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Why Tenzir

On this page we explain what Tenzir is, what you can do with it, and how it compares to other systems out there.

What is Tenzir?

Tenzir is a security data pipeline solution for security teams, providing the following abstractions:

  • Tenzir's pipelines consist of powerful operators that perform computations over Arrow data frames. The Tenzir Query Language (TQL) makes it easy to express pipelines—akin to Splunk and Kusto.
  • Tenzir's indexed storage engine persists dataflows in an open format (Parquet & Feather) so that you can access them with any query engine, or run pipelines over selective historical workloads.
  • Tenzir nodes offer a managed runtime for pipelines and storage.
  • Interconnected nodes form a data fabric and pipelines can span across them to implement sophisticated security architectures.

What can I do with Tenzir?

Use Tenzir if you want to:

  • Filter, shape, and enrich events before they hit your SIEM or data lake
  • Normalize, enrich, aggregate, and deduplicate structured event data
  • Store, compact, and search event data in an open storage format
  • Operationalize threat intelligence for live and retrospective detection
  • Build your own security data lake
  • Create a federated detection and response architectures

Tenzir vs. X

Tenzir fills a gap in the market—powerful enough for data-intensive security use cases, but easy enough for security users that are not data engineers.

One of the major challenges in cybersecurity is the increasing volume of data that organizations need to manage and analyze in order to protect their critical infrastructure and sensitive data from cyber threats. Traditional security information and event management (SIEM) systems are not designed to scale with this increasing volume of data, and can become costly and time-consuming to maintain. Additionally, cloud and data lakes are often geared towards data engineers rather than security professionals, leading to security teams having to spend valuable time and resources wrangling data instead of hunting for threats. This creates a significant barrier for organizations looking to effectively protect their data and infrastructure from cyber attacks.

What is needed is a security-native data architecture that enables security teams to take control of their data and easily deploy and manage the flow of security data. This solution should be able to integrate seamlessly with existing data architectures, scale from single nodes to highly distributed data fabrics, and support a variety of deployment options including cloud, on-premises, decentralized, and even air-gapped environments.

Importantly, a security-native data architecture should be easy to deploy, use, and manage without the need for dedicated data engineering resources. It should also be built around security standards and integrate easily with security tools in a plug-and-play fashion.

Tenzir aims to fill this gap as an open pipelines and storage engine for building scalable security architectures. Pipelines make it easy to transport, filter, reshape, and aggregate security events, whereas the embedded storage and query engine enables numerous detection and response workloads to move upstream of SIEM for a more cost-effective and scalable implementation. Tenzir is built using open standards, such as Apache Arrow for data in motion and Apache Parquet for data at rest, preventing vendor lock-in and promote full control of your event data and security content.

Tenzir vs. Cribl

Cribl is a data engine for IT and security. Due to popular demand, we created a dedicated page where we compare Tenzir vs. Cribl.

Tenzir vs. SIEMs

Traditional SIEMs support basic search and a fixed set of analytical operations. For moderate data volumes, the established SIEM use cases perform well. But when scaling up to high-volume telemetry data, traditional SIEMs fall behind and costs often spiral out of control. Traditional SIEMs also lack good support for threat hunting and raw exploratory data analysis. That's why more advanced use cases, such as feature extraction, model training, and detection engineering, require additional workbenches built on data lakes.

Tenzir complements a SIEM nicely with the following use cases:

  • Offloading: route the high-volume telemetry to Tenzir that would otherwise overload your SIEM or be cost-prohibitive to ingest. By keeping the bulk of the data in Tenzir, you remove bottlenecks and can selectively forward the activity that matters to your SIEM.

  • Compliance: Tenzir supports fine-grained retention configuration to meet GDPR and other regulatory requirements. When storage capacity needs careful management, Tenzir's compaction feature allows for weighted ageing of your data, so that you can specify relative importance of event types. Tenzir's powerful pipelines allow you to anonymize, pseudonymize, or encrypt specific fields—either to sanitize PII data on import, or ad-hoc on export when data leaves a pipeline or node.

  • Data Science: The majority of SIEMs provide an API-only, low-bandwidth access path to your security data. Tenzir is an Arrow-native engine with unfettered high-bandwidth access so that you can bring demanding workloads to your. Bring your own tools, e.g., to run iterative clustering algorithms, perform feature extraction, compute embeddings, and perform in-stream model inference.


Unlike a heavy-weight legacy SIEM, Tenzir is highly embeddable so that you can run it everywhere: containerized in the public cloud, in the data center in the private cloud, on bare-metal appliances deep in the network, or at the edge.

Tenzir vs. Databases

Tenzir comes with a builtin storage engine at every node. You may wonder: should I use an existing database instead?

Tenzir vs. Data Warehouses

Data warehouses and OLAP engines seem like an appealing choice for immutable structured data. They offer sufficient ingest bandwidth, perform well on aggregations, come frequently with advanced operations like joins, and often scale out well.

