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Architecture Simplicity and Performance Enhancement by Amazon Redshift

In the Awe of Cloud Data WarehouseCloud data warehouse is ascending towards a global and more economic acceptance, and AWS already has their horse in the race – Amazon Redshift. Global data players trust Redshift’s architecture to integrate their data engineering, cx automation, and business intelligence tools. In a time when Seagate already reports that barely more than 30% of our data can be put to work as of now, the cloud data warehouse is our best bet to change that forever and for good.Amazon Redshift is being used by organizations to assimilate data from multiple channels, including internal reports, marketing inputs, financial transactions, customer insights, partner interfaces, and much more, with astounding scalability. Moreover, it is reliable, durable, secure, and cost optimized.Therefore, this article will take a closer look into the architecture of Amazon Redshift and discuss the benefit that this architecture serves.The Inherited ArchitectureSince its conception, the data warehouse has been through multiple refurbishments. We’ve had models like a virtual data warehouse, data mart, and enterprise data warehouse – each serving its own complexity domain, including reporting, analysis, research, enterprise decision support, data visualization, and more. We’ve also had schemas like snowflake and star schemas that serve the organization based on data integrity and data utilization requirements. Moreover, there have been multiple data loading methods like ELT (Extract Load Transform) and ETL (Extract Transform Load) as per the structuring needs of the organization. However, Amazon Redshift, even after uplifting the storage infrastructure for the cloud, seems to have inherited the basic cluster-node architecture. Let’s have a brief revision before moving the discussion forward.The typical cloud data warehouse architecture for Amazon Redshift consists of the following elements:Clusters – This can be seen as the core enabler of the cloud data warehouse. The client tools and users essentially query and interact with the cluster through ODBC and JDBC connections. Clusters, as the name suggests, comprise a group of nodes which are computational units meant to process the data as per the client query demands.Leader Node – Each cluster typically has one node assigned for directly interacting with the client. The query is then divided and distributed among other nodes for further computation.Compute Nodes – The code compiled and assigned to these compute nodes is then processed and collected back for final aggregation to the result. Here are the primary parts of a compute node:Virtual CPURAMResizable Memory Partition SlicesA cluster’s storage capacity essentially depends on the number of nodes it is comprised of.How does this architecture help Amazon Redshift to meet the scalability and performance needs? That is the question we would explore in this article in its subsequent sections.Scalable Data QueryingThe Amazon Redshift clients are connected with the client using ODBC (Open Database Connectivity) and JDBC (Java Database Connectivity) drivers. This makes them compatible with all the prime data engineering and data analytics tools that further serve the IoT edges, Business Intelligence tools, Data Engineering clients, etc.The cluster-node architecture holds several databases for Redshift that can be queried using the editor or console offered by AWS. These queries can be scheduled and reused as per the business requirements. The permission to these queries can be managed more easily since there are multiple compute nodes to process the queries. For instance, here’s the code to deny the permission for the GetClusterCredentials command using a custom policy.Tighter Access and Traffic controlWith the cluster node architecture, it is easy to route the traffic for access control and security. Amazon Redshift accomplishes this using Virtual private cloud (VPC). With a distributed architecture, the data flow control between Redshift clusters and the client tools gets tighter and more manageable. The COPY or UNLOAD command directed at the cluster is responded with the strictest network path to ensure uncompromised security. Some of the networks availablefor configuration for RedShift are:VPC endpoints: In case the traffic has to be directed to an Amazon S3 bucket for the same reason, this path seems most reasonable. It provides endpoint policies for access control on either side.NAT gateway: NAT or Network Address Translation gateway allows connection in other S3 buckets in other AWS regions and services in and out of the AWS Network. You can also configure SSH agent depending on the operation systemMultilevel securityThe cluster-node architecture allows the security policies to work on multiple levels, strengthening the data security.Cluster Level: Security groups can be established to control the access to clusters in classless inter-domain routing and authorization. Additional policies can be set up for the creation, configuration, and deletion of the clusters.Database Level: Access control can be extended to the read and write for individual database objects like tables and views. The DDL and DML operations are also controlled to ensure that the mission critical data doesn’t fall into wrong or loose hands.Node Connection Authentication: The clients connected with ODBC and JDBC drivers can be authenticated for their identity using a single sign-on.Network Isolation: As we saw before, Redshift offers virtual private clouds to ensure that the networks are logically isolated and more secure for the cloud warehouse infrastructure. These isolated networks control the inbound traffic and allow only certain IPs to send requests and queries.Easy Clusters Management and MaintenancePolicies are set for Cluster maintenance and management that ensure regular cluster upgrading and performance checks. Regular maintenance windows can be set for the clusters to perform check-ups on them and ensure there are no deviations in their operations. These policies are determined based on:Maintenance Scheduling – You can decide maintenance windows during which the cluster won’t be operational and will be undergoing maintenance checks. Most of the vendors offer default maintenance windows which can be rescheduled as per your requirements.Cluster Upgrade – You might not always want your clusters to run on the most recent approved versions, or they can stick with the previous releases. Amazon redshift allows an option of preview that would help you understand your clusters’ expected performance for the new upgrade.Cluster Version Management – You can upgrade to the latest cluster version or roll-back to a previous one. The performance of the cluster version should be in line with your business requirements.ConclusionData warehouses were borne out of the necessity of making the data to work for our business rather than lying stale in our servers. Amazon Redshift, like many other vendors, took the concept further and evolved the traditional data warehouse architecture for cloud benefits. The cluster-node architecture isn’t a much big leap from the original Data Warehouse architecture. This makes it easier to manage, maintain and protect. With data pouring in from our social media interactions, project reports, workforce feedbacks and customer experience innovations, it will be of great disadvantage not to engage it. The clouds are ready to hold dense data volumes, all we need is a little (Red)Shift.

