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As you progress in your journey from business intelligence (BI) development toward data engineering or analytics engineering, one of the core skills you need to focus on is data modeling. Data modeling is the foundation for any data architecture—whether you are building databases, designing ETL pipelines, or creating data warehouses....
Introduction In today’s data-driven world, the choice of a database can significantly impact the performance, scalability, and maintainability of your application. With so many types of databases available, selecting the right one can be a daunting task. This guide will help you understand the key factors to consider when choosing a...
Kafka is a distributed streaming platform designed for real-time data pipelines, stream processing, and data integration. AWS lambda, on the other hand, is a serverless compute service that executes your code in response to events, managing the underlying compute resources for you. In organizations where Kafka plays a central role in...
In the first part of ETL data pipelines, we explored the importance of ETL processes, and their core components, and discussed the different types of ETL pipelines. Now, in this second part, we will dive deeper into some of the key challenges faced when implementing data ETL pipelines, outline best practices to optimize these processes...
In today's data-driven world, businesses rely on timely, accurate information to make critical decisions. Data pipelines play a vital role in this process, seamlessly fetching, processing, and transferring data to centralized locations like data warehouses. These pipelines ensure the right data is available when needed, allowing...
Introduction Maintaining data consistency and integrity across systems is crucial for any organization. In today’s data-driven world, discrepancies between data sources can lead to inaccurate analyses, poor decision-making, and operational inefficiencies. These issues can further result in financial losses, diminished customer trust,...
Ensuring everything runs smoothly in handling databases is like an ongoing adventure for folks working with data. PostgreSQL, a widely used and powerful open-source database system, is a go-to choice for many applications. But even in the land of PostgreSQL, making it work at its best isn’t always straightforward. In this journey,...
In today's world, handling complex tasks and automating them is crucial. Apache Airflow is a powerful tool that helps with this. It's like a conductor for tasks, making everything work smoothly. When we use Airflow with Docker, it becomes even better because it's flexible and can be easily moved around. In this blog, we'll explain what...
Data migration is a crucial process for modern organizations looking to harness the power of cloud-based storage and processing. The blog will examine the procedure for transferring information from MongoDB, a well-known NoSQL database, to Amazon S3, an elastic cloud storage solution leveraging PySpark. Moreover, we will focus on handling...
In this blog, I will discuss how Spark structured streaming works and how we can process data as a continuous stream of data. Before we discuss this in detail, let’s try to understand stream processing. In layman’s terms, stream processing is the processing of data in motion or computing data directly as it is produced or...