How to select Data Architecture for your application?

 How to select Data Architecture for your application?

The era of Cognitive computing is here and is taking knowledge to next level to bring about a radical change in the software industry. Shaping cognitive expertise into a lot of the data processing solutions and human-designed systems on earth with a type of thinking skill has become imaginable with this paradigm shift in the era of computing. On the other hand, planning data-centric solutions in the cloud can be little difficult. Not only do we have the normal task of meeting client necessities, but we also have to deal with an ever changing data setting of implements, prototypes and podiums. Data architecture by Azure offers a well-thought-out line of attack for designing data-centric resolutions on Microsoft Azure. It is centred on confirmed practices resulting from client arrangements.

Significant Changes are noticeable with the advent of cloud

From data processing to its storage, the cloud is altering the way applications are designed. Gone are the days of a solo database looking completely after a solution’s data. Now a days multiple, multilingual solutions are used with each one offering precise skills. Applications are designed around a data pipeline.

Concerns while going for Data Center Architecture for your application

While bearing in mind data center networking architecture software engineers must strike equilibrium between consistency, performance, swiftness, scalability and budget. In addition, to enhance data center savings, the architecture must also be able to support both present and upcoming applications. As a result, what should software engineers think through?

Let us delve into significant decision aspects while opting for data center architecture:

It is important to evaluate each case individually. Understanding of each unique The size of the data center, anticipated growth and whether it’s a new system or an upgrade of an old legacy system will all impact architecture choice; for best performance, understanding each unique condition is essential to ensuring the right cabling infrastructure layout is chosen. Think through diverse architecture arrangements, diverse layouts suit unlike circumstances, in addition to presenting different outcomes. What is more, contemplate on equipment assembly ways and means.

The data solutions explained comprehensively

On a day to day basis bulks of data get generated at ever-accelerating degrees. Consequently, big data analytics has turn out to be a great tool for companies eyeing to control mounts of valued data for revenue and economical gain. In the middle of this big data rush, two types of data solutions – Traditional RDBMS workloads and big data solutions- have been profoundly endorsed as the one-size fits everything answer for the industry domain’s big data difficulties.

Let us deep-dive into the nuances of the design patterns of both and the finest practices, challenges and procedures for designing these types of way outs.

Traditional relational database solutions

A relational database is a set of properly defined counters from which data can be logged on or get back together in a lot of diverse methods minus having to restructure the database tables. The normal worker and application programming interface (API) of a relational database is the Structured Query Language also known as SQL. SQL statements are used equally for communicating demands for data from a relational database and for collecting data for information.

But, what is there in a relational database prototypical?

Standard relational databases allow operators to be able to predefine data relationships through numerous databases. For each table, this is at times termed a relation, also termed as attributes. Each row comprises of an exclusive case of data. A sole primary key is there in each table, which recognizes the info in a table.

Advantages of relational databases

There are a lot of pluses of relational databases. Let’s discuss them in detail.

• The biggest plus of relational databases is that they allow consumers to classify and accumulate data with no trouble. And, this data can later be enquired and filtered to extract particular info for reports. They also can easily be extended. As soon as the new database gets created, fresh category of data can be added devoid of all prevailing applications being changed.
• It is accurate, as data gets deposited just once, which eradicates the duplication of data.
• It is flexible, as difficult requests are stress-free for consumers to accomplish.
• Collectively, a lot of consumers can access the identical database.
• It is reliable as relational database prototypes are well-understood.
• It is safe and sound as data in tables in relational database management structures can be restricted to let access by lone specific consumers.

These are some of the advantages which these systems have to offer. On the other hand, despite the fact they are extensively used, relational databases have some downsides too. The main disadvantage of relational databases is that they are too pricey in arrangement and keeping up the database system. With the intention of arrangement of a relational database, you need to obtain different software. As abundant information is available, developments in the difficulty of data cause another downside to relational databases. Case in point, relational databases are prepared for bringing together data by joint features. Also, as some relational databases have restrictions on field sizes leading to data loss. This is because while designing the database, the amount of data needs to be specified. Sometimes in difficult relational database systems, info cannot be shared effortlessly from one big system to a new one.

