What Drives the Next Wave of Open‑Source Big Data? Insights from the 2022 Heat Report

Background and Purpose

Hadoop, the origin of open‑source big data projects, has been around for more than fifteen years. To deeply understand the past, present, and future of open‑source big data technologies and provide valuable references for enterprises and developers, the OpenAtom Foundation, X‑Lab Open Lab, and Alibaba Open Source Committee jointly launched the "2022 Open Source Big Data Heat Report".

Scope and Findings

The report starts from the tenth year of Hadoop’s development (2015), collecting public data from 102 of the most active open‑source big data projects. It finds that the heat value of open‑source projects doubles roughly every 40 months, and five major heat jumps have occurred in the past eight years. Diversification, integration, and cloud‑native adoption are the most prominent current trends.

Target Audience

The report aims to help (1) enterprises and developers engaged in big‑data R&D, (2) developers who wish to contribute to open‑source projects, and (3) operators or maintainers of open‑source big‑data projects.

User Demand Drives Technological Diversification

Since 2015, the Hadoop‑centric ecosystem has shifted to parallel development of multiple technologies. Some Hadoop components (e.g., HDFS) have become stable foundations for newer tools, and combinations such as Flink + Kafka or Spark + HDFS have become standardized choices.

Developers’ enthusiasm now focuses on six hot areas: search & analytics, stream processing, data visualization, interactive analytics, DataOps, and data lakes. Heat‑value jumps align with these areas: data visualization (2016, 2021), search & analytics and stream processing (2019), interactive analytics and DataOps (2018, 2021), and data lakes (2020).

The evolution reflects a shift from visualization applications to processing technologies, then to storage and management, with infrastructure improvements driving higher‑level innovations.

Spiral Development and Cloud‑Native Impact

Heat jumps illustrate a spiral‑upward development: user‑side demand pushes system‑side advancements, leading to better scalability, lower cost, and higher flexibility. For example, Apache Superset’s commercial backing by Preset shows new explorations in BI.

New scenarios such as data governance, stream + OLAP, and data lakes keep driving component innovation, creating uncertainty for the future.

Challenges for Enterprises and Developers

Building enterprise‑grade big‑data platforms now requires mastering multiple components, which many small‑to‑medium companies lack. Professional big‑data teams are needed for design, consulting, and guidance.

As business scale grows, requirements for stability, security, and high availability increase, demanding observability and diagnostic tools that open‑source components alone do not provide.

Cloud‑Native Transformation

All new projects after 2015 have embraced cloud‑native architectures. In 2022, cloud‑native projects accounted for 51% of heat value, with data integration, storage, and management seeing the most turnover; over 80% of heat value comes from cloud‑native initiatives. Major projects like Spark, Kafka, and Flink now support Kubernetes.

Data integration is being rebuilt faster than other areas, moving from labor‑intensive ETL tools to flexible pipelines. Traditional tools (Flume, Camel) are in maintenance mode, while cloud‑native solutions such as Airbyte, Flink CDC, SeaTunnel, and InLong grow rapidly.

Heat trends show cloud‑native data integration surpassing traditional integration since 2018, with annual compound growth rates exceeding 100%.

Architectural Shifts in the Cloud

Running on the cloud changes architecture: elasticity, observability, and native Kubernetes integration become defaults. Shuffle services must adapt to cloud resources; projects like Celeborn aim to improve shuffle performance.

Scheduling on Kubernetes introduces new bottlenecks, prompting internal improvements at Alibaba.

Cloud storage brings bandwidth and locality challenges; projects like JuiceFS, Alluxio, and Alibaba EMR’s JindoData address these issues.

Future Roles for Big‑Data Professionals

Roles such as system engineers, data engineers, and data scientists will persist, but cloud reduces low‑level system work. System engineers will focus on providing standardized cloud services, while engineers in enterprises will shift toward business‑oriented data science and analytics.

Data engineers and scientists will concentrate on delivering business value through modeling and governance, leaving infrastructure concerns to cloud providers.

Key Directions for the Future

Three focus areas emerge: (1) Cloudification—addressing architecture through offline/real‑time integration, AI integration, stream‑batch integration, and lake‑warehouse integration; (2) Simplifying upper‑layer data applications, moving toward universal SQL analysis; (3) Growing ecosystems, where platforms like Alibaba Cloud Flink, EMR, Databricks, Snowflake, and BigQuery become foundations for vertical solutions, provided they achieve standardization.