Snapshots Vs Replication Vs Backups

September 11, 2021

Have you been told that oh you don't need any backups or replication because snapshots will fulfil all your data protection needs?

So in this article, we will share with you what's the difference between snapshots, replications and backups. And just maybe you may want to reconsider your DR strategy. Snapshot is all the backup you ever need is often something only storage vendors will tell you. More often than not these are specific vendors that don't have a complete set of data protection strategies or solutions.

Don't get us wrong. We are not saying that snapshots are bad. Snapshots have their place in the chain of data protection but it is certainly not backup neither does it replace the use case of replication. Having said that your data protection needs to differ from the next person so there may be a possibility that snapshots are all that you need but oftentimes most enterprises will have a combination of the three data protection capabilities and technologies.

So let's go through the three data protection methods, a little of how it works and what use case fits best. Snapshots are also known as the point in time copies. Point in Time copies by definition is the viewpoint of the data at the point where the snapshot is triggered. Snapshots are by far the fastest and most efficient data protection method to protect data. Sometimes in certain systems, it is almost instantaneous.

So let's look at how it works. You would have the master copy and as you write more and more data, and when you initiate a snapshot what tends to happen is they just put like a little bookmark or marker. Every time you write subsequent new data on it, there's a journal happening that tracks all these changes. When more new data is added and the next time you trigger a snapshot another journal happens. The longer you take the snapshots, the journal becomes larger and larger and will impact performance.

So why do we not like snapshots or rather why snapshots are not backup is because there is interdependency between all these snapshots and what you want to recover. Say for example you want to recover a point in time data, it is actually a combination of other snapshots and the master copy. So assuming any of the components is corrupted or destroyed, you literally don't have anything else to recover. Say for example if the master copy is dead or corrupt or whatever it may be, you are not able to restore any of these two other copies as well. Also a lot of times snapshots in most storage subsystems live on the same storage. You would have your master here and all your snapshots on top of it.

This is actually not the best practice in general because failure in the volume or the storage simply means all your backups with it will fail. It's a bit like all eggs in the same basket. Having said that snapshots because it's so fast and so powerful and it's just doing reference and pointers it is great for recovery purposes. Snapshots are great if you only need to recover and retain backups for just a couple of days and also it's highly dependent on how often do you take it. This is because the longer you keep snapshots the more resources it uses to keep the journals and all the blocks that are changed.

Many vendors have unique implementations to help alleviate this issue but still really is just delaying the inevitable. The limitations still exist. Now let's look at replication. As the name suggests replication simply means copying or replicating data to another storage. It can be on another system in the same data center but often it is remote to protect against DC failures as well. There are generally two types of replications - asynchronous and synchronous replications. Let's start with async. Async replications often mean that data is replicated at a given interval, perhaps every five minutes, changes are then replicated to the remote site so in the event of a disaster the worst that can happen, you will lose up to five minutes of data and it's often articulated as what we call recovery point objective or RPO equals five minutes.

Sync replications on the other hand replicate all IO as it is written to the storage system. It will commit both local and remote writes before and acknowledging to the host that the write is good. In many cases, mission-critical apps that cannot tolerate any loss of data would often opt for sync replications. Similarly in terms of RPO sync replications is what we term RPO equals zero, which simply means no data loss.

So why would anybody pick Async replications then? As you can tell sync replications demand on bandwidth will be extremely high and latency-sensitive comparatively, async often time have generous allowances of bandwidth and latency making it significantly cheaper. The advantage of replication is in its ability to recover very quickly with minimal data loss in the event of a complete data center failure or primary storage is completely lost. You often time have already a copy of the data and you're ready to resume business. Having said that it is not without its caveats because every data block written is replicated, that simply means if you have a corrupted block that is written or somebody accidentally or maliciously deleted a whole bunch of data. All this will also be replicated like the saying Dirty block in, Dirty block out! This makes it great for business continuity protections and insulation against primary storage failures but surely not so great if you want the ability to roll back to any point in time which brings me to my very last item.

Backups have been around pretty much since the beginning of time. Over time it's evolved to resemble a little like a combination of snapshots and replication. You make a full copy of the primary data every time you run the back up. Which for most organizations it's once a day and assuming you run at 8:00 p.m. you get a point in time replicas of the data exactly as how it looks like at the end. Similar to how a snapshot will be, assuming you do it seven days a week you will now have seven independent copies of data for 8:00 p.m. for the last seven days. Assuming the third copy is corrupted you often still have the second or fourth copy to recover, unlike snapshots or replications. Backups are also perfect for long-term retention because as long as there's capacity and resources you can store it for as long as you want.

You may be thinking the consumption of storage for backup then be massive and surely that's an issue. Yes of course, but there are many capabilities out there like dedupe and compression that will help with that problem and I mean today I will not go into depth with regards to that but the biggest issue with backups is generally time. It takes the longest to protect and also takes the longest to recover without going into details on the advanced backup and recovery capabilities, incremental forever and dedupe appliances which have improved recovery performance over the years. Regardless it is still the slowest of the three technologies we spoke about today. So depending on your needs and requirement you may only need one of the three data protection methods or a combination of all three.

If I will summarize my recommendations for the most cost-effective and fundamental form of data protection for every enterprise. Backup is a must! I cannot stress enough about backups. You need to have backups. For short term data protection between three to five days snapshots is the way to go but optionally I will still recommend backups. And for fast recovery, snapshots and replications is the way to go. Mission-critical applications definitely will be requiring replication with backups. Hopefully that has been useful. I know it seems like a lot for people that are new in the data protection domain and they may all sound the same in some sense. There are subtle differences between all of them.

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November 4, 2024

A Complete Guide To Customer Acquisition For Startups

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.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

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.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

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

Define what your goals are

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 –

MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.
CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
Churn rate – It tells you the rate at which customers stop doing business.

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.

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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

This is a decorative image for: Constructing 3D objects through Deep Learning

October 18, 2022

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

Highly calibrated cameras that take a photograph of the image from various angles.
Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

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

The technology used for this purpose needs to stick to the following parameters:

Input

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.

Practical applications and use cases

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.

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Reference Links

https://tongtianta.site/paper/68922

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

This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts

October 18, 2022

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:

Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
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.
Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.

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.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

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

October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
GAUDI also removes GANs pathologies like mode collapse and improved GAN.
GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
The scenes are thus synthesized by interpolation within the hidden space.
The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

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Reference Links

https://www.researchgate.net/publication/362323995_GAUDI_A_Neural_Architect_for_Immersive_3D_Scene_Generation

https://www.technology.org/2022/07/31/gaudi-a-neural-architect-for-immersive-3d-scene-generation/

https://www.patentlyapple.com/2022/08/apple-has-unveiled-gaudi-a-neural-architect-for-immersive-3d-scene-generation.html