Data orchestration can be defined either as an automation or as a data management process. In this article, we will look closely at both of these definitions, how they are different from each other. We will also talk about some of the most popular tools used by engineers worldwide.
Read on to find out what they are and what makes them a valid storage option for solutions built around edge reference architecture. You will also learn how the combination of data lakes and edge computing applies in real life.
We’re excited to announce that VirtusLab will be attending the Data Innovation Summit 2025 as a Venue Partner! Whether you're deep into machine learning, curious about data maturity, or just looking for some good conversation (and maybe a cup of tea), we’d love to meet you there.
The LLMs as we know them didn’t appear out of thin air. AI has roots dating back over 100 years, when early concepts like fuzzy logic emerged. Then came mathematical models that contributed to the development of neural networks. But will LLMs replace classic Machine Learning for good?
Data transformation involves altering, refining, and structuring information into a standardized and usable format. It plays a key role in enabling effective data analysis, supporting decision-making, and driving organizational growth. Read more about different types of data transformation, as well as its benefits and challenges.
In this article, we will compare two approaches to data processing, namely batch and stream processing. Each of them has different characteristics and serves different purposes, but we will attempt to break them down to help you decide which one would better suit your project.
Data is a critical asset that provides valuable insights into customer behavior, market trends, and operational efficiency. However, managing big data can be a challenge. Businesses that struggle to do so effectively may face increased costs and missed growth opportunities. One way to stay competitive and grow is through gradual engineering improvements.
Scala 3 gives you the tools to design the perfect Spark API. We proved it by creating the open-source library Iskra.
Spark 3.2.0’s support for Scala 2.13 technically allows Scala 3 Spark jobs—but it remains “an uphill path,” requiring workarounds for encoders and data shapes. Using libraries like Iskra smooths the path, yet production readiness is still experimental.