by Teknita Team | Aug 24, 2022 | Uncategorized
Choosing between Visual Studio Code and Visual Studio is not so simple. While Visual Studio Code is highly configurable, Visual Studio is highly complete. The choice may depend as much on your work style as on the language support and features you need.
Let’s take a look at the capabilities of these two development tools.
Visual Studio Code
Visual Studio Code is a lightweight but powerful source code editor that runs on your desktop and is available for Windows, macOS, and Linux. It comes with built-in support for JavaScript, TypeScript, and Node.js and has a rich ecosystem of extensions for other languages (such as C++, C#, Java, Python, PHP, and Go) and runtimes (such as .NET and Unity).
Aside from the whole idea of being lightweight and starting quickly, VS Code has IntelliSense code completion for variables, methods, and imported modules; graphical debugging; linting, multi-cursor editing, parameter hints, and other powerful editing features; snazzy code navigation and refactoring; and built-in source code control including Git support. Much of this was adapted from Visual Studio technology.
VS Code proper is built using the Electron shell, Node.js, TypeScript, and the Language Server protocol, and is updated on a monthly basis. The extensions are updated as often as needed. The richness of support varies across the different programming languages and their extensions, ranging from simple syntax highlighting and bracket matching to debugging and refactoring.
The code in the VS Code repository is open source under the MIT License. The VS Code product itself ships under a standard Microsoft product license, as it has a small percentage of Microsoft-specific customizations. It’s free despite the commercial license.
Visual Studio
Visual Studio (current version Visual Studio 2022, which is 64-bit) is Microsoft’s premier IDE for Windows and macOS. With Visual Studio, you can develop, analyze, debug, test, collaborate on, and deploy your software.
On Windows, Visual Studio 2022 has 17 workloads, which are consistent tool and component installation bundles for different development targets. Workloads are an important improvement to the Visual Studio installation process, because a full download and installation of Visual Studio 2022 can easily take hours and fill a disk, especially an SSD.
Visual Studio 2022 comes in three SKUs: Community (free, not supported for enterprise use), Professional ($1,199 first year/$799 renewal), and Enterprise ($5,999 first year/$2,569 renewal). The Enterprise Edition has features for architects, advanced debugging, and testing that the other two SKUs lack.
Visual Studio or Visual Studio Code
If your development style is test-driven, Visual Studio will work right out of the box. On the other hand, there are more than 15 test-driven development (TDD) extensions for VS Code supporting Node.js, Go, .NET, and PHP. Similarly, Visual Studio does a good job working with databases, especially Microsoft SQL Server and its relatives, but VS Code has lots of database extensions. Visual Studio has great refactoring support, but Visual Studio Code implements the basic refactoring operations for half a dozen languages.
There are a few clear-cut cases that favor one IDE over the other. For instance, if you are a software architect and you have access to Visual Studio Enterprise, you’ll want to use that for the architecture diagrams. If you need to collaborate with team members on development or debugging, then Visual Studio is the better choice. If you need to do serious code analysis or performance profiling, or debug from a snapshot, then Visual Studio Enterprise will help you.
VS Code tends to be popular in the data science community. Nevertheless, Visual Studio has a data science workload that offers many features.
Visual Studio doesn’t run on Linux; VS Code does. On the other hand, Visual Studio for Windows has a Linux/C++ workload and Azure support.
For daily bread-and-butter develop/test/debug cycles in the programming languages supported in both Visual Studio and VS Code, which tool you choose really does boil down to personal preference.
You can read more about Visual Studio and Visual Studio Code here.
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by Teknita Team | Aug 22, 2022 | Process Automation
OLAP (online analytical processing) is software for performing multidimensional analysis at high speeds on large volumes of data from a data warehouse, data mart, or some other unified, centralized data store. High-speed analysis can be accomplished by extracting the relational data into a multidimensional format called an OLAP cube; by loading the data to be analyzed into memory; by storing the data in columnar order; and/or by using many CPUs in parallel (i.e., massively parallel processing, or MPP) to perform the analysis.
OLAP CUBE
The core of most OLAP systems, the OLAP cube is an array-based multidimensional database that makes it possible to process and analyze multiple data dimensions much more quickly and efficiently than a traditional relational database. Analysis can be performed quickly, without a lot of SQL JOINs and UNIONS. OLAP cubes revolutionized business intelligence (BI) systems. Before OLAP cubes, business analysts would submit queries at the end of the day and then go home, hoping to have answers the next day. After OLAP cubes, the data engineers would run the jobs to create cubes overnight, so that the analysts could run interactive queries against them in the morning.
