Learn Data Architecture

Data architecture is essential for data scientists. It’s the foundation on which data scientists build their models and insights. A well-designed data architecture can help data scientists to be more productive, efficient, and accurate.

In this article, we will explore the typical flow of structured data before it reaches the hands of data scientists or business analysts.

In the realm of data science, unstructured data presents a unique challenge and opportunity for organizations. Surprisingly, it accounts for a significant portion, approximately 80% to 90%, of an organization’s data.

In this chapter, we explore the captivating realm of semi-structured data, where tags and markers define hierarchies and structures.

The data warehouse serves as the cornerstone for organizing and analyzing structured data. By collecting information from operational databases and transforming it into a format conducive to analytics, organizations can tap into its immense potential.

Welcome to the realm of data lakes, where structured, semi-structured, and unstructured data come together in a unified storage system.

In this blog post, we will explore the concept of a data lakehouse, which aims to address the challenges associated with integrating structured and unstructured data.

The data mesh aims to enable self-service access to reliable, trustworthy data products. Rather than having a centralized data team control and gatekeep everything, ownership is distributed across domain teams closer to the source.

In this blog post, I’m going to get you up to speed with streaming data and its profound impact on data science. We’ll explore the significance of streaming data, its real-time applications across industries, and delve into key technologies like Apache Kafka. Additionally, we’ll discuss the Lambda and Kappa architectures, which play a vital role in processing streaming data. So read on to discover the exciting world of streaming data!

In the vast landscape of data science, vector databases have emerged as a powerful tool for storing and leveraging vector embeddings. These databases enable the storage of vector representations for various types of data, such as text, audio, images, and even videos. In this blog post, we’ll delve into the world of vector databases, understanding the concept of vector embeddings and the invaluable role these databases play in facilitating vector similarity searches. Join us on this exciting journey as we explore the potential of vector databases and their applications in diverse industries.

In this blog post, we’ll delve into the concept of feature stores and their pivotal role in machine learning workflows. So let’s dive in and discover how feature stores can revolutionize your approach to developing use cases and models!