Month: July 2022

Superset is a data visualization technology that is done with the help of a group of other components. It is a suitable environment for designing control panels and confirming customer membership through OAuth, OpenID or LDAP. Its characteristics are consistent with most data sources designated to work on SQL program, and its features work in full compatibility with Apache ECharts.

Many giant companies such as Netflix, Airbnb, Twitter, Airbnb, and Lyft rely on Superset technology primarily to analyze their products due to its use in MediaWiki.

An integrated set of programs specialized in dealing with big data, application programming interfaces, and the techniques necessary to develop them, which are free and open source.

This tool consists of four sections:

• YARN is a technology dedicated to handling data sets.
• HDFS is a categorized file system that is built to run on standard devices.
• A programming environment that enables other units to be compatible with HDFS.
• MapReduce is an algorithmic pattern used for parallel computation provided by Google.

There are many tools dedicated to big data analysis on different software providers such as Microsoft, IBM and Oracle, and they are widely used by analysts of this type of data

Especially the open source programs that the largest companies rely on to analyze their products. There are also free tools such as Apache Hadoop, which are classified from the free Apache environment.

In the upcoming articles, we will discuss the big data analysis tools, each separately

Recently, with the advancement of science and technology, there have been many questions about techniques for dealing with big data, through which we can predict customer behavior, control resources, expand sales, thwart emergency conditions that hinder the progress of any business, and control fraud, in addition to making the daily transactions of many people more flexible and easy.

The term “big data” was given to a database that contains several rows or random data related to a topic, or techniques that deal with many inquiries at the same time.

Several years ago, the discussion of big data was not so important that even some data science professionals did not have a sufficient understanding of how to deal with the exact structure of this type of data.

Big data in its concept does not embody the data itself :

The concept of big data is not limited to the data itself, but goes beyond it to strategies related to dealing with that data, with another prevention, which is to find an effective mechanism to process a random set of information related to the activity of any government agency or commercial company, regardless of the amount of that information through which technicians and specialists can find The best organizational methods for converting that information into useful data conducive to overcoming all obstacles to the smooth functioning of that activity.

Moreover, according to the new concept of big data, it is considered the best way to get rid of the traditional pattern of effective relationships and transactions in the development of machine learning techniques and its branches, so that big data technicians and specialists receive greater attention and support compared to programming specialists and data scientists in general. Dealing with this large amount of data Data of all kinds leads to accurate and effective analysis and leads to following the right strategies in investing time and effort at the lowest costs to serve commercial or industrial activity or both for major international companies.

We will deal as a living model plan of a particular company planning to carry out advertising campaigns on a large scale or a company planning to evaluate its sales movement. The best option to implement these strategies that fall under the name of business intelligence is the use of big data as a model solution to implement these projects more effectively by using more accurate and professional techniques provided by this Type of data analysis.

This is done to deal with big data through several steps, and data preparation is one of the most important basics of the analysis process, which consumes the most time from the total integrated system for data analysis.

data collection :

The data is collected as a first stage by special tools from multiple sources and then stored on a file in its basic position without making any change in the properties because any change or transformation of information loses some of its features and thus reduces the efficiency of the analysis.

data selection

To explain the concept of data selection, we turn, in an illustrative example, to a promotional plan that is presented to customers for SIM products to be sold before the start of the school season, based on the analyzes of sales movement in the previous year. Based on previous analyzes without neglecting to rely on forecasting according to the surrounding developments and variables.

Here comes the role of data analysts in identifying the subgroups of the common data set, which are relied upon to find the best way to produce good results.

Clean the raw data:

This step includes filtering and processing unstructured, unformatted, or error-containing data, eliminating duplicates, if any, and analyzing them to take the form of useful and required information.

Data Enhancement and Integration:

Data is supplemented from local data sources or various other data sources (databases or information systems) and their aggregation is included when calculating new values ​​such that a game company collects and analyzes documents produced by games to gain insights into usage behavior and customer preferences so that it can produce plans to enhance the likelihood of opportunities Selling by developing new features that drive the growth of their business forward.

Data Format :

Sometimes it may require doing data formatting without modifying its values, such as sorting data with specific numbering and encoding, shortening long terms, and removing unnecessary punctuation marks in text cells.

Activate the role of forecasters

At this stage, the derived features are built and directed to work on the machine learning technology so that they are employed to raise the efficiency of the higher education algorithm and then deal with it by the forecasters.

Create an analytic model:

Since the model is a method of seeing data, this requires creating an analytical model to predict the required variable. For example, we can say that sorting is the collection of items with similar characteristics into subgroups according to certain criteria.

At this point, for the sake of clarity, we can sort customer groups based on the behavior of their customers: sports interests, vegetarians, etc. through tools designed for this purpose (such as IBM SPSS) via the built-in databases.

In practice, models that include machine learning characteristics are used to transfer the current analyzes to the future for the purpose of comparing them with reality and other samples. .

In general, this type of analysis requires analysts to devise a different method to apply it to the data because there is a state of chaos in the organization of the data resulting from not organizing and coordinating it well. Therefore, block analysis and machine learning are related to the variables created by the existing situation, so they invent a new method by writing more effective software codes. Contributes to bug fixing and rectification of errors.

As a last step in this analysis, it is possible to build control panels and charts due to the presence of a small number of data capable of graphic representation.