The OLAP cube extends the single table with additional layers, each adding additional dimensions—usually the next level in the “concept hierarchy” of the dimension. For example, the top layer of the cube might organize sales by region; additional layers could be country, state/province, city and even specific store.
In theory, a cube can contain an infinite number of layers. (An OLAP cube representing more than three dimensions is sometimes called a hypercube.) And smaller cubes can exist within layers—for example, each store layer could contain cubes arranging sales by salesperson and product. In practice, data analysts will create OLAP cubes containing just the layers they need, for optimal analysis and performance.
OLAP cubes enable four basic types of multidimensional data analysis:
Drill-down
The drill-down operation converts less-detailed data into more-detailed data through one of two methods—moving down in the concept hierarchy or adding a new dimension to the cube. For example, if you view sales data for an organization’s calendar or fiscal quarter, you can drill-down to see sales for each month, moving down in the concept hierarchy of the “time” dimension.
Roll up
Roll up is the opposite of the drill-down function—it aggregates data on an OLAP cube by moving up in the concept hierarchy or by reducing the number of dimensions. For example, you could move up in the concept hierarchy of the “location” dimension by viewing each country’s data, rather than each city.
Slice and dice
The slice operation creates a sub-cube by selecting a single dimension from the main OLAP cube. For example, you can perform a slice by highlighting all data for the organization’s first fiscal or calendar quarter (time dimension).
The dice operation isolates a sub-cube by selecting several dimensions within the main OLAP cube. For example, you could perform a dice operation by highlighting all data by an organization’s calendar or fiscal quarters (time dimension) and within the U.S. and Canada (location dimension).
Pivot
The pivot function rotates the current cube view to display a new representation of the data—enabling dynamic multidimensional views of data. The OLAP pivot function is comparable to the pivot table feature in spreadsheet software, such as Microsoft Excel, but while pivot tables in Excel can be challenging, OLAP pivots are relatively easier to use (less expertise is required) and have a faster response time and query performance.
You can read more about OLAP here.
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by Teknita Team | Aug 19, 2022 | Uncategorized
James Dixon described the data lake:
If you think of a data mart as a store of bottled water—cleansed and packaged and structured for easy consumption—the data lake is a large body of water in a more natural state. The contents of the data lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples.
A data lake is essentially a single data repository that holds all your data until it is ready for analysis, or possibly only the data that doesn’t fit into your data warehouse. Typically, a data lake stores data in its native file format, but the data may be transformed to another format to make analysis more efficient. The goal of having a data lake is to extract business or other analytic value from the data.
Data lakes can host binary data, such as images and video, unstructured data, such as PDF documents, and semi-structured data, such as CSV and JSON files, as well as structured data, typically from relational databases. Structured data is more useful for analysis, but semi-structured data can easily be imported into a structured form. Unstructured data can often be converted to structured data using intelligent automation.
Data lake vs data warehouse
The major differences between data lakes and data warehouses:
- Data sources: Typical sources of data for data lakes include log files, data from click-streams, social media posts, and data from internet connected devices. Data warehouses typically store data extracted from transactional databases, line-of-business applications, and operational databases for analysis.
- Schema strategy: The database schema for a data lakes is usually applied at analysis time, which is called schema-on-read. The database schema for enterprise data warehouses is usually designed prior to the creation of the data store and applied to the data as it is imported. This is called schema-on-write.
- Storage infrastructure: Data warehouses often have significant amounts of expensive RAM and SSD disks in order to provide query results quickly. Data lakes often use cheap spinning disks on clusters of commodity computers. Both data warehouses and data lakes use massively parallel processing (MPP) to speed up SQL queries.
- Raw vs curated data: The data in a data warehouse is supposed to be curated to the point where the data warehouse can be treated as the “single source of truth” for an organization. Data in a data lake may or may not be curated: data lakes typically start with raw data, which can later be filtered and transformed for analysis.
- Who uses it: Data warehouse users are usually business analysts. Data lake users are more often data scientists or data engineers, at least initially. Business analysts get access to the data once it has been curated.
- Type of analytics: Typical analysis for data warehouses includes business intelligence, batch reporting, and visualizations. For data lakes, typical analysis includes machine learning, predictive analytics, data discovery, and data profiling.
You can read more about Data Lake here.
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by Teknita Team | Aug 18, 2022 | Uncategorized
ERP is a mission-critical application that connects all operations, from sales and customer management to inventory and finance. It provides decision-makers with the desired visibility and enhances collaboration across teams. ERP systems must perform faster and handle more capacity. Must support new technologies such as Machine Learning, Artificial Intelligence, Digital Assistants and more. A cloud-based ERP can help organizations achieve the same, which makes it imperative for them to modernize their ERP by migrating it to the Cloud.
