Introduction
This research mainly depends on big data processing. The researcher will take three software platforms to do this work properly. The researcher will use big Query, Azure, and red hat open shift as software. These three platforms are used here. The platform investigation section will be discussed here properly. Big Data processing and analysis implementation will be discussed in this report. The evaluation will be done with the help of a “big data cloud platform”.
The researcher will use the quantitative method for this research. Primary data analysis will be done here with the help eof software platforms. All software will be done by the researcher. The dataset is provided before for this research analysis. The cloud data warehouse also one of the most important part for big data analytics.
There are some use cases that will be evaluate for the analysis of cloud data warehouse. In this cloud data warehouse, there are some platforms that can helps to make sure about the best products. With the help of this cloud data warehouse, the performance of analysis also implemented at high scale which is necessary for encryption and decryption.
Platform investigation
This section is mainly about the software which is used here. The researcher uses three software platforms Big Query, Azure, and Red hat open shift (Habeeb et al. 2019). These platforms are very important and every platform has its own working system and also working strategy.
Big Query (Google)
This platform is very important for big data processing. This platform is an upgraded software system that is used by most researchers to complete their work. BigQuery is a completely managed “enterprise data warehouse” that helps to analyze and manage the “data with built-in features” like geospatial analysis, machine learning, and business intelligence. This platform is used to solve “SQL queries to answer” any organization’s tough questions with management of zero infrastructure (Amani et al. 2020). “BigQuery’s scalable” assigned analysis engine lets researchers query petabytes in minutes and terabytes in seconds. The flexibility of this BigQuery platform is very good and maximizes flexibility by changing the engine of the computer that analyzes researcher data from the researcher’s storage choices. BigQuery platform has its own powerful tools like BI Engine and BigQuery ML let the researcher understand and analyze that data. BigQuery interfaces contain “BigQuery command-line tool” and “Google Cloud console interface” (Wang et al. 2020). It has various kinds of languages like java, python, Go, and javascript. BigQuery also works like a storage system that uses a format of columnar storage that is enhanced for analytical queries. The data of BigQuery presents in rows, tables, and columns. BigQuery is the high level of tables and views containers (Radchenko et al. 2019). Prescriptive analysis and descriptive uses contain ad hoc analysis, machine learning, geospatial analytics, and business intelligence. BigQuery delivers compacted data management and “compute resource” while access management and identity management.
Azure (Microsoft)
This software is very important to process any kind of data. This software was invented by Microsoft. Azure is called “Microsoft’s public cloud platform”. Azure provides a huge collection of services (Han et al. 2019). This platform has included “infrastructure as a service” and “platform as a service” and capabilities of “managed database service”. Microsoft invented the name Azure by developing a lexicon. The name of “Microsoft’s public cloud platform” is Azure (ur et al. 2019). This software platform is an easy platform that looks like a hardware system. This cloud is a physical set of servers in more data centers. These data centers implement virtualized hardware for consumers. Various types of steps are presented to handle the cloud system like creating a cloud, stop, starting, and deleting the system. This Azure software is good for beginners to access all types of data systems. This service platform can be easily adopted by beginners.
Red Hat OpenShift
This “Red Hat OpenShift” simplifies the management and development of a hybrid infrastructure. This platform provides the flexibility to have a completely managed service, moving in hybrid and cloud environments (Sahal et al. 2020). The “Red Hat OpenShift” is called “open source software”. “Red Hat OpenShift” is a “cloud-based Kubernetes platform” that supports developers in creating applications. This platform offers upgrades, management of the life cycle, and automated installation throughout the stack container (Wang et al. 2022). Examples of stack containers are cluster services, Kubernetes, and operating systems. OpenShift is a “container orchestration platform” for handling clusters provided as a “Platform as a Service”.
BigQuery vs azure data warehouse
| BigQuery | azure data warehouse |
| BigQuery depends on “encryption by default”. | Azure data does not depend on the “encryption by default”.
