3 Indications to Update Your Software

Our lives are busy, and so is our technology; but sometimes our software and technology will slow down, and not perform as it used to when it was brand new, which can sometimes be aggravating for a business owner.

However, choosing to update your software to the newest version can often be difficult, as it could be pricey, and also exhausting trying to figure out a new version. BUT, keeping older application software can also take time and money the longer you put off updating.

Not upgrading your software will also lag the proficiency on how much work you can get done. It can also change the security settings, which could cause your software exposure to be exposed to unwanted information.

With that, here are 3 indications on when you know it’s time to upgrade:

1. Dysfunctional Language

Having the ability to recognize that your e-commerce business is no longer keeping up with the market and your competitors are surpassing you with their system, is a good indicator that it’s time for you to look into upgrading your software.

Warnings:

• Software Updates – that are slow or not evening happening, when your software isn’t being upgraded regularly, your security system can be affected. Not having an upgraded software won’t allow you to see the new techniques and performance will begin to lag.

• Getting support for your software will take longer, which will be a sign that your server won’t be accessible to programmers who know your specific platform or language.

• You’re starting to have problems meeting basic business requirements, which could mean maximizing your platform is limited and the server doesn’t have professionals to assist your needs.

2. Losing Control

One company is hired by a small business to create a software solution to keep tabs on their sales and production processes.

However, the application software is compacted with bugs and glitches not meeting the needs of the company, because the software is hosted by an outsourced server, you then have no control or access to it.

Warnings:

• Not having immediate access to your own software data or code – it could be on your premise in the cloud. A good server should NEVER keep your business data captive.

• Even if it’s not old, your software isn’t meeting the business needs – if you try to find a solution, but it’s not working out because your server isn’t helping, it’s an indication it’s time to find a new server.

• When your solution continues to have bugs and glitches – that are not being corrected, SERVERS should be working quickly to solve the issues, not neglecting it.

• The server is SLOW and not responding or neglecting your requests – which can often show limited professionalism, and expertise. Whatever the case, a company should be actively trying to find someone who is capable. 

3. Administration Slow Downs

Having a new company launch a cloud-based solution more than 10 years ago – that was once an industry leader, but is now slacking because of lagging software, even when they’ve done minor upgrades here and there. This is not what you want in an industry leader. 

Warnings:

• When you have daily customer complaints – about your system appearing dated and not responding to multiple devices.

• When you begin to compete in the industry – despite having irresistible features. If users think it doesn’t look good then they won’t promote your software to find clients, they will go for another more appealing company.

• Your overall software just slows down – when you add new features to try and make it better it ultimately makes it worse because of the older internal system.

Moral of the story is to ensure that you’re not only keeping up with the aesthetics of your software and applications, but also staying on top of upgrading the internal system. 

If you need assistance with your internal servers, feel free to reach out!

 

 

Thorough Analysis of Big Data Frameworks: Spark vs. Hadoop MapReduce

Choosing the right big data framework is a challenge, especially since there are many available on the market. Examining each framework from the perspective of certain needs is probably the best bet for your business, rather than comparing the pros and cons of each platform.

Our big data consulting practitioners compare two leading frameworks to address a lingering question that many people have wondered. Between Hadoop MapReduce or Spark, which framework should you choose? Let’s dive in!

Checking Out Market Situations

Taking a quick glance at the market situation, we know that both Hadoop and Spark are the flagship products in big data analytics, with Hadoop leading the market for more than 5 years. Both frameworks are open-source projects by the Apache Software Foundation. 

The user base of Hadoop amounts to 50,000+ clients according to our market research, while Spark possesses 10,000+ installations. In 2013, Spark’s popularity surpassed Hadoop in only a year. A recent growth rate for installations indicates that the trend is still ongoing. Spark correspondingly outperforms Hadoop with 47% vs. 14% (2016/2017).

