List of Apache Nifi Customers
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Since 2010, our global team of researchers has been studying Apache Nifi customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Apache Nifi for Extract, Transform, and Load (ETL) from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Apache Nifi for Extract, Transform, and Load (ETL) include: Micron Technology, a United States based Manufacturing organisation with 53000 employees and revenues of $37.38 billion, Slovak Telekom, a Slovakia based Communications organisation with 2535 employees and revenues of $866.0 million, Ona Data, a Kenya based Professional Services organisation with 30 employees and revenues of $3.0 million and many others.
Contact us if you need a completed and verified list of companies using Apache Nifi, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight | Insight Source |
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Micron Technology | Manufacturing | 53000 | $37.4B | United States | Apache Software | Apache Nifi | Extract, Transform, and Load (ETL) | 2019 | n/a | In 2019, Micron Technology implemented Apache NiFi in its enterprise analytics/ETL layer to ingest worldwide manufacturing data into a global data warehouse. Apache NiFi was used for automated ingestion and site-to-site transfers to higher-performance processing engines such as Spark, supporting manufacturing analytics and data science in the United States. The implementation configured flow-based data pipelines and reusable NiFi processors to standardize ingestion, enforce data provenance, and enable clustered throughput. NiFi clustering and site-to-site functionality were used to move high-velocity telemetry from manufacturing sites into centralized processing layers, and typical NiFi capabilities such as flow monitoring and back pressure control were applied to stabilize pipelines. Integrations centered on the global data warehouse and downstream analytics platforms, with NiFi acting as the ETL ingress layer for manufacturing telemetry and batch streams. Operational coverage included worldwide manufacturing sites as data sources and analytics and data science teams in the United States as primary consumers, with NiFi flows designed to support downstream Spark processing and model training workflows. Governance emphasized centralized ingestion configuration, data lineage tracking, and controlled rollout of flow templates to site teams to ensure consistency. The deployment enabled faster data acquisition for analytics and supported improved forecasting and operational wins reported by the analytics team. | |
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Ona Data | Professional Services | 30 | $3M | Kenya | Apache Software | Apache Nifi | Extract, Transform, and Load (ETL) | 2017 | n/a | In 2017, Ona Data implemented Apache Nifi to ingest, process, and disseminate global health and service-delivery data supporting public health monitoring and humanitarian programs based in Nairobi and Washington, DC. The deployment places Apache Nifi at the front of Ona Data's data warehouse and streaming stack, working alongside Kafka, Druid, and Superset, and the Apps Category is . The Apache Nifi implementation focuses on high-throughput ingestion and stream processing, using NiFi processors and flowfile routing to perform schema-aware transformation, enrichment, and lineage capture. Data pipelines are configured to normalize field-collected records, apply lightweight validation and routing logic, and forward event streams to Kafka for downstream aggregation and analytics. Integrations are explicit within the stack, with NiFi delivering near-real-time streams into Kafka, which feed Druid for time-series storage and Superset for visualization and dashboarding. Operational scope centers on supporting cross-functional public health and humanitarian program teams, enabling field programs to access timely data for monitoring and reporting across Nairobi and Washington, DC. Governance is implemented through NiFi flow provenance, role-based access controls on the pipeline endpoints, and standardized flow templates to enforce consistent ingestion patterns across projects. The pipeline architecture emphasizes modular flows for reuse, operational monitoring for pipeline health, and data lineage to support auditability and downstream analytics. The NiFi-based pipeline enables near-real-time data delivery and visualization for Ona Data's field programs. | |
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Slovak Telekom | Communications | 2535 | $866M | Slovakia | Apache Software | Apache Nifi | Extract, Transform, and Load (ETL) | 2018 | n/a | In 2018, Slovak Telekom implemented Apache Nifi for telecommunications network operations monitoring. The deployment centralized SNMP polling of network devices and transformed SNMP responses into HDFS and Elasticsearch to support monitoring and analytics across its Slovakia network operations. The implementation uses Apache Nifi as the orchestration and data routing layer, Apps Category "". Apache Nifi flows were configured with SNMP polling processors, transformation and serialization steps, and routing logic to deliver telemetry into HDFS for long term storage and into Elasticsearch for indexing and search, reflecting typical ingestion and dataflow orchestration capabilities. Operational scope is focused on network operations within Slovak Telekom's telecommunications function, ingesting device telemetry and forwarding it to downstream monitoring and analytics platforms. Implementation timing is estimated from public project references and vendor listings and is scoped to network monitoring pipelines rather than enterprise wide data integration. |
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