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Building a Modern-Day Data Analytics Platform on AWS

With the rise of cloud computing, companies are constantly migrating their legacy data warehouses or analytical databases to the cloud. However, one challenge that we might come across while doing so is letting go of our monolithic thinking and design and fully benefitting from the modern cloud architecture. In this article, we’ll learn the model for creating a flexible, scalable, and cost-effective data analytics platform in the AWS cloud. But first, let’s understand what the process of Data Analysis is.Process of Data AnalysisData Analysis is the science of analyzing, cleansing, transforming, and modeling data to discover valuable information, recommending conclusions, and assisting decision-making. A data analyst requires high technical ability, focusing on complex databases, statistics, and formulas that need skillsets to interpret data such as DATA mining, OLAP, SQL, Reports, statistics, etc.So, how can we use AWS Cloud Computing to build a Data Analytics Platform?Amazon Web Services offers an integrated suite of services that provides everything we need to quickly and easily develop and drive a data lake for analytics. AWS-driven data lakes can manage the agility, scale, and flexibility that is required to unite different data and analytics processes to acquire deeper insights in ways that conventional data warehouses and data silos cannot.What is Data Lakes?To create your data lakes and analytics solution, Amazon Web Services offers the most expansive collection of services to move, store, and interpret your data.Image Source: https://aws.amazon.com/Data Movement: Extracting data from various sources such as (AWS S3 Bucket, Dropbox, SFTP, FTP, Google Drive, or On-Premise HDD) and various types of data structures as (DOC, EXCEL, JSON, XML, CSV, PDF, or TEXT). Image Source: https://newsakmi.com/Creating Data Lakes With AWSTo create data lakes and analytics solutions, Amazon Web Services offers the most extensive set of services to move, store, and analyze the data. The first step in creating data lakes on AWS is to move the data to the cloud. Any physical limitations of bandwidth and transfer speeds reduce the capability of moving the data without any major disruption, high expenses, and time. To make data transfer smooth and flexible, Amazon provides a wide range of options to transfer data to the cloud. To develop ETL jobs and ML Transforms for the data lake via SSIS or AWS Glue Services.The AWS Services that you can make use of for Data Movement are:Direct connect for On-Premise Data MovementIoT for Real-time Data ConnectData Lake: Store various data types securely on diverse Database Systems such as (MySQL, MS SQL, ORACLE, MongoDB, DynamoDB) from gigabytes to exabytes.As soon as the data is cloud-ready, AWS makes it easier to store data in any format, securely, and at a large scale with Amazon S3, AWS Redshift, or Amazon Glacier. To make it simpler for the end-users to identify the relevant data to make use of in their analysis, AWS Glue automatically produces a single catalog that is searchable and queryable by users. The AWS Services you can employ for Data Lake are:S3 for Cloud StorageGlaciar for BackUp and ArchiveGlue for Data CatalogueAnalytics: Analyze your data with the broadest range of analytics services or algorithms.AWS offers the widest and most cost-efficient set of analytic services that operate on the data lake. Every analytics service is built for a broad range of usecases like interactive analysis, big data processing, making use of the Apache Spark and Hadoop, real-time analytics, operational analytics, dashboards, data warehousing, and visualizations.The AWS Services can be used for Analytics are as below:EMR for Big Data ProcessingKinesis for Realtime AnalyticsRedshift for Data WarehousingAthena for Interactive AnalysisElasticsearch for Operational AnalyticsQuickSight for Dashboard and Data VisualizationMachine Learning: Predict future results and direct actions for speedy responses.Now, for predictive analytics use cases, AWS offers a wide range of tools and machine learning services that operate on your data lake on AWS. ML has powered Amazon.com’s supply chain, forecasting, recommendation engines, fulfillment centers, and capacity planning at Amazon. The AWS Services we can make use of for Machine Learning (ML) are as follows:Deep Learning AMIs for Frameworks and InterfacesSagemaker for Platform ServicesConclusionAs long as you are curious and capable of learning the latest and better technologies, you can develop and operate a robust and modern data analytics platform. This data analytics platform on AWS is an indispensable part of the digital transformation and AI transformation of every organization that aspires to stay relevant and competitive in today’s industry.

