The world of data platforms has evolved dramatically over the last decade. What began as an ecosystem dominated by Hadoop has transformed into a landscape defined by cloud-native lakehouses, AI-powered analytics, governed pipelines, and unified multi‑cloud strategies. With every major vendor innovating rapidly—AWS, Azure, Google Cloud, and Cloudera—the question many enterprises face today is: …
The Myth of Multi‑Cloud Lock‑In: A Practical Perspective (With Real‑World Examples)
Introduction “Vendor lock‑in” is one of the most overused—and misunderstood—terms in cloud discussions today.It has become a selling slogan, a fear‑based argument, and often a key justification for choosing multi‑cloud architectures without fully understanding the implications.But the irony?Lock‑in existed long before cloud computing. We simply didn’t call it that. This …
How Polyglot Persistence and Decentralization Supercharged Microservices
Over the past decade, the way organizations build, deploy, and scale their digital ecosystems has transformed dramatically. At the heart of this transformation is the evolving big data platform—once a monolithic, centralized system, now an ecosystem of specialized, decentralized, and distributed components. Two key concepts have shaped this evolution: polyglot persistence and decentralization. …
Cloudera vs AWS vs AZURE vs Google Cloud: How to decide on the right big data platform?
UPDATED:28 Sep 2024: This article was published many years ago. Most of the facts described in this article may not be valid in today’s scenario. The updated version of this article will be published soon. Background Big data concepts evolved to solve a specific problem of processing data of diversified …
From RDDs to DataFrames: A Clear, Real‑World Guide for Spark Developers
Apache Spark provides multiple ways to process big data, and two of its most commonly used abstractions are RDDs and DataFrames. Although they belong to the same ecosystem, each serves different purposes and is suited for different kinds of workloads. RDDs, or Resilient Distributed Datasets, were Spark’s original abstraction. They …
Concepts of Containers
Understanding Containers: A Simple Story for Everyone In today’s fast‑moving digital world, companies must deliver new apps and services quickly. But older ways of deploying software—where apps are tied tightly to the machine they run on—often cause delays, confusion, and unexpected problems. This is where containers come in. Think of them as …
Hadoop Streaming with Perl Script
In this article, I am going to explain how to use Hadoop streaming with Perl scripts. First, let’s understand some theory behind Hadoop streaming. Hadoop has been written in Java. Therefore, the native language to write MapReduce program is Java. But, Hadoop also provide an API to MapReduce that allows …
Implementing Security in Hadoop Cluster
When we talk about security in Hadoop, we need to explore all the aspect of cluster networking and understand how the Nodes and Client communicate to each other. Let’s list down possible communication in a simple cluster. Master – Slave communication => Namenode – Datanode / Jobtracker – Tasktracker communication …
Tool & ToolRunner – Simplifying the concept
Writing a mapper & reducer Program definition is easy. Just extend your class by org.apache.hadoop.mapreduce.Mapper and org.apache.hadoop.mapreduce.Reducer respectively and override the map and reduce methods to implement your logics. But, when it comes to write driver program (contain main method of program) for the MapReduce Job, it’s always preferable to …
Developing Java Map-Reduce on local machine to run on Hadoop Cluster
Introduction In this post, I have explained how to develop hadoop jobs in Java and export JAR to run on Hadoop clusters. Most of the articles on internet, talk about installing eclipse-plugin and using maven or ANT to build JAR. To install eclipse-plugin for hadoop, one needs to install eclipse …