Main Distinction Between Frameworks

The main difference between Hadoop MapReduce and Spark, in fact, resides in the processing approach. While Hadoop MapReduce needs to read from and write to a disk, Spark can simply do it in-memory. There is a significant difference in the speed of processing as a result from this. 

Spark potentially could be up to 100 times faster. You also have to consider the volume of data processed since that differs between the two frameworks. Hadoop MapReduce is able to operate with much larger data sets as opposed to Spark.

What tasks are each framework good at? Let’s take a closer look.

The Good About Hadoop MapReduce

Huge Data Sets – Linear Processing: 

Hadoop MapReduce permits massive amounts of data to be processed in a way where two or more processors (CPUs) handle separate parts of an overall task. This is known as parallel processing. 

Large chunks of data are broken down into smaller pieces that are processed separately on different data nodes. The results from multiple nodes automatically get gathered to return a single result. Hadoop MapReduce may outperform Spark if the resulting dataset is larger than the RAM available.

If Speed Doesn’t Matter, This Is For You: 

If the pace of processing isn’t crucial for your business, then Hadoop MapReduce is considered to be a good solution. It makes sense to suggest using Hadoop MapReduce if data processing can be done during the night hours.

The Good About Spark

Speedy Data Processing: 

Spark is faster than Hadoop MapReduce as a result from in-memory processing. Up to 100x faster for data in RAM and up to 10x faster for data in storage.

Iterative processing: 

Spark defeats Hadoop MapReduce if the assignment is to process data repeatedly. Resilient Distributed Datasets (RDDs) by Spark authorize multiple memory mapping operations. As compared to Hadoop MapReduce, it must write interim results to a disk.

Processing on-the-fly: 

Businesses should opt for Spark and its in-memory processing if it needs immediate insights.

Processing Graphs: 

For iterative computations that are common in graph processing, Spark's computational model is great! Plus, Apache Spark has GraphX, an API for computing graphs.

Machine Learning: 

Spark has a built-in library for machine learning that has out-of-the-box algorithms which run in memory. This function is called MLib. Hadoop needs a third-party to provide a machine learning library.

Combining Datasets: 

Spark can generate all combinations faster because of its speed. However, Hadoop may be better if it’s necessary to join very large data sets that require a lot of shuffling and sorting.

Practical Application Cases

Thanks to near real-time processing, Spark is likely to outperform MapReduce after examining several examples of practical applications. Let’s look at the examples.

Customer Dissection: 

To create a distinctive customer experience, businesses need to have an understanding of customer preferences. To help with this, customer behavior should be analyzed while identifying segments of customers that demonstrate similar behavior patterns.

Risk management: 

By selecting non-risky options, predicting various future scenarios can help managers make right decisions.

Fraud detection in Real-Time: 

By using machine-learning algorithms, the system would be trained on historical data where then these findings can be used to detect or predict an anomaly in real time that may indicate a potential fraud.

Industrial Big Data Analysis: 

It's all about identifying anomalies and predicting them, but these anomalies are connected to machinery breakdowns in this case. To detect pre-failure conditions, a correctly designed system collects the data from sensors.

Hmmm, What To Choose?

To determine which framework you should choose, the needs of your business will help guide you to make a final decision. Hadoop MapReduce has an advantage when it comes to linear processing huge datasets. Spark is fast, efficient and provides real-time analytics, graph processing, machine learning, and much more! One last thing that might change your mind, Spark is fully compatible with the Hadoop ecosystem.

To gain more insight on making your decision, get in contact with us.

 

 

GitHub vs GitLab: Which is Best for You?

An important aspect of the software development lifecycle is repository management. A Git repository is where you can collaborate, test, share, store web projects, and code. Since Git is distributed, you can have local repositories. This allows you to work on your code without having Internet access.

Using the right repository for your project is important for accelerating your software development initiative and efficiency. 

In this post, we’ll discuss two different Git repository managers - GitHub and GitLab to help you understand which fits your project best.

Let’s dive right in!

