by Huenei IT Services | Feb 5, 2024 | Data
Do you Know the Difference Between Data Engineering vs Data Science?
Belonging to the world of technology involves hearing many concepts that may sound similar to each other. And one of them may be data engineering vs. data science. Although they share some similarities, the reality is that there are many important differences between them.
For this reason, the purpose of this article is to inform you and let you know what each concept means. Read on and find out more about the difference between data engineering vs data science!
Data engineering vs Data Science: what are the similarities and differences between the two terms?
Well, to learn more about data engineering vs. data science, it is necessary to know that in the world of technology and data there are many professions and roles. Precisely, this is the main shared characteristic between both concepts: both the engineer and the data scientist are constantly working with large volumes of Big Data.
However, the difference is in the purpose. Engineers are in charge of extracting large volumes of information and organizing databases. On the other hand, data scientists perform visualization tasks, diagramming learning tasks, and patterns on the data previously extracted by engineers.
For this reason, the tools used by each tend to vary. In the case of data scientists, they usually use resources such as Deep Learning, Machine Learning, data processors (such as Spark), or programming tools such as R or Python. In this way, engineers use databases such as SQL and NoSQL, the Hadoop ecosystem, and tools such as Apache Airflow or Dagster.
It should be made clear that both are indispensable professions for any company that wants to take advantage of technology. However, this serves only as an introduction to the subject. For this reason, we recommend that you read on to find out more about each of these fields of work.
What does data engineering consist of?
Let’s specify a little bit the roles that are practiced in data engineering. According to Coursera, it is the practice of designing and building systems that collect and store large volumes of data. Therefore, the engineer is the person who is responsible for building and maintaining data structures for use in multiple applications.
The ultimate goal of the data engineer is to make all this data accessible for the organization to consider in decision-making. In other words, the idea is that this data is transformed into useful information that executives can use to maximize profits and see growth in the company.
It is for this reason that a data engineer must have advanced knowledge of databases. Likewise, as there is a trend towards cloud programming, he or she needs to know all these systems. This professional must also be able to work in conjunction with different departments, to understand the organization’s objectives.
So, it is key to understand that data engineers will not only need to be passionate about programming. They will also need to have communication skills, as they will be working in conjunction with other departments and professionals, as is the case with data scientists.
And what specifically is Data Science?
Now, you may want to know more details about data scientists, which is another of the most sought-after professions by companies in recent years. IBM considers that data science combines knowledge in mathematics, statistics, programming, and artificial intelligence, to make efficient decisions and improve the company’s strategic planning.
It should be noted that Data Science is not synonymous with Artificial Intelligence. In reality, a data scientist uses Artificial Intelligence to extract useful information from unstructured data. AI is a series of algorithms that mimic human intelligence to read and understand data, but it is the scientist who makes the final decision.
This situation means that the data scientist has to be a person with a strong sense of logic. Not only will they have to work by studying the behavior of the data, but they will have to understand what the company wants. For this reason, they must not only master statistical software and programming but also have a strong interest in market and company situations.
Similarly, it should be considered that the data scientist will not only obtain data from a single source, as a traditional data analyst would do. Here they will seek to have a global perspective of the problem. Although they will bring their subjectivity to include their point of view in the decision-making process, the objective data will reinforce their arguments.
In short, you have seen that understanding the difference between data engineering vs data science is not complicated at all. Both professions are essential to working with Big Data since taking advantage of large volumes of information is key to achieving great results in a company. We hope this article has cleared up your doubts!
by Huenei IT Services | Feb 5, 2024 | Cybersecurity
When designing software, some aspects need to be taken into account. For example, usability, aesthetics, and functionalities. But that’s not all: data and privacy must also be guaranteed. This means that personal data must be protected at all times. So, here we explain why this is so important!
Data and privacy: how do they influence software development?
Taking care of data privacy in the IT world is not an option: it’s a necessity. Thanks to the current digital transformation, more and more companies are asking for an application, a website, or any online structure to provide services. And a common mistake is to believe that only speed and efficiency in software development matter.
The “security” factor must also be guaranteed at every stage of development. Otherwise, cybercriminals can take advantage of these weaknesses, not only to generate problems in work processes but also to steal sensitive data that can cost millions of dollars.
And this is something that can be worked on from one area in particular: DevSecOps. According to IBM, it is the abbreviation for Development, Security, and Operations. So, it is a working practice that seeks to integrate security into each of the operations of software development, to make applications and services much more reliable.
