Category: Big Data
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Slowly Changing Dimensions (SCDs): What Are They and Why Do They Matter?
Image by Pete Linforth from Pixabay Imagine you’re keeping a diary about your favourite ice cream shop. One day, they introduce a new flavour. The next week, they rename the “Choco Fudge” to “Double Chocolate Delight.” Later, they move to a bigger location. How do you keep track of all these changes in your diary?…
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Database Partitioning and Sharding: A Data Engineer’s Perspective
As a data engineer, one of the most common challenges I’ve faced is managing the growing volumes of data that modern applications generate. When a single database struggles to handle all that data, performance suffers, queries slow down, and scaling becomes a nightmare. That’s when techniques like partitioning and sharding come to the rescue. Image…
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The Evolution of SQL: From Traditional Databases to Big Data
SQL (Structured Query Language) has been a cornerstone of database management and data analysis for decades. It has evolved significantly since its inception in the 1970s, adapting to the changing landscape of data storage, processing, and analysis. This evolution reflects the broader technological trends and the increasing demand for handling vast amounts of data efficiently.…
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Revolutionizing Data Management: The Power and Promise of Data Mesh
Imagine a school has a big library with all the books, and only one librarian to manage everything. Every time a student wants to borrow a book or find information, they have to wait in line for the librarian to help them. This can be slow and frustrating, especially when many students need different books…
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Threaded Together: Enhancing Distributed Computing through Concurrency and Synchronization
In the realm of distributed computing, where multiple computing entities work together to solve complex problems, threads play a pivotal role. Threads, which are smaller units of processes, facilitate concurrent execution, enabling systems to perform multiple operations simultaneously. This blog explores the significance of threads in distributed computing, the challenges they present, and the benefits…
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Data Pipeline Showdown: Full Load or Incremental Load?
In an ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) process, loading data in a pipeline refers to the process of moving data from a source system into a destination system, such as a data warehouse, data lake, or other storage solutions. There are two ways to load data in a pipeline – full…