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Retrieve or Rollback: Cloud Object Storage Data Versioning to the Rescue

Ghost of the Lost Data Significant files were accidentally deleted in a university in Wellington. “Huge volume” of IT calls baffled the IT admins as well as the post-grad students, some of them just a couple of weeks from handing in their thesis. A few corrupted files held up more than $130 million transfers for JPMorgan Chase, which they blamed on some “Oracle bug.” News like such often scares the business leaders into rethinking their data storage infrastructure. RAID errors, disk errors, and, of course, human errors can lead to some devastating data losses beyond recovery. In a time when the businesses are more and more dependent on SaaS tools and Cloud Native development, how would you ensure that you have your data backups ready for unforeseen calamities? There are several known ways in which this question can be answered. But the two most popular ways that fit with all kinds of cloud object storage infrastructures are: Data Redundancy and Versioning While we already discussed Data Redundancy in another article, this one would specifically focus on Versioning in detail. We will begin with explaining the concept and need of it and then move forward with more technical details. Versioning, the friendly neighbour In addition to redundant geo-replicas of data objects, data loss can also be protected by archiving their variants throughout history. Versioning simply denotes a process where these data object variants are stored in the same bucket. This makes the data restore and retrieval faster. Thus, while data redundancy is more of a globally spread ally, versioning is like a next- door neighbour that can immediately come for help. It is a powerful tool for data protection, especially during times when remote work and global access have lowered the guards for cloud object storage. Any instance of data corruption can be easily dealt with by rolling back to a previously most reliable version. Be it human error or some application failure, maintaining versions for data objects can provide more immediate damage control, hence, much lower outages for the end customer. Versioning is a very cloud savvy solution for data durability. Object storage in cloud infrastructure is not only scalable but also easier for data retrieval thanks to the shared storage pool. So how does Versioning exactly help, and how do you implement it for your cloud storage infrastructure? Let’s find out. Different Versions of Aid Fool-Proof Process When you enable versioning for the data objects, the variants of these data objects don’t overwrite their predecessors. General practice is to store the object versions with different version IDs, thus making it easy for Cloud Storage to retrieve only the latest version for transactions as long as there isn’t a need for a rollback to the previous version. Moreover, having different version IDs reduces the impact of human errors while working with the data objects. Operational Security and Authorization Rather than accidental overwrites, data loss gets more severe in case of accidental permanent deletes. Therefore, Versioning is also subjected to authorization and security. Take the Amazon S3 approach, for instance. Here, for the purpose of initial protection (a first-aid, if you may), the deleted object isn’t permanently deleted and just has a delete marker attached to it. Thus, when the user tries to GET the data object, the system throws a 404 Not Found error. This means that the latest version of the data object is practically “deleted.” However, to permanently delete these data Faster Data Recovery While retrieving the redundant geo-replicas might take a little time, Versioning allows faster data recovery, especially in the face of minor accidental overwrites and deletions. All one has to do is roll back to the previous version ID and continue the work without interruption. This also makes object storage versioning an effective weapon against data outages. Additional Security Measures Apart from the initial authorization and the delete marker, Versioning also complies with measures like Multi-factor authentication, where the user has to be authorized from multiple sources before allowing them to make any permanent changes in the data object versions. Such additional measures also make these versions more secure from external attacks due to data breaches and unauthorized access. Add to this the cloud security measures against phishing, DoDs, and ransomware; the cloud object storage gets practically super-immune. Let us now look at how you can enable versioning for your cloud object storage. How to version? Here’s are the steps to enable versioning for your bucket. STEP1: Uploading the File onto storage The file is uploaded onto the storage to get a unique ID (known as version ID, generation number, or something else, depending on the cloud storage vendor of your choice). The version ID, as discussed above, will hold the current version of the data object for future reference. STEP 2: Request Versioning Next, you request to enable versioning for your data object. By default, most of the vendors have the versioning disabled to ensure unnecessary replication. The XML request sent has a status added to it, thus requesting the versioning to be enabled or disabled. The versioning request is approved by the authorized bucket owner. STEP 3: Enable/Disable Versioning The bucket owner can enable the versioning for their respective object storage buckets using multiple ways as offered by the cloud storage vendor. Some of the popular ones are: Storage Management Console – A GUI based console where the owner can sign in, select the intended bucket and enable or disable versioning Storage Management CLI – The command line interface or CLI works with textual commands to respond to the versioning request Storage Management SDKs – For users working with Java, .Net, or similar languages, SDKs work best for the purpose of dealing with versioning requests. Conclusion IBM suggests that millions of USD can go into putting out the fires caused by data corruption and data loss. Be it a big university responsible for encouraging the researches and innovations of its pupil or a multi-national IT firm aiding the world with its simplistic solutions. The value of data cannot be compromised for performance speed and intelligence. Versioning can be the defensce you need against accidental data corruption due to any overseen security flaw or internal human error. Enable versioning for your critical data objects and leave the rest to the scalability, access security, and speed of cloud object storage.