DevSecOps is a natural evolution in the way organizations approach security. Thanks to this approach, potential problems can be prevented. In other words, by devoting just a few minutes or hours to security, you can save weeks or months of remediation.
A clear example has been Amazon, which mentions that it makes more than 50 million changes a year in its applications. Each one of them invests only a few minutes or hours. However, it saves weeks or months of work, as it avoids major corrections. In this way, they reduce their security problems by 50%.
Similar is the case with PayPal. With more than 400 million accounts and millions of annual transactions, it is necessary to ensure security at scale in all applications. This not only avoids scams but also consolidates the company as one of the leaders in online payments.
Benefits of ensuring good data privacy in software
Now, what are the advantages of ensuring data privacy in software development? Read on and find out.
Yes: cyber-attacks not only cause problems at the infrastructure level but can also result in millions of dollars in losses for companies. Due to lost productivity, remediation costs, and data breaches, companies can end up in crisis. For this reason, organizations can mitigate risks through the good development of each of their services.
But it is also necessary to delete these problems by backing up data in the cloud and distributing it across multiple servers. By ensuring that data is protected, cyber-attacks that cause financial crises can be avoided.
This is one of the specific benefits of the DevSecOps model. Automated security tests and checks can start to become part of all development phases. This situation results in a remarkable benefit, which is none other than having a higher level of CI/CD system security.
Thanks to these tests, the code that passes to the next stage will have an adequate level of security. All this is done in an automated way, generating collaboration between all the people on the team. For this reason, the SDLC (System Development Life Cycle) is usually much more efficient.
Ensuring data security at every stage of software development also allows for clear objectives. When all privacy policies are clear and the software complies with the appropriate security protocols, realistic expectations about the launch of a service are generated.
For example, a common mistake is for companies to rush to launch software onto the market that has not been tested in terms of security. This situation can lead to a computer attack resulting in the theft of private information, which can generate losses in the millions of dollars. Here we work from the beginning to ensure stability.
The importance of continuous work on data privacy
Finally, you must know that no software will ever be 100% secure. Hackers work day after day to perfect their information theft techniques. For this reason, there is no way to guarantee the invulnerability of your services. However, you can minimize this probability.
The way to do this is as simple as it is effective: by working continuously. If you have a team specialized in computer security, they will be able to check that all privacy standards are being met. If not, the necessary repairs can be made to ensure that the code developed is secure.
That’s it! We hope this article on data and privacy in software development has been of interest to you.
by Huenei IT Services | Feb 1, 2024 | Artificial Intelligence
A recent McKinsey study revealed groundbreaking productivity potential from pairing developers with generative AI tools. Test developers saw coding tasks completed up to twice as fast across refactoring, new feature building, and code documentation.
The gains come from generative AI supercharging developers in 4 key areas:
- Expediting manual and repetitive coding work through autocompletion and documentation
- Jump-starting new code drafting with on-demand suggestions
- Accelerating updates to existing code by easing edits
- Enabling developers to tackle unfamiliar challenges with framework guides and snippets
Leading AI coding assistants like GitHub Copilot, TabNine, and Codex allow developers to generate code snippets and entire functions through conversational prompts, drastically accelerating rote programming work. Developers retain oversight to evaluate quality and customize outputs. While focused on Python currently, experts predict advances across languages and platforms. Though optimal use cases differ. Java and C# projects have seen 10-30% shorter timelines leveraging automation for routine changes. Accelerated coding paves the way for faster release cycles, reduces costs, and frees up resources to focus on innovation. But responsible implementation is key amid rising adoption. Organizations must mitigate risks around data privacy, security vulnerabilities, and reputational impacts through governance policies and controls. Upskilling developers on generative AI best practices also improves experience, and retention while maximizing productivity gains. The future is bright for symbiotic human and AI collaboration in software engineering. With disciplined adoption, generative AI unlocks speed, cost savings, and creativity for transformative gains.