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| BigQuery is better than Azure data. | Azure is the nominal platform. |
| The performance data strategy is very high for this BigQuery data. | The performance data strategy is very low for this Azure data. |
| The data protection level is very high in the BigQuery platform. | Data protection level is not so good in the Azure platform. |
| Google BigQuery is called the “Google Cloud Platform” outcome that delivers cost-effective, serverless, “highly scalable data” and warehouse abilities created in features of Machine Learning. | The azure platform provides an “End-to-End Analytics Solution”. The parts are totally different from the others. |
| This google BigQuery platform is a faster process (Hajjaji et al. 2021). It takes minimum time for its petabytes to process. | This Azure platform is not a faster process. It takes some time but more than the BiogQuery platform. |
| The data exchange system is normal for this BigQuery platform. | Data exchange system is very good compared to the google BigQuery system. |
BigQuery vs Red Hat Openshift
| BigQuery | Red Hat Openshift |
| Generated by google from developed and scratch from Dremel. | Obeys the “relational nature of the PostgreSQL database”. |
| The web service is provided by BigQuery which reveals Dremel over an interface of rest.
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This redshift platform obeys columnar structure and BI applications. |
| This BigQuery supports “nested data” with the help of columnar structure.
|
This redshift obeys relational structure. |
| This BigQuery delivers the native solution for inserting streaming data.
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For loading streaming data it uses kinesis. |
| For the storage system, BigQuery uses storage of Google cloud.
|
For “Bulk data loading”, the redshift uses an S3 system. |
| This is very easy to do. | This is difficult to do. |
Big Data processing and analysis implementation
These three platforms are used by the researcher. The working condition of these platforms is very high. The “big data processing” system is perfect for the warehouse system. The SQL system is not used here by the researcher. Cloud platforms are used by the researcher for simulation purposes (Osman et al. 2019). The analysis contains various kinds of EDA data, price prediction, and classification. Some software analysis results are provided by the researcher below.
Figure 1: Installing pyspark
(Source: Self-created)
The figure displays the installed pyspark. The storage of downloading pyspark is 281.4 MB. The storage depends between 281.4 to 5 MB. The building wheel for pyspark is done in the software. The value of successfully installed py is 0, ten, nine, and five, and the value of pyspark is three, and three-point one (Alwasel et al. 2021). The downloading storage value is 199.7, 19 MB.
This is the code to successfully install pyspark. This code is made in google colab. In present it is easier for the user to use the cloud data warehouse. The easier process of cloud data warehouse also helps to manage the evaluation process and to replace the time, resources. The easier process of cloud data warehouse also helps to increase the performance at a high scale. It also enable the semi-structure data and reduce the data structure problems.
Figure 2: Importing libraries
(Source: Self-created)
The figure displays the importing libraries. This is the necessary import code. Pandas as pd, numpy as np all are the import commands which are mandatory for the results. The warning command is ignored by the researcher (Torabzadehkashi et al. 2019). The plt style is used as a command like FiveThirtyEight.
The percentage command is matplotlib inline. Plotly. Express as px is the import command which is provided by the researcher with the help of google colab. The cloud data warehouse is the cost efficient process that can helps to implements the various parameters which consist of speed, scales and usage. The cost efficiency of cloud data warehouse also consumed the cloud resources, enable the TB scanned.
The figure displays the importing dataset which is used in the software. The value of the sales territory key is one and the last one is two. The region of sales territory value is northwest. The group’s sales territory is North America. The country of sales territory is the United States. The year is taken from 2005 to 2008 the research (Liu et al. 2021).
The quote of sales amounts is 2693000, 2321000, 923000, and 4102000. In this table, five entries are presented. df. the head is the command of the dataset input. The supports structured and semi-structured also creates several opportunities in cloud data warehouse. In this supports structured and semi-structured data, most of the data are no longer arrives but enable the predictable and structured formats.
The figure displays the checking of null values with the help of the google colab platform. In this graph, eight bars are available which show the different components with proper values. Five bars have the highest value. Each and every bar has its own individual name and also individual value (Yang et al. 2019). The sales territory group, sales territory group, and sales territory region are the smaller graphs.
The concurrency also creates several opportunities for encryption and decryption. In this concurrency there are some necessary factors that can implement the queries. The implementation of the queries also helps to execute the programs in an appropriate way.