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Data Loss Looming Threat; Cloud Storage Data Redundancy, Saviour

A Minor InconvenienceYou took a break from work and started scrolling through your social media feed. Suddenly, you stop scrolling as your eyes fall on a headline that goes somewhat like, “Man accidentally deleted company database thanks to a coding error.” Shrugging it off as a marketing gimmick, you move on with the day. But later that night, right before you go to sleep, your brain throws the question – “How prepared are we in case something like this actually happens?” Here’s an article to answer that question.Previously on Data StorageThe history of technology has essentially been the history of shrinking hardware. Information storage is no blip, for that matter. Organizations have always worked on relocating their investments to attain volumes of data stored in the most compressed spaces. While the general stimulus for this behavior has been the human need for innovation, data storage architects have also been concerned about irreversible data loss. Fires, equipment damage, external attacks, or even coding accidents have been a few of the bafflements against storing huge amounts of business-sensitive data. Many services have been developed to ensure data storage. Most of them have been about replicating sensitive data and storing it at multiple locations. However, managing these copies has been a headache of its own. That was, of course, until quite recently when the IT world found arguably the greatest disruption in Data storage so far – Cloud Storage.The cloud storage providers, whether Amazon S3, Azure cloud storage, Google cloud, or any other popular name, inherently offer automated data protection strategies owing to what is called Data Redundancy. In this article, we will try to understand more about Data Redundancy. Using that knowledge, we will try to fix a few metrics to assess your cloud storage service.Data Redundancy, an Increment to Your Familiar Data BackupWe all keep at least two copies of our house keys. One goes with your favorite keyring, while the other stays hidden below the flower pot outside the house. That is practically the approach that inspires Data Redundancy. Cloud Storage vendors allow multiple copies of business intensive data across multiple geographies. While one of the copies of your data might be in Singapore, the other might very well be under a sea! Such geo-replication ensures that even in the case of an unavoidable catastrophe, the data is not only secure but accessible.Data redundancy is an exclusive offering by cloud storage. While data backup strategies so far were merely dealing with compressed data replicas stored within the organization’s infrastructure, cloud storage added an extra layer of global connectivity and performance scalability. Geo-replicas created by cloud storage is more manageable, readily accessible, and yet heavily secured. The data is encrypted using Prefect Forward secrecy cypher suites like DHE (Diffie-Hellman Ephermal) or ECDHE (Elliptic Curve Diffie-Hellman Ephermal that restrict its access and readability. The access network itself is guarded by security sensitive APIs and constant request monitoring solutions.The next question that naturally comes up is – How do you opt for a cloud storage vendor based on its data redundancy offerings? Answering that question would and choosing the right cloud storage infrastructure needs a more nuanced discussion.Data Redundancy Decisive FactorsCloud Storage ensures that the risks of your data vulnerabilities don’t slow you down. Data Redundancy is a big help on that front. But how does one assess the efficacy of this site-to-site replication? The below decisive factors provide the answer.Redundancy LevelDepending on the scale and size of your data, you might need multiple local and offsite replicas. While for a small size business, the regular N+1 redundancy works fine, greater levels would be required as you scale up. Moreover, local backups might be more easily recoverable in case of internal errors; offsite replicas are effective against unforeseen disasters and external attacks. This your cloud storage provider should be able to guide you in understanding your data needs and help you decide the redundancy level accordingly.Access SecurityThe access security provided by your cloud storage vendor also needs to be probed for choosing the most secure redundancy services. Most suitable providers would use strict access control policies that would ensure that the local and offsite replicas aren’t accessible to unprivileged and unintended users. With the right policies and control lists, you can ensure that your backups, wherever they might be, are readily accessible yet secure.Source – Access control list (ACL) by AWSData ComplianceRedundant data across multiple geo-locations are likely to fall under varying compliance guidelines as per the administrative authorities. While choosing a cloud storage service provider, you should be inquisitive about the security and other policies maintained by them. The ownership policies and responsibilities should be well addressed in terms of user-roles, access control, user-identities, and other relevant factors. All these factors would need to comply with the standardized guidelines offered by the regulatory norms. Failing any of them might land your data replicas and organization in trouble.Automation ServicesAutomation with Cloud is a given. Still, make sure that your cloud storage vendor specifically provides automation strategies for identifying critical information, timeline revisions, versioning, auto-saves, and reports. Data redundancy automation would also ensure that the replicas are compliant with the regulatory and access policies that you need to follow. In case of any mishap, automation strategies would quickly recover the data in the best available state and would let your employees carry-on with the work unhindered.Secure MobilityWhile cloud storage promises mobility and global access, you should be careful about the networks through which you would access the data copies when needed. Attacks like Denial of Service might jam up your network and delay or terminate the data recovery process. Make sure your cloud storage provider strategies like intermediate security components that help the network steer clear of any unwanted requests and ensure zero data outages due to delayed data recovery.ConclusionData Redundancy is the perfect companion for your cloud storage infrastructure. The stronger strategies and policies for your geo-replicas, the stronger guarantee you would have for the uptime of your services. With the above discussed factors in mind, you can choose the best cloud storage services for your business and sleep well without worrying about fatal coding errors!.resourceSingleInnerRight > h5.blueTitle{text-align:left;}.filledCheckboxes input[type="checkbox"]{opacity:0;display :none;}.multiStepFormBody button.close{z-index:99;}

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Aziro 2021 Technology Predictions, From Cloud to Edge and everything in between