Testing First-Hand
So far, we have analyzed how the IT industry is leveraging AI to its advantage. But can we assure that everything described above is true? At Huenei, we incorporated the use of AI tools very early on. Given the promising landscape they offer and the technological revolution they entail, we could not refrain ourselves and had to give it a try. The incorporation of AI into our processes has helped streamline our and our client’s productivity. Through the use of Copilot, the autocomplete tool created by GitHub in partnership with OpenAI, we have managed to make code-writing tasks more efficient. Based on previously generated code, Copilot can autocomplete code lines or blocks. The decision to incorporate it was based on the good metrics achieved, with 40% of its Python suggestions being accepted by developers. It is important to keep in mind that developer intervention will always be necessary to avoid risks due to errors. AI has also assisted us in the process of executing unit tests, saving time and resources. Machine learning algorithms can analyze code and automatically generate test cases quickly, identifying possible scenarios and generating relevant data, reducing manual workload and accelerating the process. We have achieved optimization of unit testing by identifying areas of code prone to errors, allowing us to focus our efforts on critical flows. Similarly, code analysis provides us with recommendations on areas to expand testing coverage. By gathering and preparing test data, we have implemented a model that aligns with existing processes. The constant training and monitoring help guarantee risk mitigation. The results have been excellent. Leveraging intelligence represented an exciting opportunity to enhance the efficiency and quality of software development through automation, increasing the reliability of outcomes and reducing costs of the end product.
by Huenei IT Services | Dec 31, 2023 | DevOps
The Ultimate DevOps Toolset for your Business
Today, companies can draw on many valuable technology resources. However, perhaps the most obvious case is DevOps resources. This set of IT practices will allow you to work more efficiently. But which are the most successful tools? We will tell you in detail.
The best DevOps tools you need to know about
The first thing to point out is that this market is really interesting. According to a report by DZone, the DevOps market will generate about $6.6 billion by 2022. More and more organizations are implementing it at different scales, and you should be one of them.
In this sense, don’t you know what this concept means? Well, it encompasses all the practices, work philosophies, and tools that allow organizations to offer IT services in a more efficient way. By adopting this philosophy, customers can be served with quick corrections, gaining a competitive advantage in the market.
All this means that companies that incorporate this work culture, through different tools, are better able to meet their objectives. After all, they can optimize their products and services, with fast development processes. Do you want to know which are the best tools? We will tell you about them below.
Jenkins
Jenkins is an open-source automation server. But how does it help companies? Well, it’s easy: it serves to automate all software development processes. For this reason, this tool allows teams to monitor recurring tasks, integrate changes easily and identify problems quickly.
Jenkins allows you to use more than 100 plugins, which can be integrated with many current tools. This standalone program was written in Java and runs on Windows, Linux, or macOS. In addition, Jenkins can be configured via a simple web interface with integrated help.
Docker
Let’s continue with another resource that you can take advantage of. Docker is used by more than 11 million developers around the world. This tool allows you to build, package and deploy code simply and dynamically, to improve work productivity.
Docker eliminates all configuration activities, focusing on fostering team collaboration. In this sense, Docker allows developers to run in the development environment and operations teams to perform various tests and deployments.
It has several notable features in its favor. For example, Docker can use virtualization on the operating system to deliver containerized apps. In addition, it works with GCP and AWS, simplifying migration to the cloud. On the other hand, it also integrates seamlessly with other tools, such as GitHub or CircleCI.
Puppet
A puppet is an open-source tool for improving software configuration management through automation. This tool works to manage the different stages of the software lifecycle. For example, provisioning the IT structure, applying patches, and configuring software components.
Among its main features, Puppet is developed in C++, Clojure, and Ruby. For this reason, it runs smoothly on Windows, Linux, and Unix. It also uses declarative language, which is used to define the system configuration. And, to top it off, it reduces manual errors, allowing your team to scale IT infrastructure.
Apache Maven
Developed in Java, Apache Maven is used for projects that have also been created in Java. And what does it work for? Well, simple: it seeks to manage and understand projects. It helps in the construction, reporting, and documentation of the various projects.
Apache Maven has predefined targets for compiling and packaging code. You can also download Maven plugins and Java libraries so that the development process is as fast and efficient as possible. In addition, you have automatic updates and dependency closures.
Bamboo
Bamboo is also used to link builds, releases and automated testing. All in one workflow! Thanks to this tool, you can create multi-stage build plans.
You have two versions: an open source one and a paid one. If you develop an open-source project, there is no need to pay. On the other hand, for commercial organizations you’ll need to purchase subscriptions. Anyway, its intuitive user interface, auto-complete features and automation processes make it worth the investment.
Gradle
Finally, you can also take advantage of Gradle. It allows you to speed up the productivity of all software developments. Gradle is built in Java, Kotlin, and Groovy, and is used to automate different aspects of projects. For example, software development, testing, and deployment.
