Skip to main content

Who Really Led the Hadoop Market? A Look Back at the 2014 Forrester Wave

Struggling with delivery, architecture alignment, or platform stability?

I help teams fix systemic engineering issues: processes, architecture, and clarity.
→ See how I work with teams.


In 2014 every Hadoop vendor claimed to be the market leader, but the Forrester Wave told a different story: the ecosystem was crowded, overlapping, and full of marketing noise. Looking back from 2025, it’s clear that none of the commercial players won—open source won, and the industry evolved far beyond the Hadoop vendors of that era.

In early 2014, Forrester Research published its well-known Forrester Wave: Big Data Hadoop Solutions, Q1 2014. The report evaluated the major players of that time—Cloudera, Hortonworks, MapR, IBM, Teradata—and declared them all “leaders.” Not surprisingly, each vendor immediately launched a marketing campaign claiming they were the one true leader.

From the outside it looked almost comedic: five companies staring at the same chart, each insisting the dot representing them was the real champion. The reality? The Hadoop distribution market was crowded, competitive, and full of overlapping capabilities. Nobody led decisively—and that matters.

The Big Three: Cloudera, MapR, Hortonworks

The three dominant Hadoop vendors of the time—Cloudera, MapR, and Hortonworks—were positioned extremely close to each other. In reality, each offered strengths, each had weaknesses, and most customers chose based on support, ecosystem tooling, and familiarity rather than technical superiority.

Cloudera

Cloudera provided the most advanced management tooling with Cloudera Manager and a rich operational experience. However, many of its differentiating components were closed source: Cloudera Manager Enterprise, Navigator, BDR, and others. Still, Cloudera became the most “enterprise-ready” Hadoop distribution of the era.

MapR

MapR followed a very different path. Their file system—MapR-FS—replaced HDFS entirely, offering NFS access, consistent snapshots, and C/C++ performance advantages. Their editions (M3, M5, M7) targeted different workloads, including low-latency operational databases. Despite strong engineering, the proprietary strategy limited community adoption. MapR eventually disappeared from the mainstream ecosystem.

Hortonworks

Hortonworks took the pure open-source route. HDP shipped 100% Apache components, including Ambari for cluster management, Falcon for governance, and Knox for security. Their mission was to push Apache Hadoop forward—something the ecosystem benefited from. Hortonworks later merged with Cloudera, ending the era of independent Hadoop distributions.

What Looked Like Leadership in 2014

Every vendor positioned themselves as the true innovator:

  • IBM claimed the broadest enterprise reach.
  • Teradata framed Hadoop as an extension to its existing analytics stack.
  • Cloudera leaned on enterprise tooling and partnerships.
  • MapR highlighted performance and their custom FS.
  • Hortonworks emphasized open-source purity.

But the truth was more nuanced. Everyone was competing for the same emerging market: on-premises Hadoop clusters running batch analytics with a growing need for SQL engines, streaming integrations, and security hardening.

What Actually Happened: The 2025 Perspective

Looking back a decade later, the “Hadoop distribution wars” are a resolved chapter. The world moved elsewhere.

  • Hortonworks and Cloudera merged.
  • MapR exited the market.
  • Cloud-native platforms replaced most on-prem Hadoop workloads.
  • SQL engines evolved into high-performance lakehouse technologies like Spark SQL, Trino, and DuckDB.
  • Object storage replaced HDFS for major analytics workloads.
  • Hadoop tuning became niche knowledge, though still relevant in legacy clusters (see tuning guide).

The Forrester chart predicted a vibrant and competitive market. What it didn’t predict was that the entire category—Hadoop distributions—would become increasingly irrelevant as cloud platforms, lakehouses, and open table formats reshaped the industry.

With hindsight, the only true constant was the open-source foundation itself. The real leadership came not from vendors but from the Apache community and the engineers who built the tools that shaped big data infrastructure for a decade.

Verdict

Every vendor claimed leadership. None fully achieved it. The winner was the idea itself: open, distributed, scalable data processing. And while the Hadoop vendor landscape changed dramatically, its influence lives on in the systems powering today’s analytics platforms.

If you need help with distributed systems, backend engineering, or data platforms, check my Services.

Most read articles

Why Is Customer Obsession Disappearing?

Many companies trade real customer-obsession for automated, low-empathy support. Through examples from Coinbase, PayPal, GO Telecommunications and AT&T, this article shows how reliance on AI chatbots, outsourced call centers, and KPI-driven workflows erodes trust, NPS and customer retention. It argues that human-centric support—treating support as strategic investment instead of cost—is still a core growth engine in competitive markets. It's wild that even with all the cool tech we've got these days, like AI solving complex equations and doing business across time zones in a flash, so many companies are still struggling with the basics: taking care of their customers. The drama around Coinbase's customer support is a prime example of even tech giants messing up. And it's not just Coinbase — it's a big-picture issue for the whole industry. At some point, the idea of "customer obsession" got replaced with "customer automation," and no...

How to scale MySQL perfectly

When MySQL reaches its limits, scaling cannot rely on hardware alone. This article explains how strategic techniques such as caching, sharding and operational optimisation can drastically reduce load and improve application responsiveness. It outlines how in-memory systems like Redis or Memcached offload repeated reads, how horizontal sharding mechanisms distribute data for massive scale, and how tools such as Vitess, ProxySQL and HAProxy support routing, failover and cluster management. The summary also highlights essential practices including query tuning, indexing, replication and connection management. Together these approaches form a modern DevOps strategy that transforms MySQL from a single bottleneck into a resilient, scalable data layer able to grow with your application. When your MySQL database reaches its performance limits, vertical scaling through hardware upgrades provides a temporary solution. Long-term growth, though, requires a more comprehensive approach. This invo...

What the Heck is Superposition and Entanglement?

This post is about superposition and interference in simple, intuitive terms. It describes how quantum states combine, how probability amplitudes add, and why interference patterns appear in systems such as electrons, photons and waves. The goal is to give a clear, non mathematical understanding of how quantum behavior emerges from the rules of wave functions and measurement. If you’ve ever heard the words superposition or entanglement thrown around in conversations about quantum physics, you may have nodded politely while your brain quietly filed them away in the "too confusing to deal with" folder.  These aren't just theoretical quirks; they're the foundation of mind-bending tech like Google's latest quantum chip, the Willow with its 105 qubits. Superposition challenges our understanding of reality, suggesting that particles don't have definite states until observed. This principle is crucial in quantum technologies, enabling phenomena like quantum comp...