Strategies for Database Management

It is witnessed that in this world of innovation and technologies, database management is continuously being reshaped by various new and improved practices that have altered the previous landscape of the domain. This is elucidated by the change in acquisition of data by techniques such as automation, Al, and cloud computing.

 

As a consequence of this, there are a plethora of new and innovative challenges as well as opportunities in the path of different database professionals around the world. In the contemporary era, the teams designed for database management are actually tasked with managing a larger databases with bigger and more variety, on both cloud and premises as well.

 

Simultaneously, the corporations in the world are now striving to achieve greater effectiveness and efficacy, as well as flexibility and scalability in order to support their new generation applications and to assist the derivation of the overall digital transformation.

 

Recently, there was a webinar that took place which was held with the senior product marketing manager of Couchbase, Tyler Mitchell. Other joining in were the director of product marketing at Delphix, Dhiraj Sehgal. The meeting became the center of attention because these personalities then discussed the different strategies that can be used today by various database professionals around the world.

 

Furthermore, across all the corporations in the world, different systems are actually fueling the interactions and becoming more interactive than ever before. Corporations are providing people with various services such as E- Commerce, Internet of Things, and Supply Chain.

 

However, with such facilities, there are also some mammoth challenges which are architectural in nature that include the scale fail, database sprawl, and the multi-cloud manageability as well.

 

Different solutions have been recommended by corporations. For instance, the Couchbase database recommends a solution that has the potential to offer strategic availability, containerized databases, and integrate the workloads.

 

The corporations who are considered to be successful actually have a very rapid speed of innovation and the previous techniques to focus on data actually contained a melange of manual processes and systems. The recent and innovative technique is to create a comprehensive DataOps platform which is used to streamline the various operations and unlock the overall cloud value.

 

 

According to the product manager, corporations such as Delphix transform hybrid applications through:

  • Offering non-disruptive, efficient data migration
  • Provision of data environments conducive to space efficiency.
  • Allowing the hybrid cloud dev/test with the on-demand data
  • Masking cloud data
  • Offering very convenient portability across the clouds

 

Impact of Big Data, Data Warehouse, and Internet of Things on Insurance Companies.

It is witnessed that over the past few years, the Internet of Things (IoT), have increasingly changed the models of business in a plethora of different ways, affecting many sectors and industries in the world. Currently, it is witnessed that the insurance companies are also largely getting affected as the rate-making procedures and business models largely driven by IoT have induced a new paradigm shift in the sector. For instance, now the primary drivers are real-time scoring and telematic approaches such as:

  • Auto insurance Telematics: the telemetry data can be utilized for claim reduction incentives, optimized tariffs, and measuring the insurance premiums for the risk of damage to the person or individual. The ‘Pay-how-you-drive’ tariffs is a great example.
  • Health insurance Telematics: Life-style and data related to health is utilized for the measuring of plans related to health care.

It is important to note that storing the raw data produced with the contract data and structured party is not always a useful thing to do. It is more useful and effective to consider Hadoop which is a more appropriate option available. Furthermore, many consider this to be a cost-effective avenue as well. On the contrary, it is also witnessed that these large data structures are not still accustomed to handling and processing the intricate and complex relational data structures with efficacy. Hence, we can say that it is more likely that large data structures such as Hadoop and others such as Oracle which is a relational data store, would be used simultaneously in parallel by the IT sector of the Insurance Corporations.

However, it is important that we first built a strong connection between these data systems. Firstly, the data can be a combination taken from the data warehouse at the analytical data marts level, which can include the linking of streaming data to a contract number or a customer from the data warehouse. But, assuming that the IT department of an insurance corporation processes the IoT data, we can say that a scenario can be structured in which the analytics results of the big data are generated or handed over by an external service provider to the insurance corporation. In any situation, the overall problem will be to connect the analytical results with the information in the data warehouse.

Hence, we are now witnessing that many insurance corporations are starting to develop an innovative and new system of automotive tariffs. In simple words, the corporations are now rewarding the defensive driving by giving off favorable premiums. All of this is done through IoT, which is through the analyzation from a telematics box which can be installed in the vehicle and sends anonymous info on the behavior of driving by the driver.

Learn theme Development in Magento- Part 1

Magento is a famous platform that is specifically used for the purpose of e-commerce. There are a plethora of dimensions to this comprehensive platform and in the first part of this blog-guide to Magento, we will be discussing the theming process in the platform.

When we endeavor to develop a website, there aren’t too many guides available out there and hence the primary resource that i have considered is the Magento 2Â dev.docs which is considered to be very useful. The proof of its usefulness lies in its comprehensiveness as it incorporates almost everything a user requires. As there is a lot to cover, this blog will be divided into two parts.

Pre requisites

  • Prior experience of Magento coding.
  • Know how of Magento 2
  • Completely Installed Magneto 2, running smoothly, and accessible to the frontend &Â admin

Installing Magento

Installation of Magento is the first step and it can be tough task if you have not installed it before, but there some good resources out there that are recommended to especially the beginners such as:

Beginner’s guide to installing Magento 2 using Composer

Magento 2 Vagrant

Magento 2 developer documentation

Creating a good theme

After the process of installation is completed, we would now move on to creation of a good theme to attract online traffic. In Magento 2, as similar to Magento 1, the themes are stored in ~/app/design/frontend.  which is inside of the front end directory. For this purpose, you will have to create a Vendor Directory, similar to the Package in Magento 1. Then a theme folder has to be developed within the vendor directory with the name of your choice.