As we hopefully look forward to what can be a better year, Aziro (formerly MSys Technologies) brings you the 2021 tech predictions based on our Software Product Engineering Services and Digital Transformation expertise. 1. Software Product Engineering Momentum for Single Data Platform with Low Code/No Code 1.1 Single Data Platform and Low Code/No Code to be Preferred Choice In 2021, we will see one single Data Platform for business use cases. Also, the Low Code/ No Code will become the norm in the product engineering field. Whether enterprises need to fill developer gaps or reduce development time, single low-code platforms will be a popular and easy-to-use solution. It will be of great help and game-changer for several organizations. 1.2 Modern Programming Languages to Emerge as Frontrunners Backed by the Tech giants, expect modern programming languages to gain more traction, popularity, and usage in 2021. Companies will look forward to using modern programming languages instead of traditional ones like C/C++ for more security-sensitive and complex projects. 1.3 Client-Side and Server-Side Web Frameworks – Convergence In 2021, all the major JavaScript/TypeScript based client-side frameworks such as React, Vue.js, and Svelte shall cross-pollinate for improved usability. Also, ASP.NET Core will be a significant player in Server-Side development for enterprises. Several small companies and start-ups want to have an end-to-end framework, including the View layer, for rapid application development. PHP Laravel, Python-based Django, and Ruby on Rails will be great options for them in 2021. 1.4 Cross-Platform Development to Compete with Native App Development Though Native app development is still the better choice for enterprises, cross-platform app development is catching up. In 2021, larger enterprises will favor Native app development; however, small companies and start-ups will favor cross-platform app development. 1.5 REST API for Business Applications REST API will continue to dominate API technology in 2021. Software or App developers can now develop sophisticated Cross-platform or native apps using REST API. 2. Cloud Engineering Code-First To Take Over Open Networks, Cybersecurity Remains Top Priority 2.1 Code-first open network architecture In 2021, the Code-First model will take over the entire open network architecture, focusing on the domain of the application. It will start generating classes for the domain entity instead of designing the database first and then creating the classes that match the database design. This code first open network architecture, will ensure an efficient flow of information, and control data up and down the organization seamlessly. 2.2 Edge Computing and 5G to Gain Ground Value 2021 is the year edge computing will finally become a real value. In 2021, new business models will evolve to promote the deployment of “edge” in production. Cloud platforms needing to compete with artificial intelligence and the extensive proliferation of 5G will make edge use cases much more practical. 2.3 Automated Governance and Heightened Cybersecurity With the heightened awareness of governance and cybersecurity in 2021, an open governance and security automation framework will help build code to minimize human error. This will allow cybersecurity professionals to develop structured workflows that can be integrated into existing SOAR platforms and SIEM applications, ensuring that security strategies are aligned with business objectives and consistent with regulations. Automation combined with open-source tools is the key to a code-first model, edge computing, and robust cybersecurity. 3. DevOps Uprising of BizDevOps and Auto-Pilot mode in DevOps Automation 3.1 Automated Code Analysis In 2021, we will see an uprising of automated codes to identify the release cycle challenges as early as possible. Static and dynamic code analysis tools will help enterprises ship code that is more stable and faster, and minimize production challenges. 3.2 DataOps Will Grow 2021 will be a high time when DevOps will start using available data and metrics to generate valuable insights. Such foresight would predict incidents or outages, develop automation, and forecast capacity to improve budget planning. 3.3 DevOps Becomes BizDevOps 2021 will see a constant rise of BizDevOps being widely adopted by enterprises aiming to be more focused, agile, and flexible. This is due to the existing pandemic situation and the necessity to transform into full digitalization. With the right BizDevOps tools, enterprises will have the power to streamline their business innovation process while minimizing the risk of uncertainty. 3.4 The Evolution of GitOps GitOps signifies how DevOps apply developer tooling to drive operations. In 2021, GitOps will speed up development to make changes and updates securely into complex applications running in Kubernetes. 3.5 Autonomous DevOps Automation and Chaos Engineering DevOps will now slowly move towards autonomous and advanced techniques that help in activities within the lifecycle and automated outputs across all the stages. The ecosystem will include robotic process automation tools to help automate manual tasks for better productivity. Also, chaos engineering is all set to become an extremely critical aspect of today’s hybrid infra world. In 2021, it will be used more and more to boost confidence in the system’s ability to withstand turbulent and uncertain outages. 3.6 Serverless Architecture Is on The Rise Another pattern that will revolutionize DevOps in 2021 is the application of serverless architecture. With serverless, enterprises can overcome any barrier amongst operations and development. This could help further in enabling operability and achieving business agility while minimizing costs. 3.7 Emergence of NoOps and DevSecOps 2021 will see more and more managed services emerging to minimize DevOps operations and reduce OPEX in customers. Also, the “Sec” part of DevSecOps will become an integral part of the SDLC. Customers will be delivered with practical security solutions to optimize software functioning. Automation blended with code analysis, GitOps, DataOps, DevSecOps, NoOps, and serverless architecture will enable BizDevOps and chaos engineering. This will help drive tangible business results for enterprises worldwide. 4. Storage Engineering Smart and Secure Storage with Artificial Intelligence, powered by NVMe 4.1 AI and Storage Conflation In terms of performance, this is a good year for AI and Storage cooperation. Rising Artificial computing (AI) applications indicate a boost in accelerated compute servers, specialized processors. This is good news for smart NICs and data processing units (DPUs) as processor CPUs heavily rely on them for data center efficiency and flexibility. 4.2 Containers to see an increased application in Storage Container-based services will handle scalability and Agility for storage solutions. Container-centric transactional databases, backup, and archives, logs, among others, will outdo their traditional counterparts owing to the unequivocal popularity of Kubernetes. 4.3 Wireless Innovation to Boost IoT and Storage Integration 2021 is also a good year for exploring better wireless innovations. This is good news for cloud storage services. With better wireless connectivity options, organizations could easily integrate IoT technologies and cloud storage and processing solution with their on-premise storage ecosystems. 4.4 Newer Security Measures to Grace Data Storage The hyper-scale software ecosystems will also be strengthened by security measures like hierarchical data security and data-at-rest encryption algorithms. Owing to the rise in mobile access for data in 2020, Storage tiering and active archives will make more sense from a security perspective. This also paves the way for service-mesh based secure networks to enable more remote connectivity with on-premise core-data centers. 4.5 NVMes to Continue their Impressive Run Rate With their support for Remote Direct Memory Access, RDMA, NVMe based storage solutions are going to thrive this year as well. With the advances in SSD storage and PCIe buses, NVMe will maintain a prominent storage landscape presence. NVMe’s support for Computational storage drives, CXL interface, and ASIC processors will make it an essential player in integrating Storage and Artificial intelligence innovations. Thus, while the organizations explore artificial Intelligence-based Data Lakes and Data Warehouses, container-based infrastructures and cloud storage innovations would be indispensable in the near future. NVMes-based solutions would be a preferred choice when it comes to hardware, and advanced security measures would be welcomed to build a competitive data storage advantage. 