Big Data architectures

Big data architectures are used wherein there is too large or complex data for traditional database systems to handle. It is planned to have a grip on the breakdown, processing, and exploration of huge data. A lot many firms are entering into the big data world. However, the starting point at which firms are entering is completely different, as it is influenced by the abilities of the consumers and their tools. For this very reason, the tools used for working with big data sets are getting updated day by day. This has made things easier than ever before as large data can be extracted now from your data sets through innovative analytics.

The data setting has changed from the past few years. Data is increasing day by day and when it arrives at a quicker pace, it demands to be collected and witnessed. On the other hand, some data arrives at a slow pace, but in huge amounts. Then at this point of time, this data requires machine learning. At this juncture, big data comes into play as these are tasks that big data architectures strive for to answer. A lot of workload is needed in seeking these tasks.

When the big data architectures need to be contemplated?

Contemplate big data architectures when the necessities listed below arise:

• When there is a large amount of data which needs to be stored and processed.
• When you need to alter free data for exploration.
• When you need to process and evaluate abundant chunks of data in real time.

In each type, the main choice norms along with a skilled medium have been described to make things easier. Based on the facts described you can decide on the precise technology for your set-up. On the other hand, in definite circumstances where running workloads on a traditional database may be the better solution.

Conclusion

This write-up is by no means a deep-dive in the nuances of common classifications of data solution. This is to provide decent, comprehensive content in this theme, letting us to know and escalate the essential ideas in picking up the precise data architecture or data channel for your setup. You just need to dive into detailed focus extents as needed for deciding on the Azure services and technologies that are an apt fit for your requests. In case, if you have already zeroed on the architecture, then hopping straight to the technology pick seems the right thing.

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Fundamentals of Azure Blob storage

Azure Blob storage is Microsoft’s object storage solution for the cloud. Let us first understand what a Blob is. The word ‘Blob’ expands to Binary Large OBject. Blob storage is optimized for storing massive amounts of unstructured data, such as text or binary data. Blob storage is ideal for: Serving images or documents directly to a browser.

It is a feature in Microsoft Azure that lets developers store unstructured data in Microsoft’s cloud platform. This data can be accessed from anywhere in the world and can include audio, video and text. Blobs are grouped into “containers” that are tied to user accounts. Blobs can be manipulated with .NET code.

Blob Service Hierarchy

The blob service is composed of the following components:

• Storage Account: This storage account can be either a General Storage account (V1 or V2) or a Blob Storage Account.
• Container: A container contains a group of blobs in which there can exist an unlimited amount of blobs. A mandatory requirement of a container is that its name should always be lowercase.
• Blob: A blob is a file of any size and type.

Blob storage allows Microsoft Azure to store arbitrarily large amounts of unstructured data and serve them to users over HTTP and HTTPS.

Microsoft’s use cases include serving streaming video, files, text and images to remote users. Azure allows users to store blobs in containers. A blob might be dedicated solely to video while another might store image files.

Microsoft defines three types of blobs: block blobs, append blobs and page blobs. Block blobs support up to 50,000 blocks up to 4 megabytes, with up to 195 gigabytes in total. Block blobs are intended for text and other binary files. Append blobs support appending operations and are designed for log files. Page blobs are designed for frequent read/write operations. Blobs are created and accessed with .NET code.

Why Use Azure Blob Storage

• Strong Consistency – Blob storage ensures the user has superior data integrity. The data accessed is always the latest version as it instantly updates changes made to an object.
• Object Mutability – The ability to edit a specific data object will improve overall performance while reducing bandwidth consumption.
• Multiple Blob Types -Having page blobs, block blobs and append blobs gives more flexibility to choose a storage option that suits your specific requirements.
• Geo-Redundancy – Ensures maximum availability by Geo-replication to enhance both local and global access which leads to maximum business continuity. Automatically configure Geo-replication options in a single menu, to easily empower enhanced global and local access and business continuity.
• Worldwide access -With rest-based object storage, the data stored in blob storage can be accessed from anywhere in the world with Azure’s regional data centers. It allows access to the right people at the right time without the need to worry about physical location.

