Stream and Analyze PostgreSQL Data from S3 Using Kafka and ksqlDB: Part 2

Introduction

In Part 1, we set up a real-time data pipeline that streams PostgreSQL changes to Amazon S3 using Kafka Connect. Here’s what we accomplished:

  • Configured PostgreSQL for CDC (using logical decoding/WAL)
  • Deployed Kafka Connect with JDBC Source Connector (to capture PostgreSQL changes)
  • Set up an S3 Sink Connector (to persist data in S3 in Avro/Parquet format)

In Part 2 of our journey, we dive deeper into the process of streaming data from PostgreSQL to S3 via Kafka. This time, we explore how to set up connectors, create a sample PostgreSQL table with large datasets, and leverage ksqlDB for real-time data analysis. Additionally, we’ll cover the steps to configure AWS IAM policies for secure S3 access. Whether you’re building a data pipeline or experimenting with Kafka integrations, this guide will help you navigate the essentials with ease.

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Can Cloud Data Be Hacked

Cloud computing has become integral to our daily lives, often in ways we don’t even notice. The cloud has transformed how we manage and access data, from backing up photos on smartphones to sharing files and collaborating on documents. However, the cloud isn’t immune to security risks like any online platform. Cyberattacks targeting cloud data are a real concern and deserve careful attention. 

In this blog, we’ll explore the potential vulnerabilities of cloud storage and share actionable steps to protect your data effectively. 

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Advanced-Data Modeling Techniques for Big Data Applications

As businesses start to use big data, they often face big challenges in managing, storing, and analyzing the large amounts of information they collect.

Traditional data modeling techniques which were designed for more structured and predictable data environments, can lead to performance issues, scalability problems, and inefficiencies when applied to big data.

The mismatch between traditional methods and the dynamic nature of big data causes these issues, resulting in slower decision-making, higher costs, and the inability to fully leverage data.

For many organizations, these challenges result in slower decision-making, higher costs, and the inability to fully use their data.

In this blog, we will explore the sophisticated data modeling techniques designed for big data applications.

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