Now you have a directory structure as below:

  • ~/app/design/frontend
    •  /<Vendor_Name>
      • /<Theme>

Now, you need to declare your theme as your structure is in place so that Magento knows of its existence and hence you can apply it as an active theme in the admin.

After this, you have to create theme.xml file on the root in the previously created theme folder. The code can also be used inside the Luma and Blank theme folders as well. Now, the name of the theme needs to be inserted in the <title> tags and the parent theme can be specified for fallback purposes.

1234<theme xmlns:xsi="<a class="vglnk" href="http://www.w3.org/2001/XMLSchema-instance" rel="nofollow"><span>http</span><span>://</span><span>www</span><span>.</span><span>w3</span><span>.</span><span>org</span><span>/</span><span>2001</span><span>/</span><span>XMLSchema</span><span>-</span><span>instance</span></a>" xsi:noNamespaceSchemaLocation="urn:magento:framework:Config/etc/theme.xsd"><title>INSERT THEME NAME</title><parent>Magento/blank</parent></theme>

This code is the minimum magnitude you’ll need and you can declare a theme image too. It will be exhibited on your theme page, in the admin so that you can see a preview of the theme. Use the code given below in order to add one of these. The code must be used between the <theme> XML nodes and underneath the declaration of <parent> theme.

123<media><preview_image>media/theme-screenshot.jpg</preview_image></media>

Thumbnail image name should be changed to that of your filename and image should be placed in the following location:

  • ~/app/design/fronted/<Vendor_Name>/<Theme>
    • /media
      • /theme-screenshot.jpg

It is important to note that if this file is not in the current location when  theme page is visited in the admin, then an error is likely to occur. Hence, make sure your image is placed perfectly.

Registration.php

Lastly, you have to add a registration.php file to the root of your theme in order to declare your theme.

  • ~/app/design/frontend/<Vendor_Name>/<Theme>
    • /registration.php

Paste the following code given below into the file, then edit the Vendor Name and Theme to match the structure the structure of your theme?

01020304050607080910<?php/*** Copyright © 2015 Magento. All rights reserved.* See COPYING.txt for license details.*/\Magento\Framework\Component\ComponentRegistrar::register(\Magento\Framework\Component\ComponentRegistrar::THEME,'frontend/<Vendor_Name>/<Theme>',__DIR__);

Composer

An interesting aspect of Magento is that the theme are distributed as various composer packages and hence include a composer.json file. It is optional to create one of these files and its not included in the demo but you can add such files. Just simply copy the file from Luma theme or Magento Blank and then edit it according to your need.

Structure of Directory

Now, at this point the overall registering and declaration is complete and you now only need to create a directory structure which is in the preparation of your  layout and template files. Your theme directory should appear as follows.

  • ~/app/design/frontend/<Vendor_Name>/<Theme>
    • /theme.xml
    • /registration.php
    • /composer.json
    • /media
      • /theme-screenshot.jpg
    • /web
      • /css
        • /source
      • /fonts
      • /images
      • /js
    • /etc
      • /view.xml
    • /Magento_Theme
      • /layout
        • /default.xml

The web folder contains your theme’s images css, fonts, and js will go. Also, as the Magento 2 doesn’t have a skin folder therefore these files go in here.

The Magento Catalog image sizes can be configured from etc/view.xml file by just copying it from one of the default themes and editing it accordingly.

In addition, you can also add a logo and declare it before activating your theme. Then the image folder will be used to store the image file and this can be file type you prefer. In this case, the file type is svg; and hence, to tell the theme to utilize the logo, you have to create the Magento_theme/layout folders and make sure that the following code is added to the default.xml file. You can edit this accordingly.

0102030405060708091011<page xmlns:xsi="<a class="vglnk" href="http://www.w3.org/2001/XMLSchema-instance" rel="nofollow"><span>http</span><span>://</span><span>www</span><span>.</span><span>w3</span><span>.</span><span>org</span><span>/</span><span>2001</span><span>/</span><span>XMLSchema</span><span>-</span><span>instance</span></a>" xsi:noNamespaceSchemaLocation="urn:magento:framework:View/Layout/etc/page_configuration.xsd"><body><referenceBlock name="logo"><arguments><argument name="logo_file" xsi:type="string">images/logo.svg</argument><argument name="logo_img_width" xsi:type="number">300</argument><argument name="logo_img_height" xsi:type="number">300</argument></arguments></referenceBlock></body></page>

Activate your theme

Once everything is complete, you have to browse to the admin of your Magento 2 store, where you will go to the Content > Design > Themes. You have to make sure that your theme appears in this particular list. In the case if it doesn’t appear, the theme has not been declared correctly.

