Category: Data Engineering

Building a Scalable And Cost-Efficient BigQuery Platform: Architecture, Practices & Lessons

As data platforms evolve from proof-of-concept pipelines to business-critical systems, scaling BigQuery requires more than writing efficient SQL. Without the right architectural choices, governance, and monitoring, organizations often face unpredictable costs, query slowdowns, and operational instability.

This blog outlines a set of platform-level engineering decisions and best practices adopted to run BigQuery at scale—focused on performance, cost optimization, security and observability. Each practice is backed by real-world implementation examples. Continue reading “Building a Scalable And Cost-Efficient BigQuery Platform: Architecture, Practices & Lessons”

Technical Case Study: Amazon Redshift and Athena as Data Warehousing Solutions

Introduction

Modern data architectures demand flexible, scalable, and cost-effective solutions that can handle diverse analytical workloads. Amazon Web Services offers multiple data warehousing approaches that serve different needs: 

  • Amazon Redshift: A petabyte-scale, fully managed data warehouse designed for complex analytical queries 
  • Amazon Athena: A serverless query service that allows direct querying of data in S3. 

Continue reading “Technical Case Study: Amazon Redshift and Athena as Data Warehousing Solutions”

End-to-End Data Pipeline for Real-Time Stock Market Data!

Transform your data landscape with powerful, flexible, and flexible data pipelines. Learn the data engineering strategies needed to effectively manage, process, and derive insights from comprehensive datasets.. Creating robust, scalable, and fault-tolerant data pipelines is a complex task that requires multiple tools and techniques.

Unlock the skills of building real-time stock market data pipelines using Apache Kafka. Follow a detailed step-by-step guide from setting up Kafka on AWS EC2 and learn how to connect it to AWS Glue and Athena for intuitive data processing and insightful analytics.
Continue reading “End-to-End Data Pipeline for Real-Time Stock Market Data!”

Stream PostgreSQL Data to S3 via Kafka Using JDBC and S3 Sink Connectors : Part 1

Step 1: Set up PostgreSQL with Sample Data

Before you can source data from PostgreSQL into Kafka, you need a running instance of PostgreSQL with some data in it. This step involves:

  • Setting up PostgreSQL: You spin up a PostgreSQL container (using Docker) to simulate a production database. PostgreSQL is a popular relational database, and in this case, it serves as the source of your data.
  • Create a database and tables: You define a schema with a table (e.g., users) to hold some sample data. The table contains columns like id, name, and email. In a real-world scenario, your tables could be more complex, but this serves as a simple example.
  • Populate the table with sample data: By inserting some rows into the users table, you simulate real data that will be ingested into Kafka.

Continue reading “Stream PostgreSQL Data to S3 via Kafka Using JDBC and S3 Sink Connectors : Part 1”

Exploring Time Travel Queries in Apache Hudi

Apache Hudi (Hadoop Upserts Deletes and Incrementals) is an advanced data management framework designed to efficiently handle large-scale datasets. One of its standout features is time travel, which allows users to query historical versions of their data. This feature is essential for scenarios where you need to audit changes, recover from data issues, or simply analyze how data has evolved over time. In this blog post, we’ll walk through the process of setting up Hudi for time travel queries, using AWS Glue and PySpark for a hands-on example. Continue reading “Exploring Time Travel Queries in Apache Hudi”