Common uses of Blob storage include:

• Serving images or documents directly to a browser
• Storing files for distributed access
• Streaming video and audio
• Storing data for backup and restore, disaster recovery, and archiving
• Storing data for analysis by an on-premises or Azure-hosted service

The Azure Blob Storage Model: Overview

An Azure Storage Account will consist of one or more Containers, which are created and named by the user to hold Blobs. All blobs must be located in a container. In general (and at the time of this writing), an Azure user can have up to five separate storage accounts.

An individual storage account may contain an unlimited number of containers, and an individual container may hold an unlimited number of blobs. However, the total size of all containers may not exceed 100TB.

Windows Azure defines different types of blob

• Block blobs – Block blobs are for your discrete storage objects like jpg’s, log files, etc. that you’d typically view as a file in your local OS. Regular (non-Premium) storage only.
• Page blobs – Page blobs are for random read/write storage, such as VHD’s (page blobs are what’s used for Azure Virtual Machine disks). Supported by both regular and Premium Storage.
• Append Blobs: Append Blobs are Optimized for append operations. e. g. Logs

Blobs Access Tiers

Blobs also have the so-called access tiers. They may be hot, warm and cold storage types.

• Hot files are the ones, you store in the cloud to access a lot. Expensive to store, but cheap to access.
• Warm files are the ones, you store in the cloud, to access not that frequently. Less expensive to store files then in hot, more expensive to access
• Cold files are the ones, you store in the cloud to access once in months or years. Dirt cheap to store files, the most expensive type to access files

Microsoft has all three access tiers for the Blob Storage – Hot Tier, Cool Tier and Archive.

You cannot change access tier for Page Blobs. Access tier is applicable only for Append Blobs and Block Blobs. The limitation relates to the purpose and architecture of these storage types. You can change access tiers for files, but not disks.

There are tools to analyze blobs in a storage account and recommends potential cost savings when objects are moved between the “Hot”, “Cool” and “Archive” tiers.

This project framework provides the following features:

• Lists blobs in a blob container / all container in a storage account.
• Matches blobs against user-defined criteria for analysis.
• Recommends potential savings in monthly storage costs when objects are moved between the “Hot”, “Cool” and “Archive” tiers.
• Changes access tier of analyzed blobs.

We would discuss about how to upload Azure blob storage in our subsequent blogs.
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Why Enterprises and Start-ups leaning towards Automation Testing?

Why Enterprises and Start-ups leaning towards Automation Testing

Applications and products are made on a daily basis and they are tested thoroughly, still they have defects. Testers make every effort to catch them in advance prior to the release of the product/application. On the other hand, they always arise and come back, even with the finest manual testing procedures. To combat this situation, test automation software is the finest method to upsurge the efficiency, productivity and handling of your software testing. How to go about selecting the right tool fitting the parameters of your requirement? Let us delve into it to get a befitting solution to this problem.

Norms for tool selection for Automation Testing

With a stern competition and ever increasing business needs, there is a burden on IT firms to provide high-quality products using less resource, in less time. This quandary can be controlled by pondering on a few things listed below to yield huge benefits:

• First and foremost, you need to identify the testing tool as per your needs and financial plan.
• Secondly, based on the technology used for your application/product, you need to select the tool best suiting it.

Prior exercise and homework is mandatory to get the desired results and profits.

What is Automation Testing ?

Automation Testing is simply an automated version of the manual testing process presently in use. This type of testing is the use of plans, tools and things that increase or decrease the necessity of manual or human intervention.  Put simple, it is a procedure in which software tools implement pre-scripted trials on a software application before it is released into creation.

Plainly, such a procedure takes account of the following things:

• Comprehensive test scripts, as well as anticipated expected outcomes established from industry functional specifications and design documentation
• Also, it takes in a separate test setting, together with a test database that is restorable to a recognised constant, such that the test scripts can be repeated each time there are alterations made to the application/product.

We had earlier automated Unit Testing in our blog earlier. Do have a look.

Standards for building framework and does the selected tool for Automation Testing offer up to the mark results?