The figure displays the heatmap graph which is created by the google colab. Every color denotes the numeric values. The black color denotes -0.36, and the red color denotes 0.15, and the white color denotes one. The values of the sales territory key are -.36, 0.15, and 1, the values of the calendar year are 0.01, one, and 0.15, and the value of the sales amount quote is one, 0.01, and -0.36. The data granularity is defined as the easier way to performance constraints of aggregations. With the help of this data granularity, it helps to supports the high performance without heavy cost tradeoff.
The figure displays the Count Plot graph of the sales territory key. The X-axis value is zero, one, two, two point five, three, three points five, and four, and the Y-axis value is one, three, six, eight, ten, etc (Shilo et al. 2020). First six bars have highest values and then the values of the bars gradually decrease. After six no bars, the bar drops its value.
The deployment options have the ability to deploy the cloud data warehouse. In the case of this encryption and decryption, the deployment options also helps to enable the huge array services that can offer a multi cloud deployments.
The figure displays the Count plot graph of the sales territory region. The X-axis value is northwest, central, Canada, France, Germany, kingdom, etc, and the Y-axis value is one, three, six, eight, ten, etc. first six bars have the highest values and then the values of the bars gradually decrease. After six no bars, the bar drops its value.
The color denoting system is applied here with the help of a software command. Data freshness in the case of this encryption and decryptions also helps to require the real-time analytics and for the predictive maintenance. In the case of this encryption and decryption it is also used to supports the streaming along with high performance.
The figure displays the Count Plot graph of the sales territory group. The X-axis value is north America, Europe, and the pacific, and the Y-axis value is zero, ten, fifteen, twenty, etc. in this section, three bar graphs are presented with proper color detection. The graph gradually decreases its value. The value of North America’s graph is the highest, then the lowest value of the graph is pacific.
The figure displays the Count plot graph of the sales territory country. The X-axis value is the United States, France, Australia, etc, and the Y-axis value is 2.5, 5, 10, 12, etc. in this section, six graphs are presented with the proper value. The highest value is 20 for the United States, then Canada, France, etc.
The figure displays the Count plot graph of the calendar year. The X-axis value is 2005, 2006, 2007, and 2008, the Y-axis value is 2, 4, 5, etc. in this section, four graphs are presented with the proper value. The highest value is above ten for 2008, then 2007, 2006, etc. The graph gradually increases its values in the upward direction.
The figure displays the Count Plot graph of the date key. The Y-axis value is 2, 4, 5, etc. in this section, four graphs are presented with the proper value. The highest value bar is located at the middle position according to the date. The X-axis denotes the date key and Y-axis denotes the count section of this graph.
The figure displays the Count Plot graph of sales amount quote. The Y-axis value is 0.2, 0.6, 1, etc. in this section, various numbers of graphs are presented with the proper value. The X-axis denotes the sales amount Quote and Y-axis denotes the count section of this graph.
The figure displays the Pie chart between sales territory region, country, and group. This is a circle base graph and the values show like a percentage. The value of France is 8.82 percent, the value of Canada is 11.8 percent, the value of the United Kingdom is 8.82 percent, and the value of Australia is 5.88 percent. The countries are denoted with the help of different colors.
The figure displays the Line graph of the sales amount quote. The graph starts from the zero position and then goes upward again it goes in a downward direction. The graph formation is shown with the help of line. The X-axis denotes the sales amount Quote and Y-axis denotes the density section of this graph.
The figure displays the Box plot graph between date key and sales amount quote. The color formation is presented in this graph for better understanding.
The figure displays the Line graph between the sales territory region and sales territory key. The line started from the upper section and goes downward at that time one breakage is presented in the middle of the graph.
Conclusion
In this section, the researcher discussed this research report. The evaluation has been done with the help of a “big data cloud platform”. Primary data analysis has been done here with the help of software platforms. The platform investigation section has been discussed properly. Big Query, Red Hat Open shift, and Azure platforms have been discussed properly.
Cooperation between Big Query, Red Hat Open shift, and Azure platforms has been done perfectly with table format. Analysis results have been discussed properly with the help of proper figures. Checking null values, Heat map graph, Count Plot graph of sales territory key, and Count Plot graph of sales territory group have been described properly in this research report. Pie chart, bar plot graph, Line graph, Box plot graph, Histogram graph, and Scatter plot have been discussed here.
Reference List
Journals
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