In what way are big data and cloud technology complementary to one another?

-

Cloud computing and big data are complementary to each other. Rapid growth in big data is regarded as a problem. Clouds are evolving and providing solutions for the appropriate environment of big data while traditional storage cannot meet the requirements for dealing with big data, in addition to the need for data exchange between various distributed storage locations. Cloud computing provides solutions and addresses problems with big data. The cloud computing environment is expanding to be able to absorb big amounts of data as it follows the policy of data splitting, that is, to store data in more than one location or availability area. Cloud computing environments are built for general purpose workloads and resource pooling is used to provide flexibility on demand. Therefore, the cloud computing environment seems to be well suited for big data.



OR,

  •  Big Data is the large data set collected from large network-based systems. The Cloud is the location where this data is processed and accessed, usually using a software as a service (SaaS) model and utilizing AI and machine learning to present data to users.
  • Big Data and Cloud Data have a symbiotic relationship, as the Cloud infrastructure effectively enables storage, real-time processing, and Big Data analysis at scale and quickly. The biggest benefit of using Cloud storage for your Big Data is this scalability: Cloud storage is available on a pay-as-you-go basis. Essentially, the Cloud is the mechanism that serves, stores, and presents the opportunity for users to access and analyze Big Data efficiently.
  • Without Cloud Computing, there would be a huge amount of untapped potential within Big Data analytics, as current computers can’t analyze this scale of data feasibly, if at all. At the same time, Big Data plays a role in the development of Cloud Computing because without Big Data, there wouldn’t be anywhere near as much demand for Cloud-based solutions. So really, Cloud Computing services exist because of Big Data. The only reason we collect Big Data is that we now have the services capable of collecting, storing, and processing it. Simply, one would not exist without the other. A combination of the two can transform your organization into an efficient, data-driven market leader.

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

Suppose that a data warehouse consists of the four dimensions; date, spectator, location, and game, and the two measures, count and charge, where charge is the fee that a spectator pays when watching a game on a given date. Spectators may be students, adults, or seniors, with each category having its own charge rate. a) Draw a star schema diagram for the data b) Starting with the base cuboid [date; spectator; location; game], what specific OLAP operations should perform in order to list the total charge paid by student spectators at GM Place in 2004?

-
Image
  b) The specific OLAP operations to be performed are: 1. Roll-up on date from date id to year. 2. Roll-up on spectator from spectator id to status. 3. Roll-up on location from location id to location name. 4. Roll-up on the game from game id to all. 5. Dice with status= “students”, location name= “GM Place”, and year=2004
Read more

Explain network topology .Explain tis types with its advantages and disadvantges.

-
Network topology Network topology refers to how the nodes and links in a network are arranged. A network node is a device that can send, receive, store, or forward data. A network link connects nodes and may be either cabled or wireless links. Understanding topology types provides the basis for building a successful network. There are a number of topologies but the most common are bus, ring, star, and mesh: A bus network topology is when every network node is directly connected to a main cable. In a ring topology, nodes are connected in a loop, so each device has exactly two neighbors. Adjacent pairs are connected directly; non-adjacent pairs are connected indirectly through multiple nodes. In a star network topology, all nodes are connected to a single, central hub and each node is indirectly connected through that hub.  A mesh topology is defined by overlapping connections between nodes. You can create a full mesh topology, where every node in the network is connected to every other
Read more