To automate any product or an application, the following factors must be well-thought-out.

• Data driven competences must be along with database authentication
• Fixing and sorting capabilities must be present
• Platform individuality
• Customizability factor
• The type must be control friendly
• It must be able to support unattended test trials
• Study of various systems and communication method must be done.

Develop the framework and select the tool based on the factors given above and the circumstances.

As we all know that, an automated testing tool is capable of replaying recorded and predefined test cases and compares the results to the predictable action and report the victory or fiasco of manual test cases to the testers. These generated automated test cases can simply be repeated and they can be stretched to complete responsibilities difficult with manual testing. Because of this, savvy managers have found that automated software testing is an essential component of successful development projects. For all these reasons, automated testing has definitely become an integral part of software development life cycle. On the other hand, the only downside of automated testing being that it is said to be too costly or challenging for smaller companies to implement every so often.

Automation Testing is considered important for small as well as big firms to implement with the intention of providing exceptional software or an application. However, it is somewhat tough to get started to stand out from the counterparts and stay competitive in the industry. Drawing an operative plan, constructing strong frameworks, picking out the correct tools, and evaluating the possible financial effect that automation possibly will have on your delivery lifecycle is necessary to think over. Prior to the start of any fruitful automated testing line of attack this exercise is necessary; on the other hand, each phase presents its individual trials and expenses.

Select the right tool and get a seamless application or a product. Leverage the mentioned things to help your team members and managers and get the desired results.

Significance of Automation Testing Tools – Are they worth the hype?

Automation Testing is simply executed when the manual testing procedure is not able to meet prospects and when it becomes impossible to bring in additional testers. Now, let us understand the myriad benefits that automated testing have to offer. The various benefits are as follows:

• The precious time of the testers get saved, mainly when carrying out regression testing.
• Even the most careful and meticulous tester can make errors in the course of repetitive manual testing. But automated testing allows the test cases to perform the similar stages specifically every time they are accomplished in providing complete results
• It also lets timely bug finding with an improvement in the quality of manual test cases.
• Also, the test cases prepared by the automated tools can be used for future purposes as and when the need appears.
• The test trials can be scheduled without any human intervention and you will get the required test cases or results.
• With distributed test implementation, automate testing test cases can be simply accomplished on multiple laptops/desktops on a shared network or server at the same time
• Each and every test case can be followed easily covering all the scenarios.
• The process is really fast, easy, well-organized and precise which in turn improves return on investment.
• In a nutshell, automate testing definitely saves a lot of time with an effective use of resources. With so many advantages, automated testing is certainly the finest approach to grasp testing objectives. Case in point, it is a mean to quality testing.

These are some of the key benefits as a single procedure is adapted during the course of the software or application building. This makes the job hassle free and much easier.

However, there are few pitfalls too; if automated testing is not done in the manner it should be done. Inappropriately, a lot of people slip-up test automation with testing. As soon as they select the tools to automate the testing, they want to automate all the test trials and want to throw out testers. It is an assessment use for sure. To get effective results you need domain information and must be able to learn the performance of the application in no time. You must be able to put on appropriate test methods to be able to spot inconsistencies in any application or software. It is all about using your expertise along with the selected tool performance in an appropriate manner. Though it has its share of ups and downs, still it is adapted extensively in the whole world. Also, it is recommended that firms must stay updated about testing upcoming developments to reap a lot of benefits.  They just need to add the validations linked to the developments to the current base structure and can keep going.

Conclusion

We all have seen the various advantages and the significance of testing and test automation across myriad domains. It is definitely an integral part of software development as it accelerates the process and the application/product can be launched in a smooth manner with practically no faults. For all the benefits which it has to offer it has slowly but surely gained popularity with more recognition and prominence in the software industry. On the other hand, it should be done in the approved manner to avail its profits advantages which would prove advantageous to the project and organization. One thing which must be kept in mind is that it is a leeway of human intelligence. Hence, it must be applied with care by observing and evaluating the product/application closely and sensibly. This is necessary to get the desired results. As a result, planning and choosing effectively is the key to meet the testing requirements as per the need and the selected tool.
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