What is GitHub?

GitHub is a cloud-based repository management hosting service that provides a Web-based graphical interface. It serves as a hosting site where web developers (novice programmers and seasoned engineers) can work reciprocally, upload, and improve the code they create.

Additionally, it offers a robust version control system, which allows for consistent collaboration without jeopardizing the integrity of the original project. You can use GitHub for both public and private projects.

What is GitLab?

This is an open-source code repository, as well as a collaborative development platform. It offers a location for code storage and collaborative development of projects. Like GitHub, GitLab also offers version control that allows users to check previous code. GitLab supports both public and private development branches and offers features for bug tracking and project management. 

Similarities Between GitHub and GitLab

Since both are developed on the same Git basis of version control, their functions are similar. 

Third-party Integrations:

Both GitHub and GitLab offer a wide range of third-party integrations. Integrating your version control system with other applications enriches your workflow and can boost productivity for your developers and other employees connected to the software.

Tracking:

GitHub, as well as GitLab, offers a simple issue tracker that lets you change status and assign owners accordingly. Both of them have great reporting tools, including bug reporting and user feedback that can be accessed instantly.

Labels: 

Both utilize a simple system of labeling that allows you to assign informative titles to easily categorize issues, merge requests, and epics. 

Issues: 

Both GitHub and GitLab offer features like setting the issue status, assignees, milestones, and they can each be filtered without a challenging process.

Enterprise solutions: 

Both GitHub and GitLab offer enterprise solutions for businesses.

Differences Between GitHub and GitLab

Open Source: 

Both are open-source platforms and they both provide free private repositories for open-source projects. However, GitHub allows you to have unlimited collaborators and unlimited repositories, while GitLab allows an unlimited number of users with unlimited free private repositories. 

Authentication Level: 

With GitHub, you can decide who gets reading or writing access to your repositories. While in GitLab, users have different levels of access based on their roles. 

Import/Export Features:

GitHub is more restrictive when it comes to import and export features of existing GitHub repositories as it does not provide step-by-by documentation. On the contrary, GitHub provides a GitHub importer tool to make importing and exporting easy. 

GitLab offers quite extensive documents on how to import and export data from external sources including GitHub, Bitbucket, and any GIT URL. Also, GitLab allows you to export projects to other systems.

GitLab Vs GitHub Enterprise:

The decision to use either GitLab or GitHub is highly dependent on the project and organization.

GitLab enterprise is significantly cheaper compared to GitHub. If you are operating on a tight budget, GitLab is a preferred option. It also provides a feature-rich experience. 

Still, GitHub with its established strong market position is highly popular among larger development teams and organizations. 

GitHub Vs GitLab Performance:

GitHub focuses more on high availability and infrastructure performance, whilst delegating other functionalities to third-party tools. Meanwhile, GitLab puts more emphasis on providing maximum features in a robust platform for end-to-end development management.

GitLab CI vs GitHub Actions:

One of the differences between GitLab and GitHub is the built-in CI of GitLab. GitLab provides its CI for free. It has been addressing the DevOps market earlier than its competitor, as well as offering an operation dashboard that lets you understand the dependencies of your development and DevOps efforts. 

Additionally, GitHub released Actions in 2019. Actions allow you to write tasks that automate and customize the development workflow. But it does not come with a deployment platform and needs additional applications. 

Wrapping It Up

Both GitLab and GitHub are web-based repository managers that allow collaborating on code. GitLab has amazing and unique features that allow you to go from development to cloud without necessarily having to use other third-party tools. GitLab has lots of features. On the other hand, GitHub is trusted by many developers. It provides you with a larger number of integrations and offers collaboration tools.

GitHub Actions makes development faster and easier. But for the DevOps lifecycle, GitLab ranks better with its built-in CI/CD framework and monitoring features.

Your choice of repository management platform depends on the objectives you need to achieve and what suits your needs best.

Get in touch with us to discuss more.