Gradle has a very advanced ecosystem of integrations, in addition to different plugins that allow systematizing the software delivery throughout the entire life cycle. Gradle allows scaling the development through fast builds, and it is so versatile that it can be used by large companies and also by startups.
In short, you have seen that there are many DevOps tools that you can take advantage of for your business. As you have seen, they will help you to achieve better and better results, and we hope this article has been of great help to you!
by Huenei IT Services | Dec 31, 2023 | Data, Software development
Data is a vital resource for any organization. Managing business data requires a careful and standardized process. We have already discussed in previous articles the life cycle of data and how it can help your company in making business decisions. This is why today we propose to take another step into the world of data and understand what types of data companies like yours work with.
Database management problems are often related to tight behaviors in the organization. That is to say, inconveniences with the treatment of the data that arise from the use of outdated, inefficient technologies that consume many organizational resources. This translates into a high dependency between the programs used and the data, little flexibility in administration, difficulty in sharing data between applications or users, data redundancy, and poor information security.
But even in advanced technology companies, it is common to find the same limitation: staff does not understand the types of data they are working with and have difficulty transforming the data into key knowledge relevant for decision making. And with the advancement of Big Data in companies, these problems represent a loss of value for customers, employees, and stakeholders.
Data in companies: different structures.
Everyday companies collect (and generate) a lot of data and information. With the advancement of technology, data that lacks a defined structure became accessible and of great use for making business decisions; years ago, it was almost impossible to analyze these data in a standardized and quantitative way. Let’s see what the alternatives we face are:
- Structured data. They are traditional data, capable of being stored in tables made up of rows and columns. They are located in a fixed field of a specific record or file. The most common examples are spreadsheets and traditional databases (for example, databases of students, employees, customers, financial, logistics…).
- Semi-structured data. These do not follow a fixed and explicit scheme. They are not limited to certain fields, but they do maintain markers to separate items. Tags and other markers are used to identify some of its elements, but they do not have a rigid structure. We can mention XML and HTML documents, and data obtained from sensors as examples. Some other not-so-traditional examples that we could mention are the author of a Facebook post, the length of a song, the recipient of an email, and so on.
- Unstructured data. They are presented in formats that cannot be easily manipulated by relational databases. These are usually stored in data lakes, given their characteristics. Any type of unstructured text content represents a classic example (Word, PowerPoint, PDF files, etc.). Most multimedia documents (audio, voice, video, photographs) and the content of social media posts, emails, and so forth, also fall into this category.
How do I structure my data?
Beyond the level of structure discussed above, it is essential to your organization’s data management process that you can standardize its treatment and storage. For that, a fundamental concept is that of metadata: data about data. It sounds like a play on words, but we mean information about where data is used and stored, the data sources, what changes are made to the data, and how one piece of data refers to other information. To structure a database we have to consider four essential components: the character, the field, the record, and the file. So we can understand how our data is configured …
- A character is the most basic element of logical data. These are alphabetic, numeric, or other-type symbols that make up our data. For example, the name PAUL consists of four characters: P, A, U, L.
- The field is the grouping of characters that represents an attribute of some entity (for example, data obtained from a survey, from a customer data management system, or an ERP). Continuing with the previous example, the name PAUL would represent a complete field.
- The record is a grouping of fields. Represents a set of attributes that describe an entity. For example, in a survey, all responses from Paul (a participant) represent one record (also known in some cases as a “row”).
- Last but not least, a file is a group of related records. If we continue with Paul’s example, we could say that the survey data matrix is an example file (whether it is encoded in Excel, SQL, CSV, or whatever format it is). Files can be classified based on certain considerations. Let’s see some of them:
| The application for which they are used (payroll, customer bases, inventories …). |
| The type of data they include (documents, images, multimedia …). |
| Its permanence (monthly files, annual sets …). |
| Its possibility of modification (updateable files –dynamic, modifiable-, historical –means of consultation, not modifiable). |
As you have seen, the world of data is exciting and you can always continue learning concepts and strategies to take advantage of its value in your organization. To close this article and as a conclusion and example of the value of data for companies, we want to invite you to learn about a project in which we work for one of our clients. The General Service Survey that we develop for Aeropuertos Argentinos is an application of the entire life cycle of data (from its creation to its use) and is fed with data of different levels of structure. It is about the development of a platform to carry out surveys to visitors and employees, together with the analysis and preparation of automated reports. Don’t miss this case study!
