The recent announcement from AWS about the general availability of their new ARM-powered Graviton2 servers caused us to take another look at the performance of these ARM servers. In this blog post we describe the results which you may find surprising. Introduction MinIO [https://github.com/minio/minio] is an Apache licensed, open source S3-compatible object storage server with a
Read more...Introduction While MD5 hashing is no longer a good choice when considering a hash function, it is still being used in a great variety of applications. As such any performance improvements that can be made to the MD5 hashing speed are worth considering. Due to recent improvements in SIMD processing (AVX2 and especially AVX512) we are providing a Go md5-simd
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Veeam's V10 release adds object storage support in a big way. Find out how these two software stacks play together to deliver performance oriented backup and restore.
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Almost all of the modern cloud-native applications use HTTPs as their primary transport mechanism even within the network. Every service is a collection of HTTPs endpoints provisioned dynamically at scale. Traditional load balancers that are built for serving web applications across the Internet are at a disadvantage here since they use old school DNS round-robin techniques for load balancing and
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Introduction JSON has established itself as the "lingua franca" of the web. As such the parsing performance of JSON is hugely important for many applications. Despite the simple and human-friendly nature of JSON, it is not a technically trivial format to parse at high speeds. Recently some new designs have been presented one of which is simdjson [https://github.com/
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With another Strata in the rearview mirror, it is time to reflect on what we saw and heard during the week. Strata is clearly a data science show at this point but data science is broad topic. Our perspective, as a provider of high performance object storage, is framed accordingly and we focus on the data stack more than we
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The demise of Hadoop is probably overblown. It will not suddenly disappear from the enterprise landscape - there are simply too many clients, too much sunk investment for it to vanish into the night. What is not overblown is the fact that Hadoop, like countless technologies before it, is in secular, irreversible decline. There are a number of reasons but
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Few would argue with the statement that Hadoop HDFS is in decline. In fact, the HDFS part of the Hadoop ecosystem is in more than just decline - it is in freefall. At the time of its inception, it had a meaningful role to play as a high-throughput, fault-tolerant distributed file system. The secret sauce was data locality. By co-locating
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The power of scale [https://hbr.org/search?search_type=&term=scale&sort=popularity_score] is well-documented in the world of business. Cloud providers - Amazon in particular - have amassed extraordinary scale in a very short period of time. The cloud providers are now using this scale to rearchitect how enterprises interact with their data. They are remaking the
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Apache Spark is a framework for distributed computing. It provides one of the best mechanisms for distributing data across multiple machines in a cluster and performing computations on it. Spark achieves this by constructing data structures called RDDs (Resilient Distributed Datasets). RDDs allow data to be broken into disparate chunks and processed independently of one another. The individual chunks can
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The growth of Presto in the enterprise is a function of its speed, SQL compatibility, extensibility and enterprise feature set. While initially designed to speed up Hadoop, the success of the project has led to much broader adoption - on S3, Cassandra, MySQL, and more. Presto allows for data queries that traverse data stores and locations - a big plus
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Well written software is fast software. When MinIO was conceived it was designed from scratch to be simple, to scale (because simple things scale better) and to be fast. Simplicity and scale have their own subjective and objective measures - but fast is generally a numbers game. When you take well-written, fast software and pair it with fast hardware the
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High performance object storage is one of the hotter topics in the enterprise today. On the one hand, object storage has become an indispensable part of the enterprise storage strategy (public or private cloud) - carrying the vast, vast majority of the enterprise burden when measured in TBs or PBs. On the other hand, object storage has traditionally served a
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Software isn't usually described as bombproof. Particularly the type of software that is responsible for large analytic jobs or machine learning workloads. The words “finicky”, “complex” or in the case of good marketing “professional grade” (meaning you need years of study and multiple certifications) are more common. Bombproof software, however, is one of the many benefits associated with active open
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When one looks at the amazing roster of talks for the Spark + AI Summit, what you don’t see is a lot of discussion on how to leverage object storage. On some level you would expect to —
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One of the key challenges in any digitization journey is the adoption of machine learning techniques. Given the explosion of tools and frameworks, it can be difficult
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When early object storage APIs were developed they focused on the efficient storage and retrieval of objects. Amazon’s success with S3 and its implementation of the robust S3 API quickly became the de facto standard for object storage in the cloud. MinIO, recognizing this, invested heavily in creating the most compliant implementation of the S3 API outside of Amazon.
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How to use Apache Flink to build a private cloud data pipeline for a variety of use cases.
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In this post we’ll learn more about object storage, specifically Minio and then see how to connect Minio with tools like Apache Spark and Presto for analytics workloads.
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In the first part of this two post series, we’ll take a look at how object storage is different from other storage approaches and why it makes sense to leverage object storage like Minio for data lakes.
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