Category: Data Engineering
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Secure API Integration in Python Using Multiple Authentication Methods (with Azure Key Vault Support)
APIs are the backbone of modern applications, enabling seamless integration between different systems. However, interacting with APIs often requires authentication, which varies across APIs. In this article, I’ll show you how to build a reusable Python function that supports Bearer Token, Basic Authentication, and APIs that require no authentication. By the end, you’ll have a handy function…
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Cherry-Pick Made Simple
Introduction In collaborative development, multiple teams often work on different features while production systems may require urgent fixes. Managing these changes across development, testing, and production environments can be tricky — especially when using a branching strategy in Azure DevOps. In this blog, we’ll walk through a real-world scenario involving three branches — main (development), release (testing),…
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Migrate RDS MySQL to S3 with Zero Downtime: AWS DMS Guide
Introduction To transfer data from Amazon RDS (MySQL) to Amazon S3, one of the most effective tools at your disposal is the AWS Database Migration Service (DMS). This reliable, user-friendly, and fully managed service enables seamless data movement with minimal disruption to your existing systems. Whether you’re performing a one-time bulk migration or setting up…
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Event Stream vs Apache Kafka: Choosing the Right Engine for Real-Time Data
Introduction In today’s digital world, data is moving at the speed of thought. Imagine a fleet of 100 vehicles, each equipped with 200 sensors, continuously generating millions of events per second. This isn’t fiction — it’s happening in industries like logistics, automotive, and smart cities. If you delay this data by even 30 seconds, the…
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Real-Time Data Ingestion Using Kafka, Event Hub, and Delta Live Tables
Introduction: In the world of big data, real-time data processing is becoming a necessity rather than a luxury. Businesses today need insights as soon as the data is generated. In this blog, we will walk through building a real-time streaming pipeline using Kafka (as a producer), Azure Event Hub (as the broker), and Delta Live…
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UDF vs Inbuilt Functions in PySpark — The Simple Guide
If you’re working with PySpark, you’ve probably asked yourself this at some point: “Should I use a built-in function or just write my own?” Great question — and one that can have a huge impact on your Spark application’s performance. In PySpark, there are two main ways to transform or manipulate your data: Using Inbuilt…
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Apache Spark 4.0’s Variant Data Types: The Game-Changer for Semi-Structured Data
As enterprises increasingly rely on semi-structured data—like JSON from user logs, APIs, and IoT devices—data engineers face a constant battle between flexibility and performance. Traditional methods require complex schema management or inefficient parsing logic, making it hard to scale. Variant was introduced to address these limitations by allowing complex, evolving JSON or map-like structures to…
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Ensuring Data Quality in PySpark: A Hands-On Guide to Deduplication Methods
Identifying and removing duplicate records is essential for maintaining data accuracy in large-scale datasets. This guide demonstrates how to leverage PySpark’s built-in functions to efficiently clean your data and ensure consistency across your pipeline. Predominant methods to remove duplicates from a dataframe in PySpark are: distinct () function dropDuplicates() function Using the Window function Using…
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Bulk API : An inevitable gamechanger
Essence: As businesses grow and handle ever-larger datasets, the demand for efficient data synchronization and management tools becomes increasingly essential. “Salesforce offers a robust ecosystem with a variety of APIs that facilitate seamless integration with external systems and enhance overall process efficiency.” It has become essential for the firm to deal with larger data sets…
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Unleashing the Power of Explode in PySpark: A Comprehensive Guide
Efficiently transforming nested data into individual rows form helps ensure accurate processing and analysis in PySpark. This guide shows you how to harness explode to streamline your data preparation process. Modern data pipelines increasingly deal with nested, semi-structured data — like JSON arrays, structs, or lists of values inside a single column.This is especially common…