After you correctly manage to declare and see your theme in the path above, you have to browse to Stores > Configuration > Design. Here, you have to select the right store scope with changing of the theme to your own designed theme.

Concluding Part 1

This brings our Part 1 of Theme creation to an end. It is assumed that you would now have a working theme which is created and configured by yourself. In the Part 2, we will be covering the following:

  • Styling
  • Layout changes
  • Editing & Overriding templates

Let us know if there are any comments, queries or questions.

The Must-Have Skills to Excel in Machine Learning

Machine learning is an intricate field in the industry and working on it requires a strong skill set which incorporates knowledge, stats, and practical skills in engineering. For instance, if a user faces a problem in finding products and services, then it is your job to understand the problem on the website and apply statistical modeling in order to solve the particular problem.

Hence, we can say that the success in machine learning depends upon the acquisition of a strong skill set. If you have a major in Computer Sciences, the overall skills required would be to fathom the statistical fundamentals, gain a know how of the algorithms of machine learning, and lastly to establish proficiency in working with data. This includes the data querying from a plethora of sources, its manipulation, and reasoning.

If you have a background of a statistical or quantitative nature, your priority must be your programming skills. It is important for you to become proficient in at least one DS language such as R or Python as it will greatly assist in the execution on any modeling tasks in Machine learning. It is extremely beneficial for you to adopt and understand the effective software engineering practices such as handling of data flows through data systems and automating the workflows which are commonly used such as libraries.

And lastly, the most important and crucial skill for machine learning in the market is to apply the skills in order to solve the business problems by casting the business problem as a machine learning problem. in this way, the machine learning will be effective and productive. For example, machine learning can be utilized to improve the overall delivery-time prediction which have a massive impact on the overall client experience.

Top 3 Data Mining Techniques

Different Data Mining techniques are used for different problems in corporations and hence, we can say that the overall knowledge of the business problem helps in determining the technique for data mining that is likely to generate the best results for the corporation.

In the contemporary world of globalization and digitization, we are largely surrounded by mammoth sets of Big Data which is expected to increase 40% every year for the next decade. The irony of the matter is that despite of such a large magnitude of data availability, the world still starves for beneficial knowledge. Why is this the case? It is important to understand that the data is buried inside large chunks of big data and can only be extracted through significant techniques.

In this small piece of literature, we shall provide you with the 3 major techniques that are crucial for data mining.

CLASSIFICATION ANALYSIS

In order to retrieve relevant, productive, and beneficial information about data or Meta data, this analysis comes very handy. The classification analysis is used for the classification of different data into classes or segments. This process is similar to clustering as the process of segmentation is carried out in both the techniques, but the uniqueness of Classification analysis is that, unlike clustering, the analysts would be having the overall knowledge and understanding of the different classes or segments. Hence, we can say that in classification analysis different algorithms are applied in order to decide how the classification of new data should be done. For example, the Microsoft Outlook uses a particular algorithm to segment an email as Spam or Legitimate email.

REGRESSION ANALYSIS

The Regression Analysis, in the statistical terms, refers to the process of analyzing as well as identifying the connection among different variables. This analysis is important because it helps you to gain a sound understanding of the characteristic value of the changes in dependent variable, if the independent variable changes at any time.

CLUSTERING ANALYSIS

The clustering analysis is another important technique that uses clusters comprising of objects which are similar in nature. This means that with in the same groups the present objects are similar to each other and different from the ones in the other groups or the so-called clusters. Therefore, we can say that the clustering analysis is the systemic process through which we can explore clusters in the Big Data, in a manner that the overall degree of association between the two sets of data is the highest if they are from the same group and lowest otherwise. For instance, this Clustering Analysis is used in Customer Profiling.

Learn the Difference Between Data Mining and Big Data

Data mining and Big Data are considered to be two different things but both are crucially important to understand in the realm of data analytics. Although both of these terms relate to the handling of large magnitudes of data for different recipients, but they are actually used in different context and for two different elements for this type of operations.

 

The term “Big Data” refers to the large sets of data that outgrow the databases which are simple in nature, and which were used in the times when technological advancement was a thing of the future and people used a less feasible and more expensive methodology or data handling architecture. For instance, the term “Big Data” can be used to address the large magnitude of data which is not easily handled in Microsoft Excel spreadsheet. Hence, it will be referred to as Big Data.

 

On the other hand, Data Mining refers to the process of analyzing and thoroughly looking through sets of “Big Data” in order to search for pertinent or important information. Putting the entire operation in simple words, we can say that the operation is similar to the phrase of “looking for a needle in the haystack”. The notion behind this is that the decision-makers in large corporations require access to more specific and smaller sets of data which have to extracted from the homogeneous large sets of “Big Data”. Therefore, “Data Mining” is used as a technique to elucidate the information which can assist businesses in chartering direction for their business.

 

Furthermore, different software packages including analytic tools can also be used in Data Mining, but generally the process of Data Mining include operations with intricate search operations which return results which are specific in nature. For instance, a tool used for Data Mining would look through years of accounting data in order to locate and provide a particular column of accounts needed by the user. Thus, we can simply say that Big Data is the primary asset, while Data Mining can be considered as the handler of this asset.