However, as a cornerstone for security operations, they fall short in supporting the following relevant use cases where Tenzir has the edge:

  • Data Onboarding: it takes considerable effort to write and maintain schemas for the tables of the respective data sources. As a data pipeline product, Tenzir is well suited for your extract-transform-load (ETL) needs: purpose-built for security data, available integrations for key security tools, and many data connectors out of the box.

  • Rich Typing: modeling security event data with a generic database often reduces the domain-specific values to strings or integers, as opposed to retaining their original semantics, such as IP addresses or port numbers. Tenzir offers a rich type system that can retain such semantics during data onboarding, giving you the ability to query the data with your own taxonomy at query time.


Data warehouses may be well-suited once data is fully structured, but you still need to cover the ETL and reverse-ETL aspects. Thanks to automatic schema inference, Tenzir significantly reduces the cost of data onboarding for data warehouses.

Tenzir vs. Relational DBs

Unlike OLAP workloads, OLTP workloads have strong transactional and consistency guarantees, e.g., when performing inserts, updates, and deletes. These extra guarantees come at a cost of throughput and latency when working with large datasets, but are rarely needed for analytical workloads. In a world of incomplete data, high data velocity, and immutable representations of activity, relational databases are rarely encountered.


If you aim to perform numerous modifications on a small subset of event data, with medium ingest rates, relational databases (like PostgreSQL or MySQL), might be a better fit. Tenzir's columnar data representation is ill-suited for row-level modifications, but shines for analytical workloads.

Tenzir vs. Document DBs

Document DBs, such as MongoDB, offer worry-free ingestion of unstructured data. They scale well horizontally and flexible querying.

However, they might not be the best choice for the data plane in security operations, for the following reasons:

  • Vertical Scaling: when co-locating a storage engine next to high-volume data sources, e.g., on a network appliance together with a network monitor, CPU and memory constraints coupled with a non-negligible IPS overhead prohibit scaling horizontally to build a "cluster in a box."

  • Analytical Workloads: the document-oriented storage does not perform well for analytical workloads, such as aggregations. But such workloads are common when hunting or when deploying detections.

  • Economy of Representation: security telemetry data exhibits a lot of repetitiveness between events, such as similar IP addresses, URL prefixes, or log message formats. This data compresses much better when transposed into a columnar format, such as Parquet.

A special case of document DBs are full-text search engines, such as ElasticSearch. The unit of input is typically unstructured text. These engines use (inverted) indexes and ranking methods to return the most relevant results for a given combination of search terms.


Most of the security tools work with structured data. Operators spend a substantial amount of time to convert data from unstructured to structured. But if your primary use case involves working with text documents, Tenzir might not be a good fit. That said, needle-in-haystack search and other information retrieval techniques are still relevant for security analytics, for which Tenzir's indexed storage engine also has basic support.

Tenzir combines the flexibility of the document-oriented data model with the power of structured OLAP. By relying internally on columnar data representation with Apache Arrow and simultaneously performing schema inference, you get the best of both worlds.

Tenzir vs. Timeseries DBs

Timeseries databases share a lot in common with OLAP engines, but focus their data organization around time as key dimension.


If you plan to access your event data only through the time dimension and need to model the majority of data as series, a timeseries DBs may suit the bill. If you access data through other (spatial) attributes, like IP addresses or domains, a traditional timeseries DB might not be good fit—especially for high-cardinality attributes. If your workloads involve running more complex detections, or include needle-in-haystack searches, Tenzir might be a better fit.

Tenzir vs. Key-Value DBs

A key-value store performs a key-based point or range lookups to retrieve one or more values. Security telemetry is high-dimensional data and there are many more desired entry points than a single key besides time, e.g., IP address, application protocol, domain name, or hash value.


Key-value stores alone are not suitable as foundation for running security analytics workloads. There are narrow use cases where key-value stores can facilitate certain capabilities, e.g., when processing watch lists. (Tenzir offers a context plugin for this purpose.)

Tenzir vs. Graph DBs

Graph databases are purpose-built for answering complex queries over networks of nodes and their relationships, such as finding shortest paths, measuring node centrality, or identifying connected components. While networks and communication patterns can naturally be represented as graphs, traditional security analytics query patterns may not benefit from a graph representation.


If graph-centric queries dominate your use case, Tenzir is not the right execution engine. Tenzir can still prove valuable as foundation for graph analytics by storing the raw telemetry and feeding it (via Arrow) into graph engines that support ad-hoc data frame analysis.

Tenzir vs. Vector DBs

Vector databases operate on embeddings, which are high-dimensional floating point vectors. For generative AI applications, decision support systems, or search on unstructured data, embeddings are the building block, and vector databases offer native operations on them, such as approximate nearest neighbor search.


Tenzir can efficiently represent embeddings via Apache Arrow, but lacks specific processing capabilities. Use Tenzir to transport your vectors to more purpose-built engines that also build on Arrow data frames.