Do you want to start big data management? Here is a mini-guide in a nutshell that will help system analysts to recognize methods and approaches, but also to identify the most useful support tools, optimizing on one side the data center and, on the other, the service to users.
Companies need more detailed information, ever more precise and, possibly, in real time. To the managers of the information systems there is nothing left to do but to review the setting of data management, preparing the data centers to solve the theme of Big Data.
Metaphorically speaking, the functional principle is that of the Stock Exchange: information flows before the eyes and we must be able to visualize and interpret them as quickly as possible to make decisions on which competitiveness and business success depend. On the one hand, it requires experience and, on the other hand, a good infrastructure equipped with automatism to support information flows.
What does a system analyst need to do to start a Big Data management project?
Big Data Management: 4 things to know before starting
Here is a mini-guide in a nutshell that will help system analysts to recognize methods and approaches, but also to identify the most useful support tools, optimizing on one side the data center and, on the other hand, the service to users.
1) To get to know the corporate IT architecture well
One of the fundamental principles for starting a big data project is starting from a fundamental analysis aimed at understanding how an information system works and, in particular, how databases and infrastructures work in more detail. Before discouraging you, know that there are some support tools such as, for example, Cloudera, Hadoop, Spark, Hive, Pig, Flume, Sqoop or Mesos, support frames that help to manage the various configurations of information flows passing through the data center.
2) Learn to manage the heterogeneity of information
Another important point at the method level is knowing that your Big Data Management archive could include structured data and unstructured data, coming from various sources such as data warehouses, Hadoop, NoSQL, various storage solutions, files or applications.
This means that you need to learn how to organize all these types of data in such a way that the system can process them in the most efficient way. To do this, make sure that all the personal data are consistent and consistent, avoiding in this way to create multiple versions of the same information on multiple unsynchronized databases.
3) Set also the appropriate security criteria
Data security is also a strategic priority. A system analyst should also know how to organize, manage and protect information. There are scores of data management products available on the market that will help you in this activity.
To do this, you must familiarize yourself with the data protection processes that characterize your organization as well as with all security and compliance policies. Depending on how sensitive or not the data is, you need to consider masking, drafting or encryption solutions.
4) Ensuring the quality of services to end users
The last important point that must be resolved with respect to a Big Data Management project is that the data serve different professional figures. Therefore, the objective of management is to guarantee not only that there are satisfactory interrogation methods, but also adequate response times. Therefore, before starting a big data management project, it is important to assess the quality of service requirements established by users (Quality of Services – QoS). For example, do you know how much data they want to analyze? Do you know how fast the response times should be for each query? In the case of a large database that requires real-time response times, what you need to know is that by allocating the largest possible number of data on the memory or on the Flash cache there are guarantees of greater speed compared to query times. Excellent database environments with maximum memory read speeds are HP’s Vertica, IBM’s Blue Accelerator or Sap’s Hana.
Very important in a Big Data Management project is also to understand the result you want to reach the customer, or the type of answers they are trying to achieve. In fact, if you know the results to which the users are aiming, it is easier to organize data and systems to achieve the goals of maximum efficiency.