Top Cloud Big Data Technologies Working Work From Home

Cloud Computing Big Data has a complementary relationship. Processing and processing of big data requires cloud computing as the platform.

The values and laws encompassed by big data can make cloud computing better integrated with industrial applications and play a bigger role.

This prompted the Software Engineering study program (RPL) at BSI University (Bina Sarana Informatika) to hold a webinar with the theme ‘Become a Reliable Software Developer by Utilizing Big Data and Cloud Computing Technology’,

Agus explained that, big data is not yet understood by many people, but the benefits of big data have been felt by many internet users who frequently access information. Big data can be interpreted as a collection of data that is very large, complex and always increases over time.

The Difference Between Big Data & Cloud Computing

Before discussing how the two go together, it’s important to form a clear distinction between “Big Data” and “Cloud Computing”. Although they are technically different terms, they’re often seen together in literature because they interact synergistically with one another.

  • Big Data: This simply refers to the very large sets of data that are output by a variety of programs. It can refer to any of a large variety of types of data, and the data sets are usually far too large to peruse or query on a regular computer.
  • Cloud Computing: This refers to the processing of anything, including Big Data Analytics, on the “cloud”. The “cloud” is just a set of high-powered servers from one of many providers. They can often view and query large data sets much more quickly than a standard computer could.

Essentially, “Big Data” refers to the large sets of data collected, while “Cloud Computing” refers to the mechanism that remotely takes this data in and performs any operations specified on that data.

“This data is generated from our activities on the internet. For example, personal information such as name, address, telephone number, date of birth is simple.

However, as we use social media, marketplaces, and search engines that we use every day, we can generate around 2.5 quintillion bytes of data every day.

The Need for Big Data Is Much Needed In The Digital Era

Big data is divided into two categories, namely operational big data technologies and analytical big data technologies. First, operational big data technologies are useful for processing data generated from daily activities, online transactions, so that it is used as input for the next category.

“Meanwhile, analytical big data technologies are more complex, because they process incoming data to get valuable output. Examples of operational big data are online tickets, online shopping such as harbolnas, warehouse clearance.

While examples of analytical big data are stock marketing, crypto, weather forecasts, NASA or SpaceX data,” he added.

Meanwhile, Ahmad Setiadi, as the head of the BSI University RPL study program, responded that the concept of big data can add insight to students to manage all the data generated and process it in an appropriate way. So that it can provide value.

“Through this webinar on the use of big data and cloud computing technology, students are invited to understand the concept of big data and cloud computing which can help them have insight and skills to become software developers,”

Big Data Technology and Cloud Computing courses introduce technologies that support the management and utilization of big data, both for operational and analytical needs.

In addition to providing an introduction to big data technology that can be owned and managed by an organization (installed in an organization’s data center), this course also introduces big data technologies provided by cloud-based service providers.

After Graduating From This Course Students Sre Expected

understand the technologies in the Hadoop ecosystem and their utilization
understand and be able to utilize big data technologies provided by cloud-based services, namely Google Cloud Platform.

The learning material covers introduction along with exploratory practice about:

  • Big data, characteristics of big data, examples of using big data
  • Hadoop ecosystem and Hadoop cluster architecture
  • Parallel computing with Hadoop MapReduce and examples of its use
  • Apache Hive, data warehouse technology for big data analysis with SQL queries in the Hadoop ecosystem
  • NoSQL database technology, Apache HBase in the Hadoop ecosystem
  • Apache Sqoop and Flume
  • Cloud computing and types of cloud services
  • Google Cloud Platform (GCP) and the products provided by GCP (especially storage products, big data products, machine learning platforms).
  • BigQuery data warehouse to analyze Big Data with SQL queries
  • BigQuery ML for building and exploiting machine learning models with SQL queries
  • Various kinds of Machine Learning API provided by Google Cloud Platform
  • Big data technologies in the Hadoop ecosystem are available on cloud-based services on the Google Cloud Platform
  • Teaching method Even semester 20/21:

Lecturers apply interactive learning by utilizing Google Classroom and Google Meet. Students can access lecture materials (slides and other material files) on Google Classroom before the lecture meeting schedule. Lecturers provide explanations of lecture material via Google Meet.

Students carry out assignments in the form of exploring big data technologies that have been explained at lecture meetings by accessing and practicing in the Hadoop cluster and its ecosystem in the UNPAR Big Data Informatics Lab.

The Roles & Relationship Between Big Data & Cloud Computing

Cloud Computing providers often utilize a “software as a service” model to allow customers to easily process data. Typically, a console that can take in specialized commands and parameters is available, but everything can also be done from the site’s user interface.

Some products that are usually part of this package include database management systems, cloud-based virtual machines and containers, identity management systems, machine learning capabilities, and more.

In turn, Big Data is often generated by large, network-based systems. It can be in either a standard or non-standard format. If the data is in a non-standard format, artificial intelligence from the Cloud Computing provider may be used in addition to machine learning to standardize the data.

From there, the data can be harnessed through the Cloud Computing platform and utilized in a variety of ways. For example, it can be searched, edited, and used for future insights.

This cloud infrastructure allows for real-time processing of Big Data. It can take huge “blasts” of data from intensive systems and interpret it in real-time.

Another common relationship between Big Data and Cloud Computing is that the power of the cloud allows Big Data analytics to occur in a fraction of the time it used to.

Big Data & Cloud Computing: A Perfect Match

As you can see, there are infinite possibilities when we combine Big Data and Cloud Computing! If we simply had Big Data alone, we would have huge data sets that have a huge amount of potential value just sitting there. Using our computers to analyze them would be either impossible or impractical due to the amount of time it would take.

However, Cloud Computing allows us to use state-of-the-art infrastructure and only pay for the time and power that we use! Cloud application development is also fueled by Big Data.

Without Big Data, there would be far fewer cloud-based applications, since there wouldn’t be any real necessity for them. Remember, Big Data is often collected by cloud-based applications, as well!

In short, Cloud Computing services largely exist because of Big Data. Likewise, the only reason that we collect Big Data is because we have services that are capable of taking it in and deciphering it, often in a matter of seconds. The two are a perfect match, since neither would exist without the other!

In addition, practical assignments will also be given by accessing cloud services on the Google Cloud Platform. Assignment reports are uploaded to a class in Google Classroom.

The Mid Semester and Final Semester Examinations are conducted online using Google Classroom.

As a complement, students are also encouraged to take free online lecture materials from IBM Cognitive Class and Coursera.

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