Author: Suhas Jangoan

ELT (“Extract, Load, Transform”) represents a paradigm shift in data management, emphasizing the power and scalability of modern data processing. By prioritizing the loading of raw data into powerful data processing systems before transformation, ELT enables organizations to harness their data more efficiently and with greater agility.

The Role of ELT in Modern Data Architecture

ELT plays a pivotal role in contemporary data strategies, aligning with a cloud-first approach and the need for real-time analytics. It supports the vast data volumes generated by today's businesses, providing a flexible framework that adapts to an ever-changing data landscape:

To navigate the digital landscape safely, organizations must prioritize building robust cloud infrastructures, and sanctuaries for their valuable data. The foundation of a secure cloud architecture requires steadfast principles and guiding decisions like invisible forces that form a resilient structure. Here we explore the key tenets for building a secure environment within the cloud.

Least Privilege

The concept of 'Least Privilege' dictates that a person or system should have the minimal level of access or permissions needed to perform their role. This security measure is akin to compartmentalization, limiting the spread of damage should a breach occur.

In the digital era, data is the new gold, and the cloud is the new frontier. As we embark on this journey through the cloud landscape, it's crucial to understand the bedrock upon which our data-driven aspirations are built. Just as a house needs a solid foundation before you can think about interior design, understanding the structure of data management in the cloud is essential before you can leverage it to its full potential. In this article, we'll dissect the layers of data management.

Exploring the Layers of Data Management

Imagine you're constructing a skyscraper. You wouldn't start with the penthouse, would you? Similarly, when we talk about managing data in the cloud, we must begin at the bottom and work our way up. Let's explore the layers that form the backbone of any cloud-based data strategy.

When we talk about "data at rest," we're referring to data that is stored on a device or a backup and is not actively moving from network to network or being processed. Think of it as your digital data taking a nap on your hard drive, USB stick, or cloud storage. Much like a bear hibernating, just because it's static doesn't mean it's safe from threat. Enter: encryption.

The Role of Encryption in Protecting Data at Rest

Encryption is the digital equivalent of a high-security lock on your files, only allowing access to those who have the right key. It works by converting the original representation of the information, known as plaintext, into an alternative form known as ciphertext. This ciphertext appears as a random string of characters to anyone who doesn't have authorization.

The modern data stack represents the evolution of data management, shifting from traditional, monolithic systems to agile, cloud-based architectures. It's designed to handle large amounts of data, providing scalability, flexibility, and real-time processing capabilities. This stack is modular, allowing organizations to use specialized tools for each function: data ingestion, storage, transformation, and analysis, facilitating a more efficient and democratized approach to data analytics and business operations. As businesses continue to prioritize data-driven decision-making, the modern data stack has become integral to unlocking actionable insights and fostering innovation.

The Evolution of Modern Data Stack

The Early Days: Pre-2000s

Companies use big, single systems to keep and manage their data. These were good for everyday business tasks but not so much for analyzing lots of data. Data was stored in traditional relational databases like Oracle, IBM DB2, and Microsoft SQL Server.

Think of data pipeline orchestration as the backstage crew of a theater, ensuring every scene flows seamlessly into the next. In the data world, tools like Apache Airflow and AWS Step Functions are the unsung heroes that keep the show running smoothly, especially when you're working with dbt (data build tool) to whip your data into shape and ensure that the right data is available at the right time. Both tools are often used alongside dbt (data build tool), which has emerged as a powerful tool for transforming data in a warehouse. 

In this article, we will introduce dbt, Apache Airflow, and AWS Step Functions and then delve into the pros and cons of using Apache Airflow and AWS Step Functions for data pipeline orchestration involving dbt. A note that dbt has a paid version of dbt cloud and a free open source version; we are focussing on dbt-core, the free version of dbt.