Current State and Future Trends of Hadoop in the Big Data Landscape

Recent industry news reports that the two largest Hadoop vendors, Cloudera and Hortonworks, merged in October 2018, and both have faced financial challenges and leadership turnover, while the third major vendor, MapR, narrowly avoided bankruptcy before being acquired by HPE.

Contrary to pessimistic media coverage, Hadoop revenue remains strong; Gartner notes a 54% increase in 2017 for leading vendors (Amazon, Cloudera, Hortonworks, MapR), reaching $1.2 billion and 3.2% of the DBMS market, with many customers spending over $100 k annually on Hadoop software.

Growth is modest among traditional vendors but higher for public‑cloud players and companies like Huawei and MongoDB; competition is intensifying as the Hadoop stack fragments, with Apache Spark and Apache Kafka gaining adoption even without other Hadoop components.

The primary driver of change is migration to cloud platforms, where cost reduction and complexity management fuel steady increases in cloud‑based Hadoop deployments, as reflected in Gartner surveys.

Storage is shifting toward cloud object stores such as Amazon S3 and Azure ADLS, which are becoming the new data‑lake backbone; native cloud storage solutions (EMC ECS, MinIO, Red Hat Ceph) compatible with S3 are gaining interest, while Hadoop’s own Ozone object storage is released as an alpha, hinting at future hybrid deployments.

Chinese vendors like Transwarp, Huawei, and Dongfang Jinxin are expanding geographically, adding new competition to the market.

Despite diverse vendors and deployment models, moving Hadoop projects beyond the trial phase remains challenging due to design, deployment, product maturity, and skill gaps.

Hadoop comprises core open‑source components such as HDFS, MapReduce, and YARN; newer Ozone object storage aims to improve support for streaming data, though it is not yet GA and is less mature than existing object stores.

HDFS’s centralized metadata architecture raises scalability and reliability concerns, and its inability to expand storage independently makes it less ideal for modern data‑lake needs, prompting the rise of alternative distributed file and object storage solutions, which often trade performance for scalability.

Many enterprises now use the S3A connector to replace HDFS with object storage, despite its average performance and lack of append support; some vendors (Dell EMC ECS, XSKY) offer high‑performance HDFS clients to address these shortcomings.

Overall, Hadoop remains the preferred choice for building data lakes; Gartner surveys show that 34% of respondents are currently using Hadoop and 55% plan to adopt it within 24 months, reflecting a significant increase in demand since 2016.

Modern big‑data and AI applications, including TensorFlow, continue to support HDFS, and if object‑storage integration can match native HDFS performance, it will serve as an ideal foundation for enterprise data lakes.