Tag: Data Engineering
All the articles with the tag "Data Engineering".
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Lambda vs n8n: A Simple Explanation for Data Workflows
Introduction When building data systems or integrating APIs, a common question appears: should we use AWS Lambda or n8n? Both tools can automate processes, call APIs, and move data between systems,
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Should You Use AWS Lambda or AWS Glue to Update Records in HubSpot?
When integrating HubSpot with a data platform on AWS, a common architectural decision appears quickly: Should updates to HubSpot be executed from AWS Lambda or AWS Glue? The correct choice depends on
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What Is a Data Lake and What Is a Data Lakehouse?
Over the last decade, the world of data architecture has gone through several transformations. From traditional data warehouses to Hadoop-based data lakes and now to the emerging Lakehouse paradigm,
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Incremental Data Loads: Choosing Between resource_version and created_at/updated_at
Incremental data loading is a cornerstone of modern data engineering pipelines. Instead of re-ingesting entire datasets on each execution, incremental strategies focus on retrieving only records that
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EMR vs AWS Glue: Choosing the Right Data Processing Tool on AWS
When working with big data on AWS, two commonly used services for data processing are Amazon EMR and AWS Glue . Although both support scalable data transformation and analytics, they differ
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When Should You Use Iceberg with Athena? Partitioning Strategies and Best Practices
As data lakes grow in size and complexity, tools like Amazon Athena combined with table formats like Apache Iceberg become essential for scalability, data governance, and performance. In this post,
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How Google Changed Big Data: The Story of GFS, MapReduce, and Bigtable
In the early 2000s, Google faced a unique challenge: how to store, process, and query massive amounts of data across thousands of unreliable machines. The traditional systems of the time—designed for
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From Tables to Partitions: Designing NoSQL Databases with Cassandra
As data professionals transition from relational databases to NoSQL systems like Apache Cassandra, one of the most important mindset shifts is understanding that you don't model data for storage, but