Moving With SQL Server to Linux? Why Not Also Move From SQL Server to MySQL?

March 16, 2022

For the longest time, the SQL Server existed with a codebase that is all Microsoft and Windows. However, in 2016, Scott Guthrie, Microsoft's Executive Vice President for Enterprise and Cloud, published a blog post announcing that Microsoft's flagship product SQL Server will now be compatible with the Linux operating system (OS).

That was a pretty significant undertaking by Microsoft and also a necessary move as expressed by many experts in their blogs. SQL on Linux has been the most successful server product of Microsoft in terms of downloads.

SQL Server on Linux—Why did Microsoft Have to Do It?

Making the SQL Server compatible with Linux was necessary for Microsoft for two reasons: Cloud and Relevance.

An orthodox Windows-only approach for Cloud makes no sense as Linux is already the go-to OS choice in the cloud space for developers, enterprises, and corporations. With products like SQL on Linux, Microsoft is enhancing its credibility in the Big Data world.

It is not difficult to guess why developers or businesses love Linux. Reduced licensing fees of Linux compared to Microsoft is a reason strong enough. Moreover, using the Microsoft platform means being driven by a single vendor. The majority of companies, tech startups, and many influencing enterprises, consider that a Windows-only product is less strategic, less flexible, and less customizable; and, of course, developers are fond of Linux-based stack for leveraging the benefits of open-source resources.

Why Would a Business Move with SQL Server to Linux?

There are enough businesses out there, especially SaaS providers, who use the Windows platform to run Microsoft SQL Server, while the rest of their production and development environments run on Linux OS. It gets quite expensive and time-consuming to run processes in two unique and different environments. Businesses do feel the need of letting one go.

SQL Server on Linux has provided businesses the middle way out. Moving with SQL Server to Linux allows companies to save costs on platform software, and at the same time, they can continue using the SQL database that they are familiar with, rather than learning a new Linux-compatible database like Oracle.

But, is it necessary to use a commercial paid database? Yes, it is; at least till one uses platforms like Windows. However, once you decide to switch to the Linux platform, then why use SQL? Because over Linux, you are free to use open-source RDBMS such as MySQL.

Benefits of Migrating to an Open-source database like MySQL

Moving to the Linux platform from a Windows-only environment provides the flexibility to explore the range of open-source alternatives. Therefore, why only save on platform software by switching to Linux with SQL Server; and why not go a step ahead and switch to Linux with MYSQL Server and save on databases as well?

Listed Below Are Some of the Benefits of Migrating To MySQL Server

  1. It cuts down on licensing costs. Even the paid MySQL enterprise versions are way less expensive. However, there are enough completely open-source license versions of MySQL available at no cost.
  2. MySQL compatibility with programming languages and OS is greater than that of SQL. Not just Linux, it supports every major OS. And along with the .Net language, it additionally supports languages such as Perl, Tcl, Scheme, Haskel, and Eiffel.
  3. One of the biggest benefits is that MySQL isn't bound to a single vendor’s software, thus it is a lot easier to change or scale its environment to adapt to different workloads.
  4. MySQL is open-source, so every aspect of it, including advanced features, gets tested intensively by the huge tech-savvy open-source community.
  5. One major advantage of MySQL over SQL server is that it supports many storage engines—MyISAM and InnoDB are the two popular ones. But, Microsoft SQL is tied to a single storage engine developed by Microsoft. By supporting multiple storage engines, MySQL gives the developers the flexibility to use different engines for different tables. It also provides the option to use plug-in storage engines.

The Final Thoughts

The potential to drive revenue and security are two aspects that businesses tend to consider before choosing open-source products. However, the move “SQL on Linux” by Microsoft can be considered as an acknowledgment toward open-source. Isn't it a strong undertaking to put off the debate over stability, functionality, or security related to open-source? Moreover, over the years, open-source has become the favorite of not just the hackers but also the computer-savvy developers, corporations, and governments.

As far as performance and scalability of MySQL server are concerned, leading sites such as Google, Facebook, and Yahoo! are using MySQL for their large-scale applications and huge workloads. Not just in web-centric platforms, MySQL is also in adoption by enterprises and government agencies such as F5, NASA, Scholastic, and Telenor.

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Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

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As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

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How can you create the ideal customer acquisition strategy for your business?

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You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

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All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

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By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

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Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

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Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

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A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
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In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

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Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

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