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Data exploration covers collection, processing, storage, and analysis. Extracting valuable insights from the collected data informs decision-making processes and enhances overall business intelligence.

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DATA

Data Science vs. Machine Learning: understanding the difference

We live in the so-called Zettabyte Era, which started in the middle of the 2010s when the amount of digital data and network traffic exceeded one zettabyte, or a trillion gigabytes. That might give you an idea just how much data is created and consumed nowadays. Not to mention that this amount grows at an increasing rate, and is projected to reach 181 zettabytes by 2025. Even though only a fraction of that data is stored for longer periods of time, the resulting data volume is still pretty intimidating.
Volha Duplenka

Volha Duplenka

on Apr 14, 2023
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DATA

Types of Artificial Intelligence — a general overview of a formidable technology

We live in a world that even a couple of decades ago only science fiction writers could imagine. The most amazing innovations quickly become commonplace and normal. However, if there is still one that doesn’t fail to impress, that would be artificial intelligence, as proven by the recent burst of interest in ChatGPT, a new language model application with advanced AI features that can be used to build chatbots. We encounter AI everyday, not only in chatbots, but also in voice-activated personal assistants, self-driving cars, robot vacuum cleaners, image generation software, and many, many other instances.
Volha Duplenka

Volha Duplenka

on Apr 12, 2023
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DATA

Data Science vs. Data Analytics — main differences overview

We live in a world where data is ubiquitous. Websites track all their users’ every click. Your phone carries a map of where you are and where you’ve been. Smart homes record information about their occupants and sales sites collect data about your buying habits. More and more people want to look for usable information in the data, to draw practical conclusions. This interest in data has developed rapidly and widely with the consequence that companies are looking for professionals with data-driven skills to deal with specific data problems.
Katarzyna Hewelt

Katarzyna Hewelt

on Jan 26, 2023
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DATA

Sentiment Analysis. What is it and how to use it?

During face-to-face conversations or by reading text - we, people, can determine a speaker's intention or mood, whether they feel happy about something or not. We can also identify the polarity of the context - is the expression positive or negative or maybe even neutral? It is relatively easy for most people, but do computers understand emotions? Is it important for machines to understand humans’ intentions? With the technological progression and advancement of machine learning techniques - our machines are getting closer and closer to answering these questions.
Katarzyna Hewelt

Katarzyna Hewelt

on Jan 10, 2023
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NETWORKS
DATA

AI and Machine Learning landscape for networks [infographic]

Aritifical intelligence (AI) and machine learning (ML) is a trending topic in all technological domains. It offers a rich set of methods for data processing that can be used to solve practical problems, including those occurring in networks. In this article we present a set of network issues for which the use of ML methods and different AI types can bring benefits and generally seems to make sense. Please note that this is a non-exhaustive list and is open to additions. For greater clarity, five main categories have been used.
Multiple authors

Multiple authors

on Jan 5, 2023
Thumbnail of an article about Data lake vs. data warehouse — differences in data management
DATA

Data lake vs. data warehouse — differences in data management

The day-to-day activities of any organization bring in a lot of information. This data needs to be tracked, stored, and eventually analyzed to allow the business to learn and grow. The bigger the organization, the more data it has, so where a simple database used to be enough for data storage, increasingly often new solutions are required. When it comes to managing big data, i.e. vast amounts of data from multiple data sources in multiple formats, two solutions have become very popular over the years: data warehouses and data lakes.
Maciej Manturewicz

Maciej Manturewicz

on Jan 3, 2023
Thumbnail of an article about ETL vs. ELT — What are they and when do we need them?
DATA

ETL vs. ELT — What are they and when do we need them?

When we talk about data warehousing, we think of databases, which is only half of the solution. Data must be stored - this is why we need a database. But data must also be delivered into the database from a source. This is where ETL/ELT comes into the equation. Let's briefly explain what a data warehouse is and why we need one. It is a system used for storing and reporting data. Due to their design, data warehouses can process huge amounts of data, making lots of information available for business analytics.
Arkadiusz Kordos

Arkadiusz Kordos

on Nov 30, 2022
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DATA

What is data engineering?

In the past two decades, the digital world has been flooded with data. Most companies have completed digital transformations, and more and more users leave their digital footprint behind every day. All of this information can be turned into useful knowledge that allows companies to make data-driven decisions. This is where the process of data engineering takes place. Data engineering is a branch of data science. Data science is a broad term that includes all of the tasks that make data valuable - from gathering the data to storing, analyzing, and visualizing it.
Maciej Manturewicz

Maciej Manturewicz

on Nov 29, 2022
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DATA

SQL vs. NoSQL. The evolution of relational and non-relational databases

Databases are tools used daily by companies of all kinds. They make storing and transforming large amounts of data possible, and they are necessary for any data-driven business. But they had to come a long way before they became the databases we know today. Let’s take a look at the history of databases and learn how and why SQL and NoSQL have come to life. Since we, as humans, started collecting data, we also needed a way to store it. So naturally, the concept of a database came before computers. In the computer world, databases came into existence in the early 60s, when in 1962, the Oxford Dictionary first recognized the term “database” as a software system that stores and retrieves data.
Multiple authors

Multiple authors

on Nov 10, 2022
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DATA

Top 20 data engineering software and tools

Big data is an important part of operations for every technology-focused enterprise. But to make the analytics effective, there needs to be an efficient data management system governed by data engineers. Data pipelines and architecture are complex environments, and building them requires special tools. These include software for collecting, storing, and validating data, as well as applications for data visualization, analytics, and many more. Read on to check out our choices for the top 20 data engineering technologies.
Maciej Manturewicz

Maciej Manturewicz

on Sep 30, 2022
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