5. UI/UX Advanced Micro-interaction and Super Apps to drive Digital World 5.1 Software driven Behavioral Research 2021 will see the rise of new software for getting behind a user’s movements and understanding their behavioral patterns. We will see more and more designers using it in the coming years to follow user issues and preferences and validate the design decisions. 5.2 Advanced Micro Interaction In 2021, we expect to see micro-interactions such as a user clicking on a button— that causes the page to respond, become a lot more macro. With this, the designers will be intensifying the micro-interactions through advanced animations and page transitions, resulting in the UX responding to the inputs and maximizing the user’s association with the page. 5.3 AI Algorithm in UI/UX Though humans are still dominating the UI/UX field, and AI will need much more fine-tuning to become efficient, the AI algorithms will witness increased adoption by UI/UX designers. 5.4 3D and Immersive Experiences In 2021 the designers’ interest in 3D components and entire 3D scenes in interfaces will continue to grow more popular. Unusual angles, cool abstractions, etc., in 3D, will attract more attention and make websites more appealing. This shall encourage users to remain on the page longer and increase session time. 5.5 Super Apps 2021 will see the emergence of Super apps, which will combine several services and try to enhance user experience. Enterprises need super apps to create ecosystems that cover all their needs. The more and more time a user spends in an app, the greater the loyalty generated, that can be monetized later. UI/UX designers are all set to provide us with signposts through this brave new virtual world using behavioral understanding and advanced micro-interaction combined with AI, 3D, and super apps. 6. AI/ML and Data Science On-demand Storage access Key to Data Lake while AI/ML to face more Scrutiny 6.1 Data Analytics to Become more Nimble With containerization and cloud computing enjoying their prime, data analytics ventures are going to explore better data lake options. With Hybrid and Multi-Cloud infrastructures gaining traction, new abstraction services for anytime-anywhere Storage would be a key factor in data lake selection. Moreover, with Microservices architecture utilizing cloud infrastructure to the maximum, it would also make sense for data architectures to decouple and imbibe a flexible tier for application design and big data analytics. 6.2 AI to Face More Scrutiny However, reaping the benefits of such advances in data analytics would come with some responsibility. With increase end-user awareness and engagement, ethical AI alone might not make the cut. Thus, responsible AI practices will be encouraged more this year. After all, RPAs are sure to leverage AI-based unstructured data processing tools for transactional enterprise activities. While Automation will make a lot of lives more comfortable, accountability will now be demanded more than any time before. 6.3 ML to Get More Mainstream Machine learning frameworks are going to be more operational in their efforts. Frameworks like PyTorch and TensorFlow that have been enabling model training will be the frontrunners along with Presto for interactive querying. Thus, welcoming hybrid and multi-cloud infrastructures would make Data Science an efficient choice this year. As for AI/ML leveraging responsible and accountable exploration of tools combined with leading operational frameworks will definitely result in more intelligent Automation and rich customer experience. 7. Kubernetes, Microservices Kubernetes gaining reliability; Microservices losing? 7.1 Managed Kubernetes to See More Open Arms Hybrid and Multi-cloud strategies are clearly maintaining a frontrunner position this year. This implies that managed Kubernetes services are to experience more open arms than ever. The container orchestration tool would be an essential ingredient for products and services, ranging from AI/ML to Edge Computing and Data platforms. 7.2 Microservices Contemporaries The software architecture markets look further divided post the introduction of Serverless Architecture. For 2021 it would be safe to say that the three popular architectures – Microservices, Monolith, and Serverless – are going to be contemporaries, each offering their benefits with a few backfires here and there. That said, large-scale enterprises have come way past the monolithic architecture while keeping the serverless reserved for event-driven loads. Microservices will still thrive as a critical architecture for their products and services. 7.3 Service Mesh to Play Pivotal Role in Cloud Native Space Service mesh technology for security management is sure to thrive on Microservices architecture. The service mesh ecosystem is also likely to integrate with more critical tools in the cloud-native environment. Thus, it imperative that the IT stakeholders start exploring containerization technologies like Container Runtime Interface (Kubernetes) and Open Container Initiative (OCI) to stay ahead in the cloud race. There is no clear silver bullet for the architectures, although leveraging open-source projects and tools seems more in favor of Microservices architecture than any other counterpart. 8. Outsourcing Services 2021 Cybersecurity and Cloud gain outsourcing traction while Captive centers grow 8.1 Cybersecurity to Gain Big in Outsourcing Annual global expenditure on Blockchain technology is not slowing down before 2023. The distributed ledger tech has a lot of appeal for innovative ventures owing to its transparency, freshness, and, most importantly, security. In fact, the entire cybersecurity domain is going to be a major influencer in IT business outsourcing. In the post-pandemic world, owing to the jump in remote work, there’s also been a substantial rise in malicious emails and other forms of cyberattacks. Hence, just like Blockchain, more sophisticated multi-level security instruments would be employed and consulted upon. Outsourcing for such cybersecurity solutions would be a big aid to the in-house teams. Talking about aiding the in-house teams, Business process automation (BPA) will also be a point of interest in 2021. With organizations growing more confident about Artificial Intelligence fuelled Robotic Process Automation (RPA), creating more autonomous workspaces is inevitable. 8.2 The Heroic Rise of Captive Centers Another critical activity to focus on in the 2021 IT business outsourcing prospect is the growth in captive centers or global in-house centers. Exploring futuristic technology aptitude would require the organizations to move out of their geographical domains. Therefore, 2021 is likely to say an appreciable rise in captive centers, especially in the fintech, healthcare, and telecommunication industries. The post-pandemic era has escalated its need for digital transformation to more immediate. Thus more industry-agnostic GICs will be welcomed to form a core tech innovation apparatus for the multinationals operating in these domains. Thus, the outsourcing providers are suggested to invest more in expertise pertaining to cybersecurity technologies, effectively in blockchains. Expertise in RPA and BPA, along with cloud services, will get the clients’ attention for next-gen projects. The digital transformation service providers can focus more on projects from fintech, healthcare, telecommunication, and retail. Stitching it All Together Storage, Cloud, Blockchain, AI, analytics, DevOps, UI/UX, and Automation are poised to disrupt IT technology solutions in 2021. However, better trend insights and lessons will serve companies and technology partners to navigate the uncharted paths. Aziro (formerly MSys Technologies) is one of the most accomplished product engineering service providers helping organizations achieve high-quality, high-accuracy software product engineering capabilities to develop cost-effective and highly scalable digital products and solutions. Let’s discuss to suit-up for the new normal and keep acing digital disruption!

