Difference between Operational Database(OLTP) and Data Warehouse/(OLAP).

-

 Difference between Operational Database(OLTP) and Data Warehouse/(OLAP).

• The Operational Database is the source of information for the data warehouse. It includes detailed information used to run the day-to-day operations of the business.

The data frequently changes as updates are made and reflect the current value of the last transactions.

• Operational Database Management Systems also called OLTP (Online Transactions Processing Databases), are used to manage dynamic data in real-time.

• Data Warehouse Systems serve users or knowledge workers in the purpose of data analysis and decision-making. Such systems can organize and present information in specific formats to accommodate the diverse needs of various users. These systems are called as Online-Analytical Processing (OLAP) Systems.



Operational Database(OLTP) 

  • Operational systems are designed to support high-volume transaction processing.
  • Operational systems are usually concerned with current data.
  • Data within operational systems are mainly updated regularly according to need.
  • It is designed for real-time business dealing and processes.
  • It is optimized for a simple set of transactions, generally adding or retrieving a single row at a time per table.
  • It is optimized for validation of incoming information during transactions, uses validation data tables.
  • It supports thousands of concurrent clients.
  • Operational systems are widely process-oriented.
  • Operational systems are usually optimized to perform fast inserts and updates of associatively small volumes of data.
  • Data In
  • Less Number of data accessed.
  • Relational databases are created for online transactional processing (OLTP).



 Data Warehouse/(OLAP)

  • Data warehousing systems are typically designed to support high-volume analytical processing (i.e., OLAP).
  • Data warehousing systems are usually concerned with historical data.
  • Non-volatile, new data may be added regularly. Once Added rarely changed.
  • It is designed for the analysis of business measures by subject area, categories, and attributes.
  • It is optimized for extensive loads and high, complex, unpredictable queries that access many rows per table.
  • Loaded with consistent, valid information, requires no real-time validation.
  • It supports a few concurrent clients relative to OLTP.
  • Data warehousing systems are widely subject-oriented.
  • Data warehousing systems are usually optimized to perform fast retrievals of relatively high volumes of data.
  • Data Out
  • A large Number of data accessed.
  • Data Warehouse designed for on-line Analytical Processing (OLAP).

Comments

Post a Comment

Popular posts from this blog

Suppose that a data warehouse for Big-University consists of the following four dimensions: student, course, semester, and instructor, and two measures count and avg_grade. When at the lowest conceptual level (e.g., for a given student, course, semester, and instructor combination), the avg_grade measure stores the actual course grade of the student. At higher conceptual levels, avg_grade stores the average grade for the given combination. a) Draw a snowflake schema diagram for the data warehouse. b) Starting with the base cuboid [student, course, semester, instructor], what specific OLAP operations (e.g., roll-up from semester to year) should one perform in order to list the average grade of CS courses for each BigUniversity student. c) If each dimension has five levels (including all), such as “student < major < status < university < all”, how many cuboids will this cube contain (including the base and apex cuboids)?

-
Image
a) A Snowflake Shema is shown in figure below: b) The specific OLAP operations to be performed: 1. Roll-up on course from course_id to department. 2. Roll-up on a student from student_id to university. 3. Dice on course, student with department ="CS" and university = "biguniversity" 4. Drill-down on a student from the university to student_name. c) N = 4 dimensions The cube will contain 54 = 625 cuboids.
Read more

Describe how cloud computing technology can be applied to support remote ECG monitoring.

-
Image
  Healthcare: ECG (Electrocardiogram) Analysis in cloud computing Healthcare is a field, subject, sector, or territory in which information technology has several uses. These apps are being used to aid businesses in supporting scientists in developing disease prevention solutions. Cloud computing has emerged as an appealing alternative for constructing health monitoring systems as a result of the development of the Internet or to put it another way, as a result of the availability of the internet. The ECG machine, for example, is a health monitoring device that measures the human body's heartbeat and prints the results on graph paper. The electrocardiogram (ECG) measures the electrical activity of the heart's Cardium. A waveform is created as a result of this action, which is repeated throughout time and symbolizes the heartbeat.  The analysis of the shape is used to identify arrhythmias, and it is the most common way of detecting heart diseases. Here the meaning of arrhythmias
Read more

Explain market-Oriented Cloud computing architecture.

-
Image
  Market Oriented Cloud Computing (Mocc) As customers rely on Cloud providers to cover all of their computing needs, they will expect certain Qe from their providers to fulfill their objectives and continue their operations. Cloud providers must examine and satisfy the various QoS standards of each unique consumer as stipulated in unique SLAs. To do this, cloud providers cannot continue to install traditional system-centric resource management architectures that do not give incentives for them to share their resources while still treating all service requests as equal insignificance. Instead, market-oriented resource management is required to maintain the supply and demand for Cloud resources at market equilibrium, provide feedback in the form of economic incentives for both Cloud consumers and providers, and promote QoS-based resource allocation mechanisms that differentiate service requests based on their utility. The diagram depicts a high-level architecture for enabling market-orie
Read more