Platforms
Adopt
Assess
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30. Azure AI Search
Azure AI Search, formerly known as Cognitive Search, is a cloud-based search service designed to handle structured and unstructured data for applications like knowledge bases, particularly in retrieval-augmented generation (RAG) setups. It supports various types of search, including keyword, vector and hybrid search, which we believe will become increasingly important. The service automatically ingests common unstructured data formats including PDF, DOC and PPT, streamlining the process of creating searchable content. Additionally, it integrates with other Azure services, such as Azure OpenAI, allowing users to build applications with minimal manual integration effort. From our experience, Azure AI Search performs reliably and is well-suited for projects hosted in the Azure environment. Through its custom skills, users can also define specific data processing steps. Overall, if you're working within the Azure ecosystem and need a robust search solution for a RAG application, Azure AI Search is worth considering.
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31. Databricks Delta Live Tables
Databricks Delta Live Tables is a declarative framework designed for building reliable, maintainable and testable data processing pipelines. It allows data engineers to define data transformations using a declarative approach and automatically manages the underlying infrastructure and data flow. One of the standout features of Delta Live Tables is its robust monitoring capabilities. It provides a directed acyclic graph (DAG) of your entire data pipeline, visually representing data movement from source to final tables. This visibility is crucial for complex pipelines, helping data engineers and data scientists track data lineage and dependencies. Delta Live Tables is deeply integrated into the Databricks ecosystem, which also brings some challenges to customizing interfaces. We recommend teams carefully evaluate the compatibility of input and output interfaces before using Delta Live Tables.
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32. Elastisys Compliant Kubernetes
Elastisys Compliant Kubernetes is a specialized Kubernetes distribution designed to meet stringent regulatory and compliance requirements, particularly for organizations operating in highly regulated industries such as healthcare, finance and government. It has automated security processes, provides multicloud and on-premises support and is built on top of a zero-trust security architecture. The emphasis on built-in compliance with laws such as GDPR and HIPAA and controls like ISO27001 makes it an attractive option for companies that need a secure, compliant and reliable Kubernetes environment.
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33. FoundationDB
FoundationDB is a multi-model database, acquired by Apple in 2015 and then open-sourced in April 2018. The core of FoundationDB is a distributed key-value store, which provides strict serializability transactions. Since we first mentioned it in the Radar, it has seen significant improvements — including smart data distributions to avoid write hotspots, a new storage engine, performance optimizations and multi-region replication support. We're using FoundationDB in one of our ongoing projects and are very impressed by its unbundled architecture. This architecture allows us to scale different parts of the cluster independently. For example, we can adjust the number of transaction logs, storage servers and proxies based on our specific workload and hardware. Despite its extensive features, FoundationDB remains remarkably easy to run and operate large clusters.
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34. Golem
Durable computing, a recent movement in distributed computing, uses an architecture style of explicit state machine to persist the memory of serverless servers for better fault tolerance and recovery. Golem is one of the promoters of this movement. The concept can work in some scenarios, such as long-running microservices sagas or long-running workflows in AI agent orchestration. We've evaluated Temporal previously for similar purposes and Golem is another choice. With Golem you can write WebAssembly components in any supported language besides Golem being deterministic and supporting fast startup times. We think Golem is an exciting platform worth evaluating.
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35. Iggy
Iggy, a persistent message streaming platform written in Rust, is a relatively new project with impressive features. It already supports multiple streams, topics and partitions, at-most-once delivery, message expiry and TLS support over QUIC, TCP and HTTP protocols. Running as a single server, Iggy currently achieves high throughput for both read and write operations. With upcoming clustering and io_uring support, Iggy can be a potential alternative to Kafka.
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36. Iroh
Iroh is a relatively new distributed file storage and content delivery system that’s designed as an evolution of existing decentralized systems like IPFS (InterPlanetary File System). Both Iroh and IPFS can be used to create decentralized networks for storing, sharing and accessing content addressed using opaque content identifiers. However, Iroh removes some of the limitations of IPFS implementations such as having no maximum block size and providing a syncing mechanism for data via range-based set reconciliation over documents. The project's roadmap includes bringing the technology to the browser via WASM, which raises some intriguing possibilities for building decentralization into web applications. If you don't want to host your own Iroh nodes, you can use its cloud service, iroh.network. There are already several SDKs available in a variety of languages, and one goal is to be more user-friendly and easier to use than alternative IPFS systems. Even though Iroh is still in its very early days, it's worth keeping an eye on it, as it could become a significant player in the decentralized storage space.