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Self-Healing Storage & Fail-Safe Storage

Consider the below scenario You are a cutting-edge, cloud-powered Independent Software Vendors (ISVs). You have an esteemed customer base who are delighted by your uninterrupted services. On one fine day, you experience an IT outage. Wouldn’t your storage infrastructure suffer a backlash? Ok! Like any other IT organization, you have a proper backup plan. This will ensure business continuity. But, just think again, what if this backup plan succumbs to failure? Now, you require adequate time to bounce back. Meanwhile, your customers get frustrated due to service interruption, which brings down efforts for a rich customer experience management. We are almost entering the third decade of the 21st century, yet the modus operandi of technology is far from being proactive. Instead of waiting for something to break and then repair, we must unleash our technology prowess to undo glitches in the first place. So what is an ideal world for IT System Administrators? Imagine a moment of panacea, where your storage systems keep themselves healthy through self-monitoring. You would solace when they self-remediate their issues or possibly alert you in case of a complex surgical strike. This means, zero human intervention, while all these repairs initiated behind the scenes. Roots of Self-Healing Mechanism – Autonomous Restore and Backup Repair The consistent state of the data storage system is to maintain data availability. But we know that metadata inconsistency can result in a nosedive. Therefore, we can induce self-healing mechanism via stimulation of the restore system. This will enable a device to capture the latest snapshot stored in a system, even if there is a restore failure. And this restore simulation procedure must be triggered periodically to ensure auto-pilot storage repair mode. Post analyzing above-mentioned fallible storage possibilities, let us look at self-healing storage for maximum business availability, interrupted business operations and ultimately, delightful customer experiences. 2 Types of Self-healing Storage 1. Fail-Safe Storage Consider you are building a pile of boxes. To achieve your goal, you must have a minimum of 15 boxes. Unfortunately, the ninth box is defective. In such a case, you can add one additional box to maintain the threshold and still achieve your desired goal. This is what we call a fail-safe method. In storage engineering, this concept utilizes spare capacity to counter the challenge of Hot Swap or Hot Plug persistent in hard drive disks (HDDs). You create disk drives, which has in-built additional drive capacity. Whenever there is a failure, it triggers an automatic swap to replace the failed drive/s. A Fail-Safe storage architecture fits the bill for IoT Applications. A typical IoT architecture will consist of a Controller, Storage Server, Sensor, and Interface Connectivity. The sensor collects the data to house in the Storage Server via Interface Network, which is relayed to Controller resulting in a trigger action or communication. The hottest emerging IoT application is Autonomous Vehicles, which demands a high degree of safety. For the same, it relies on uncompromised data analytics. And this data is sourced from the underlying hardware integrated into the vehicle. For example, Cypress Semiconductor launched’ Semper NOR Flash family that is intelligently designed for fail-safe storage in ADAS. It has EnduraFlex™ Architecture, which leverages Arm Core for independently optimizing memory array. As a result, it can retain data for up to 25 years. 2. Heal-in-Time Heal-in-Time means executing corrective measures in real-time. Upon detection of underlying issues in a software or hardware, it must immediately be contained. Consider Oracle Solaris Predictive Self-Healing (PSH). It’s Operating System consist of a Fault Manager daemon, fmd(1M) that continually runs in the background. If there is an issue, fmd(1M) will diagnose its nature by comparing with data of previous errors. It then assigns a Universal Unique Identifier (UUID) to it. The Fault Manager daemon takes the software/hardware component offline, so it doesn’t affect the rest of the system. In meanwhile, it reports the affected components without requiring any manual intervention. Similarly, The IBM® XIV® Storage System includes built-in mechanisms for self-healing to take care of individual component malfunctions and to automatically restore full data redundancy in the system within minutes. Three Key Business Benefits of Self-Healing 1. Self-Maintained Systems Machines require continual maintenance to be up and running. Similarly, a software application must undergo frequent up gradation to remain efficient. On occasions, caches must be cleared, and services must restart. This might sound a menial and easy task, but it eats out much precious time and hampers effective execution of their core competencies. This is where self-healing storage can be fruitful. It would eliminate any kind of manual intervention and regularly update your application. It would ensure higher employee productivity along with seamless application functioning. 2. Intelligent Remediation In case of any issue, self-healing storage would independently alert the IT admin department. Upon alerting them, it would intelligently remediate the issue. A self-healing store works like a tier-one repair machine – solving the problem of an application crash, disk overheating, excess disk vibrations, network connectivity, etc. 3. Cases beyond automatic remediation Self-healing storage facilitates ongoing storage monitoring with real-time storage health insights. In a case, where the automatic remediation is beyond the scope of its capabilities, the issue can be immediately highlighted. Resultant – IT administrators get ready-to-act information about the problem with varied details. This ensures human action at the right time, which maximizes repair effectiveness and save. Conclusion Complex software development is the new norm. Datacenters will have to become equally sophisticated to support new-age application needs. The rise of All-Flash Arrays, 3D Nand, and Hybrid Infrastructure has likewise increased the usage of AI. This is because algorithms can be easily integrated into the arrays and other components of modem storage architecture. The induction of self-healing capabilities within your storage devices empowers you to create a first-tier repair mechanic – automated, self-sufficient. A self-healing storage sucks up your technical overhead while generating significant cost savings. It monitors your devices/backup systems for any possible failures. Upon detection of any anomalies, it triggers a self-repairing process to avoid business hiccups and service outages. Self-healing in storage sounds fancy for now, but let us be assured that soon it will be a necessity.

