\"Data Information Decision\" Please respond to the following: •With the obvious
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Question
"Data Information Decision" Please respond to the following: •With the obvious pace of current technology development, data continues to grow daily. Imagine that you are a Database Administrator for a large organization. Identify the primary decisions a Database Administrator must make in order to manage such data, maintain data integrity, and maintain a database at its most ideal level. Provide a rationale for your response. •Suppose you are also in charge of securing your organization’s data. Identify at least two (2) data security measures that you believe are necessary to prevent data security issues. Justify your response
Explanation / Answer
With the explosion the role of DBA has shifted from mere Database creation to maintaing infrastructure in cloud.
Big data might be structured or unstructured data, but it is a large quantity of information and likely coming in at a high velocity. Business owners need to figure out what data is valuable to them, and although it might be difficult for DBAs to determine what data is valuable, the DBA can assist in making the data available and helping connect datasources.
DBA can follow the adapt the following skills/methods to face the large amount of data in an organisation:
1.Apache Hadoop. Hadoop is a framework used for multiple-node processing of data. With high degrees of parallelism, Hadoop can be a very scalable platform for processing large batches of data very fast. There are no real tables or structures, so the data can be imported in various formats, and there are several ways to connect Hadoop to Oracle databases. Understanding how to process data in Hadoop and how to integrate it with existing datasources are valuable big data DBA skills to develop.
2.Integrations. Being able to integrate data is already an important DBA skill, and with big data, there is an opportunity to talk more to the business to understand the data and what is considered valuable information. Your new big data skill area might just be communication with these business teams. And if you are not regularly connecting different datasources, start by pulling in data from other sources. Developing a service for different datasources will prove to be an invaluable resource for big data projects.
3.Big data technologies moved into the space traditionally occupied by data warehouses and made analysis faster and more capable. Like NoSQL databases, big data technologies empower technology professionals to perform significant amounts of work themselves and allow database administrators to focus on improving performance and finding better solutions.
4.Organizations that did adopt Hadoop found out that any production cluster larger than 20-30 nodes requires a full time admin. This admin’s job is surprising similar to a DBA’s job – he is responsible for the performance and availability of the cluster, the data it contains, and the jobs that run there. The list of tasks is almost endless and also strangely familiar – deployment, upgrades, troubleshooting, configuration, tuning, job management, installing tools, architecting processes, monitoring, backups, recovery, etc.
5.With larger data sets, data aggregation and reduction techniques often are worth applying. Handling the first aggregation of a data set within a data warehouse (accessed via SQL) can often save time and effort by reducing millions of records to thousands. However, remain aware that although database platforms are fantastically efficient at data aggregation, there is a price. That first aggregation often eliminates relevant details that can remain hidden throughout an analysis. It is often best to design and inspect the detail before grouping your results into a more manageable data set.
6.A Comprehensive Data Integrity Solution in the Cloud :RAIN6 of Zetta
RAIN6 implements Cryptographic hashes (such as SHA-1 and SHA-256) which are like CRCs with the added property that it is mathematically very difficult to “make up,” a file that matches the fingerprint.
By recording these “write receipts,” it can be independently verified and calculated that the data returned is free from corruption prior to relying on it.
7.AWS example for data integrity
AWS servers maintain a very high standards for data integrity.A DBA can utilise services provided by AWS or other cloud services distributors to maintain best services.
8.Security practices in cloud for securing large data sets:
1.End-Point Input Validation/Filtering
Many big data use cases in enterprise settings require data collection from many sources, such as end-point devices. For example, a security information and event management system (SIEM) may collect event logs from millions of hardware devices and software applications in an enterprise network. A key challenge in the data collection process is input validation: howcan we trust the data? How can we validate that a source of input data is not malicious and how can we filter malicious input from our collection? Input validation and filtering is a daunting challenge posed by untrusted input sources, especially with the bring your own device (BYOD) model.
2.Real-time Security/Compliance Monitoring
Real-time security monitoring has always been a challenge, given the number of alerts generated by (security) devices.These alerts (correlated or not)lead to many false positives, which are mostly ignored or simply “clicked away,” as humans cannot cope with the shear amount. This problem might even increase with big data, given the volume and velocity of data streams.
However, big data technologies might also provide an opportunity, in the sense that these technologies do allow for fast processing and analytics of different types of data.
Which in its turn can be used to provide, for instance, real-time anomaly detection based on scalable security analytics.
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