Introduction
In a few previous posts on AI/ML, I mentioned that one of the benefits of MinIO is that you have tools for Versioning, Lifecycle Management, Object Locking, Object Retention and Legal Holds. These capabilities have a variety of uses. You may need a simple way to keep track of training experiments. You could also use these features to
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Introduction
In a previous post, I covered Building an ML Data Pipeline with MinIO and Kubeflow v2.0. The data pipeline I created downloaded US Census data to a dedicated instance of MinIO. This is different from the MinIO instance Kubeflow Pipelines (KFP) uses internally. We could have tried to use KFP’s instance of MinIO - however, this is
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Our latest YouTube training series is all about the MinIO Operator, which brings native support for deploying and managing MinIO deployments (“MinIO Tenants”) on a Kubernetes cluster. MinIO’s Mike Johnson (aka MJ) brings us through the 10-part video series to set the foundation of understanding Kubernetes before focusing on installing and configuring the MinIO Operator for Kubernetes, which will
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In this post we look at how search, and specifically OpenSearch can help us identify patterns or see trends in our ever growing data.
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This post first appeared in The New Stack.
Developers gravitate to technologies that are software defined, open source, cloud native and simple. That essentially defines object storage.
Introduction
Choosing the best storage for all phases of a machine learning (ML) project is critical. Research engineers need to create multiple versions of datasets and experiment with different model architectures. When a
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Most developers, engineers, architects and DevOps folks know MinIO. Not all know that the only thing we do is software-defined object storage. We don’t do file or block. We don’t offer a service, it is self-hosted.
Our focus is singular.
The result is that our object store is objectively, based on adoption, awards and customer feedback the best
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Apache Kafka and Apache Spark are two leading technologies used to build the streaming data pipelines that feed data lakes and lake houses. At a really high level, Kafka streams messages to Spark where they are transformed into a format that can be read in by applications and saved to storage.
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Build data pipelines with S3 to MinIO and MinIO to MinIO batch replication.
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Encryption is an important part of the MinIO architecture. MinIO applies encryption to ensure objects are secure at rest and are compliant with regulations.
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Engineers like to play and learn locally. It does not matter which tool is under investigation: a high-end storage solution, a workflow orchestration engine, or the latest thing in distributed computing. The best way to learn a new technology is to find a way to cram it all on a single machine so that you can put your hands on
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InfluxDB is built on the same ethos as MinIO. It is a single Go binary, cloud agnostic, lightweight, but is also feature packed with things like replication and encryption, and it provides integrations with various applications.
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88 GB/s writes in a 2U form factor for on-prem, colo and edge object storage.
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Kubeflow Pipelines (KFP) is the most popular feature of Kubeflow. A Python engineer can turn a function written in plain old Python into a component that runs in Kubernetes using the KFP decorators. If you used KFP v1, be warned - the programming model in KFP v2 is very different - however, it is a big improvement. Transforming plain old
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Kafka and Spark Structured Streaming are used together to build data lakes/lake houses fed by streaming data and provide real time business insights.
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Make you Kafka topics performant and efficient with Kafka Schema Registry.
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HDD failure rates create big complications for RAID arrays. Find out why erasure coding is a better option for data durability.
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Managing users, groups, and policies for security and functionality with MinIO.
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MinIO licensees gain access to SUBNET security features like long term support and policy reviews.
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