Tag: Data Engineering
All the articles with the tag "Data Engineering".
-
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
-
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
-
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,
-
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
-
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
-
Apache Cassandra vs Apache Parquet: Understanding the Differences
In modern data architectures, it's common to encounter both Apache Cassandra and Apache Parquet , particularly when dealing with large-scale, distributed systems. Both technologies are associated with
-
What Is Serialization?
In the world of data engineering and software systems, serialization is a fundamental concept that allows you to efficiently store, transmit, and reconstruct data structures. If you’ve worked with
-
Understanding Pagination vs. Batch Processing in Data Handling
When working with large datasets, developers often face the challenge of efficiently extracting, processing, and managing data. Two commonly used techniques for handling such data efficiently are