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37. Large vision model (LVM) platforms
Large language models (LLMs) grab so much of our attention these days, we tend to overlook ongoing developments in large vision models (LVMs). These models can be used to segment, synthesize, reconstruct and analyze video streams and images, sometimes in combination with diffusion models or standard convolutional neural networks. Despite the potential for LVMs to revolutionize the way we work with visual data, we still face significant challenges in adapting and applying them in production environments. Video data, for instance, presents unique engineering challenges for collecting training data, segmenting and labeling objects, fine-tuning models and then deploying the resulting models and monitoring them in production. So, while LLMs lend themselves to simple chat interfaces or plain text APIs, a computer vision engineer or data scientist must manage, version, annotate and analyze large quantities of streaming video data; this work requires a visual interface. LVM platforms are a new category of tools and services — including V7, Nvidia Deepstream SDK and Roboflow — that have emerged to address these challenges. Deepstream and Roboflow are particularly interesting to us because they combine an integrated GUI development environment for managing and annotating video streams with a set of Python, C++ or REST APIs to invoke the models from application code.
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38. OpenBCI Galea
There is growing interest in the use of brain-computer interfaces (BCIs) and their potential application to assistive technologies. Non-invasive technologies using electroencephalography (EEG) and other electrophysical signals offer a lower risk alternative to brain implants for those recovering from injuries. Platforms are now emerging on which researchers and entrepreneurs can build innovative applications without having to worry about the low-level signal processing and integration challenges. Examples of such platforms are Emotive and OpenBCI which offer open-source hardware and software for building BCI applications. OpenBCI's latest product, the OpenBCI Galea, combines BCI with the capabilities of a VR headset. It gives developers access to an array of time-locked physiological data streams along with spatial positioning sensors and eye tracking. This wide range of sensor data can then be used to control a variety of physical and digital devices. The SDK supports a range of languages and makes the sensor data available in Unity or Unreal. We're excited to see this capability offered in an open-source platform so researchers have access to the tools and data they need to innovate in this space.
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39. PGLite
PGLite is a WASM build of a PostgreSQL database. Unlike previous attempts that required a Linux virtual machine, PGLite directly builds PostgreSQL to WASM, allowing you to run it entirely in the web browser. You can either create an ephemeral database in memory or persist it to disk via indexedDB. Since we last mentioned local-first applications in the Radar, the tooling has evolved considerably. With Electric and PGlite, you can now build reactive local-first applications on PostgreSQL.
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40. SpinKube
SpinKube is an open-source serverless run time for WebAssembly on Kubernetes. While Kubernetes offers robust auto-scaling capabilities, the cold start time of containers can still necessitate pre-provisioning for peak loads. We believe WebAssembly's millisecond startup time provides a more dynamic and flexible serverless solution for on-demand workloads. Since our previous discussion of Spin, the WebAssembly ecosystem has made significant advancements. We're excited to highlight SpinKube, a platform that simplifies the development and deployment of WebAssembly-based workloads on Kubernetes.
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41. Unblocked
Unblocked provides software development lifecycle (SDLC) asset and artifact discovery. It integrates with common application lifecycle management (ALM) and collaboration tools to help teams understand codebases and related resources. It improves code comprehension by delivering immediate, relevant context about the code, making it easier to navigate and understand complex systems. Engineering teams can securely and compliantly access discussions, assets and documents related to their work. Unblocked also captures and shares local knowledge that often resides with experienced team members, making valuable insights accessible to everyone, regardless of experience level.
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Unable to find something you expected to see?
Each edition of the Radar features blips reflecting what we came across during the previous six months. We might have covered what you are looking for on a previous Radar already. We sometimes cull things just because there are too many to talk about. A blip might also be missing because the Radar reflects our experience, it is not based on a comprehensive market analysis.