There are six effective approaches, commonly known as “The 6 R’s of Cloud Migration”.
1. REHOST (i.e. Lift and Shift)
The essence of “Lift and Shift” is to quickly enjoy the CAPEX and OPEX and other benefits of Cloud IaaS. This is like a getting out of the data center which leads to significant cost savings on valuable office space and the amounts of money spent to avoid overheating/maintenance of the data centers.
2. REPLATFORM (i.e. Lift, Thinker, Shift)
Replatforming is the middle ground between three approaches wherein the code is not altered excessively. However, replatforming involves slight changes to the code for the purpose of taking advantage of the new cloud infrastructure. This is a good strategy for organizations that want to build trust in the Cloud while achieving benefits such as increased system performance.
3. REFACTOR
Refactoring involves rebuilding or redeploying the application using cloud-native features. Unlike “Lift and Shift”, a refactored application not only pulls data from cloud storage for analysis but also completes its analytics and computations within the Cloud. Companies that choose to refactor will reuse already existing code and frameworks, but run their applications on a PaaS (Platform-as-a-Service) as done in case of rehosting.
4. REPURCHASE
Repurchasing means moving to a different product. Simply put, organizations can opt to discard their legacy applications altogether and switch to already-build SaaS applications from third-party vendors.. This is cost-effective strategy, but commercial products offers less customization.
5. RETIRE
Retire means that application is explicitly phased out. In case your ERP fails the Cloud feasibility assessment, you must take a call to simple retire it and probably implement a SaaS based ERP.
6. RETAIN
This means “do nothing for now, and revisit later”. If you are unable to take data off premises for compliance reasons, then you must revisit cloud migration when you overcome the challenges or when the required compliance mandates have been received.
You can read more about The 6 R’s of Cloud Migration Strategy here.
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by Teknita Team | Aug 16, 2022 | Uncategorized
Cloud migration is the process of moving applications, data, or even the whole enterprise IT infrastructure to the remote server facilities and a virtual environment. The advantages of cloud migration are notable. The cloud architecture enables accepting any workload, and the ease of including new services offers a chance of fast responding to changing business needs.
Here are some advantages that cloud computing offers to businesses:
Moving to the cloud is cost-effective for organizations. Companies can save a lot by cloud migration, particularly in the long run. In comparison with on-premise hardware, you have no upfront investment with the cloud. Also, the energy expenses for keeping the systems up become affordable. Moreover, you don’t need to pay somebody for maintaining your hardware, because your cloud provider does everything. You just pay for what you utilize and nothing more than that.
You might experience capacity issues while using the on-premise infrastructure. However, using cloud technology, you can get rid of the capacity problems completely. Cloud service providers provide businesses with on-demand capacity utilizing a pay-as-you-go model. Hence, no seasonality or development will threaten for upending your operation. Using the cloud, businesses now can modify the storage, level of computing power, and bandwidth required at any time.
Enhanced security is one of the advantages of cloud migration. Cloud service providers make sure to maintain the most precise security measures for their clients. From authentic digital protections to high-end physical ones, they prioritize cybersecurity first. The data centers and some top cloud providers globally safeguard your data. They can employ the brightest and best cybersecurity professionals available. This helps them increase their knowledge of enhancing their security practices continuously and offers a secure space for their client data.
- More Flexibility for Employees
The cloud helps you allure and retain your staff members, providing them improved flexibility. Many employees want the capacity for traveling and working remotely instead of working from 9 to 5 in the office space. And guess what, they can do that with cloud technology. As long as your staff members have an active internet connection and a device, they can work using the capacity for enhanced collaboration offered by the cloud. This is freedom for the employees.
Collaboration means competitiveness and efficiency these days. Companies can embrace a lot of technologies for increasing their collaboration, and the cloud is one of such technologies. Since everything is available through the internet, staff members can work together in various states, cities, or nations. Employees can access files and documents at the same time and update them in real-time. And this capacity of collaborating easily helps boost proficiency. By leveraging cloud technology, you can focus on collaboration more easily for your staff to work together and create better concepts and solutions quicker than before.
With cloud technology, businesses can back up their data. Some experts say that cloud backups are more secure than internal backups. Using cloud-based backups, you can store your data safely in high-end data centers run by the tech giants globally. These companies have several teams working 24×7 for securing your data. When your data is secure, you won’t experience data loss. In case your system gets destroyed due to a natural disaster, a cloud-based backup will assist in your disaster recovery.
You can read more about Cloud Migration here.
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