure). Understanding and complying with the privacy and confidentiality interests and needs of all stakeholders is in the best interest of every organization. Client, supplier, and constituent relationships all trust in, and depend on, the responsible use of data.
Set Database Performance Levels
System performance, data availability and recovery expectations, and expectations for teams to respond to issues are usually governed through Service Level Agreements (SLAs) between IT data management services organizations and data owners (this figure).
Typically, an SLA will identify the timeframes during which the database is expected to be available for use. Often an SLA will identify a specified maximum allowable execution time for a few application transactions (a mix of complex queries and updates). If the database is not available as agreed to, or if process execution times violate the SLA, the data owners will ask the DBA to identify and remediate the causes of the problem.
Data replication means same data is stored on multiple storage devices. In some situations, having duplicate databases is useful, such as in a high-availability environment where spreading the workload among identical databases in different hardware or even data centers can preserve functionality during peak usage times or disasters.
Replication can be active or passive:
Replication has two dimensions of scaling:
Multi-master replication, where updates can be submitted to any database node and then ripple through to other servers, is often desired, but increases complexity and cost.
Replication transparency occurs when data is replicated between database servers so that the information remains consistent throughout the database system and users cannot tell or even know which database copy they are using.
The two primary replication patterns are mirroring and log shipping (see this Figure).
The choice of replication method depends on how critical the data is, and how important it is that failover to the secondary server be immediate. Mirroring is usually a more expensive option than log shipping. For one secondary server, mirroring is effective; log shipping may be used to update additional secondary servers.
Data storage systems provide a way to encapsulate the instructions necessary to put data on disks and manage processing, so developers can simply use instructions to manipulate data. Databases are organized in three general ways: Hierarchical, Relational, and Non-Relational. These classes are not mutually exclusive (see this figure). Some database systems can read and write data organized in relational and non-relational structures. Hierarchical databases can be mapped to relational tables. Flat files with line delimiters can be read as tables with rows, and one or more columns can be defined to describe the row contents.
May 2024 Newsletter.pdf
The CAP Theorem (or Brewer’s Theorem) was developed in response to a shift toward more distributed systems (Brewer, 2000). The theorem asserts that a distributed system cannot comply with all parts of ACID at all time. The larger the system, the lower the compliance. A distributed system must instead trade-off between properties.
The CAP Theorem states that at most two of the three properties can exist in any shared-data system. This is usually stated with a ‘pick two’ statement, illustrated in this figure.
An interesting use of this theorem drives the Lambda Architecture design discussed in Chapter 14. Lambda Architecture uses two paths for data: a Speed path where availability and partition tolerance are most important, and a Batch path where consistency and availability are most important.
Loosely coupled systems require component databases to construct their own federated schema. A user will typically access other component database systems by using a multi-database language, but this removes any levels of location transparency, forcing the user to have direct knowledge of the federated schema. A user imports the data required from other component databases, and integrates it with their own to form a federated schema.
Tightly coupled systems consist of component systems that use independent processes to construct and publish an integrated federated schema, as illustrated in this figure. The same schema can apply to all parts of the federation, with no data replication.
Featured articles coming soon!
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