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Why Strategy Beats Features — Always

IntroductionImagine sprinting in a race with no finish line.You run faster, harder — yet never win.That’s what chasing features without strategy feels like.In today’s IT-driven product ecosystem, speed is often mistaken for progress. Teams keep adding integrations, toggles, and design themes — but forget the why.Strategy is what connects motion to meaning.The Feature Factory TrapMany organizations equate productivity with success:“If we shipped 20 new features this quarter, we must be doing great.”But the truth?Features add noise. Strategy creates outcomes.Real-world Use CasesSaaS Overload: A SaaS platform launched 100+ integrations in a year to “expand options.” User adoption fell by 15%.Why?They solved fringe problems instead of core pain points.Dark Mode vs Checkout Fix: An e-commerce team prioritized “dark mode” over fixing checkout errors.Users quit before they could even enjoy the new theme.The Three Pillars of Lean Product StrategyEvery strong IT product strategy rests on three timeless lenses:Desirability – Do customers truly want it? This ensures you’re solving the right problem, not just building for novelty.Viability – Does it make business sense? Every decision should connect back to measurable outcomes or ROI.Feasibility – Can we realistically build and scale it? Aligns ambition with technical and operational capacity.These lenses transform teams from reactive builders to purposeful creators.Strategy Across the Product LifecycleStrategy isn’t a one-time phase — it’s a continuous discipline:Concept Acceptance: Validate the problem, not the feature. Use user research, surveys, and mock testing.MVP Development: Test assumptions with minimal investment. Dropbox began with a demo video — no code, pure validation.Market Testing: Gather real-world feedback, not opinions. Learn fast, adapt faster.Prioritized Roadmap: Turn insights into sequenced delivery. Slack deprioritized “offline mode” early on to focus on reliable real-time messaging — a strategic masterstroke.Strategy in Product Development ExecutionEven the most elegant strategy is useless unless it flows into execution.Backlog Creation: Translate strategy into prioritized epics aligned with outcomes.Development Sprints: Every story must ladder up to business or user value.User Acceptance Testing (UAT): Strategy validation in disguise. UAT Framework:Develop PlanIdentify Real-World ScenariosSelect Testing TeamsTest & DocumentUpdate Code, Re-Test & Sign-OffRelease Notes & Documentation: Close the loop. Every change should explain why it exists.Frameworks That Shape Winning Product StrategyHaving a strategy is good.Having a repeatable way to build one is better.North Star Framework:Focus around one measurable outcome that reflects customer value.Example: Spotify’s “Minutes Streamed” metric.→ Teams using North Star Metrics align 40% faster across functions.OKR Framework (Objectives & Key Results):Connect vision to measurable impact.Example: Google uses OKRs to ensure every sprint outcome aligns to business growth.RICE Prioritization Model:Formula: (Reach × Impact × Confidence) / Effort.→ Teams using RICE-style scoring report 32% higher release-to-impact ratios.Kano Model:Classifies features into Basic, Performance, and Delighters.Helps maintain balance between innovation and necessity.Case Studies: When Strategy WinsFinTech: StripeStripe didn’t compete by adding 200 payment types. Its strategy? A frictionless developer experience.That clarity turned API usability into a growth engine — powering over 60% of global startups today.E-Commerce: ShopifyShopify avoided feature sprawl by doubling down on enabling entrepreneurs to launch quickly.SaaS: NotionInstead of chasing every enterprise feature, Notion focused on modularity and user empowerment.Its strategic bet on customization paid off — achieving 4M+ daily active users by 2024.How AI Is Powering Strategic Product ManagementAI is no longer a “nice-to-have” — it’s a strategy multiplier.Market Intelligence: Tools like Crayon or SimilarWeb analyze competitors for smarter positioning.Customer Insights: NLP models process feedback from reviews, tickets, and NPS responses.Prioritization Assistance: Tools like Aha! and ProductBoard use AI to score ideas automatically.Predictive Analytics: AI models forecast feature adoption or churn risk.UAT Automation: AI-driven test case generation shortens feedback loops.Why This Matters“A good strategy doesn’t slow you down — it ensures you’re running in the right direction.”Great Product Managers don’t ask,“What can we build next?”They ask,“What should we build next — and why now?”Harvard Business Review notes that companies using strategic prioritization frameworks achieve 33% higher success rates in product launches than those chasing unprioritized features.Next in the SeriesCurious what PMs truly own beyond features?Stay tuned for Post 3: Product Manager vs Product Owner vs Project Manager— breaking down real accountability in the product world.Article written by Deep Verma | Exploring product management beyond the backlogFollow the series: #BeyondTheBacklog | #AziroOnProducts

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