by Nexlogica 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 Nexlogica 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 Nexlogica 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 Nexlogica 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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by Nexlogica Team | Aug 15, 2022 | Uncategorized
An API is a set of rules that determine how apps or devices communicate and connect with each other. Since APIs help developers communicate with the data, they become more comfortable and easier for developers. REST APIs (an application programming interface) must be well-designed; else, they can create many difficulties for developers rather than enhancing the user experience. This is why REST API best practices must be followed when it comes to catering to your clients with the most efficiency.
Here are some methods to follow while designing and developing REST APIs:
- Clear and Concise Documentation
You must have complete and clear documentation. Oftentimes, documentation is produced automatically depending on the API definition. Otherwise, you will have to ensure that the documentation can be understood easily by people with less or no experience.
- Utilizing JSON as a Data Format
JSON is the most commonly utilized data format, although you can send data in other formats like CSV, XML, and HTML. JSON syntax can make data easy to read for humans. It is easy to use and offers quick and easy data assessment and execution. Moreover, it contains an extensive array of supported browser compatibility.
This practice enables developers to make changes in particular actions or the data structure. You may deal with more than one API version if your project increases with time and in size. But the benefit is that this enables developers to create more enhancements and changes in their service alongside holding a part of API users that are slow in accepting new changes or not ready to change.
Errors should be smartly managed to reduce confusion for every API user. This returns the HTTP response codes that explain the nature of the mistake that occurred. The API maintainers get ample data from it to assess the source and reason behind the issue.
Here are some basic error HTTP status codes:
404 Not Found – This means that there are no resources.
403 Forbidden – This implies that an improper user has no permission to use a resource even if he/she gets verified.
401 Unauthorized – This means that the user is not authorized to employ a resource. Generally, it goes back if a user does not get verified.
400 Bad Requests – This implies that the client-side input has been unsuccessful in documentation or validation.
503 Service Unavailable – This marks that something unnecessary and unexpected action occurred on the server-side; for example, system failure, part failure, server overload, etc.
502 Bad Gateway – This denotes a null or invalid response from a crucial server.
500 Internal Server Error – It’s a basic server error.
Using present security frameworks like TLS and SSL is another great practice for creating APIs. SSL certificates can create a secure connection by offering a private and public key. Without this encrypted connection, you cannot get an assurance that you are safeguarding sensitive data like financial or medical info properly. TLS is SSL’s most modern version that provides improved security and protection. Regular testing is one of the essential API security best practices.
- Allowing Data Filtering, Sorting, Field Selection, and Paging
Retrieving just the data that was asked for without showcasing the whole database is one of the most challenging aspects for making sure a secure connection with API. You must use a filter for doing this so it can just return the data that meets the request.
REST API provides a variety of filtering options:
Filtering – This helps check results using particular search parameters like country, creation data, etc.
Sorting – This enables you to sort out the results in ascending or descending format using your chosen parameter like dates.
Field Selection – This feasible REST API development function enables developers to ask for just a particular of the accessible data for a specific object.
Paging – Use ‘limit’ for checking the results in a particular number. Moreover, it uses ‘offset’ for informing which section of the whole results is showcased.
- Optimizing for Human Readers
APIs must be easy to understand and use. Apart from using JSON, you can use some other things to make APIs easy to use and understand:
Utilize clear and easy naming systems with no abbreviation.
Utilize nouns rather than verbs in HTTP methods.
Have easy-to-understood and simple descriptions for error management, along with standardized error codes.
Utilize plural nouns for collections according to the accepted norms.
- Keeping Resource Nesting Limited
Resource nesting helps pair two functions that share a similar hierarchy or are associated with each other. If you consider an online store as an example, ‘orders’ and ‘users’ are resources under a similar category.
Nesting is an effective practice for the relevant pairing of resources. However, many developers overuse it, which reduces its appeal.
A few safe tactics are HTTP methods that restore the precise resource representation. HEAD, GET, OPTIONS, and TRACE strategies are considered safe. This implies that they can usually retrieve data without modifying a resource’s condition on the server. Furthermore, avoid using GET for erasing content.
Use caching, rather than asking for data several times. The benefit of caching is that users can receive data more quickly. However, the users may get outdated data also. Moreover, this may cause issues while fixing in production environments if something wrong occurs as we see outdated data constantly.
You can read more about REST API Development here.
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