Cloudera Tech Stack

Data and AI platform spanning clouds, data centers, and edge environments

Software Development Santa Clara, California 1,001–5,000 employees Privately Held

Cloudera operates a data and AI platform designed to function across hybrid and multi-cloud infrastructure—AWS, Azure, GCP, and on-premises. The tech stack reveals a company deeply embedded in open-source big-data infrastructure (Kafka, Spark, Flink, Hadoop ecosystem) while actively adopting container orchestration (Kubernetes, Helm, Rancher) and infrastructure-as-code tooling (Terraform, OpenTofu, Crossplane), indicating a strategic shift toward cloud-native deployments. Engineering dominates hiring (165 roles), and active projects center on next-generation ML/AI platforms, cloud-native SaaS design, and low-latency inference services—aligned with pain points around accelerating ML-to-production workflows and cost optimization at scale.

Tech Stack 192 technologies

Core StackApache Kafka Apache Flink Apache Spark Apache NiFi Java Kubernetes Kafka RAG AWS OpenShift Python Hadoop Trino Apache Airflow Docker JavaScript Schema Registry Azure GCP Apache Hive HBase R Cloudera Data Platform Cloudera DataFlow HDFS LDAP Kerberos Impala pytest TestNG+149 more

AdoptingHelm Kubernetes Terraform Crossplane Apache Ozone Apache Ratis OpenTofu RKE+8 more

ReplacingHDFS

What Cloudera Is Building

◆Challenges

Vendor lock-in
Performance optimization
Overcoming hdfs limitations
Cost efficiency
Performance bottlenecks in large-scale distributed systems
Accelerating ml & ai from exploration to production
Improving vulnerability detection speed
Modernizing storage and workload migration capabilities
Scaling erp platform
Cost optimisation

▲Active Projects

Cloudera data platform
Core feature set of apache ozone
Develop new features in scala/java/python
Next-generation ai and machine learning platform
Hive ecosystem enhancements
User-driven data analytics application
Internal developer platform
Low-latency foundation model services
Cloud-native saas platform design and scaling
Parallel and distributed query engine feature development

Hiring Activity

Steady310 roles · 120 in 30d

Department

Engineering

165

Sales

Marketing

Data

Product

Security

Finance

Ops

Seniority

Senior

158

Staff

Director

Mid

Junior

Principal

Lead

Manager

Notable leadership hires: Technical Lead, Partner Sales Lead, Cloud FinOps Lead, Engineering Lead, Account Director

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About Cloudera

Cloudera provides a data and AI platform for enterprises operating across multiple infrastructure environments: public clouds (AWS, Azure, GCP), on-premises data centers, and edge deployments. The platform handles structured and unstructured data at scale, underpinned by open-source components (Apache Kafka, Spark, Flink, Hive, HBase) layered with proprietary features for ML/AI workflows, security (Kerberos, LDAP), and analytics (Trino, Impala). The company sells to large organizations in finance, healthcare, government, and technology sectors. With 1,001–5,000 employees, Cloudera operates a distributed workforce across 18+ countries, with significant engineering presence in Hungary, India, and Canada. Active product development focuses on cloud-native architecture, low-latency ML inference, and storage modernization (Apache Ozone adoption signals movement away from HDFS limitations).

HeadquartersSanta Clara, California

Company Size1,001–5,000 employees

Hiring MarketsHungary, Canada, India, Czechia, Singapore, Israel, Ireland, United States

Frequently Asked Questions

What tech stack does Cloudera use?

Core: Apache Kafka, Spark, Flink, Hive, HBase, Hadoop/HDFS. Cloud platforms: AWS, Azure, GCP, OpenShift. Orchestration: Kubernetes, Docker. Infrastructure: Terraform, OpenTofu, Helm, Rancher. Languages: Java, Python, R, Scala, JavaScript. Analytics: Trino, Impala. Security: Kerberos, LDAP.

What is Cloudera working on?

Next-generation AI and ML platforms, cloud-native SaaS design and scaling, low-latency foundation model services, Apache Ozone storage features, Hive ecosystem enhancements, and a user-driven data analytics application. Addressing pain points: ML-to-production acceleration, HDFS limitations, cost optimization, and vulnerability detection speed.