Any data source, one single access.


Blossom Sky manages decentralized data infrastructures, known as federated machine learning. Build your own Enterprise AI and increase performance up to 15 times with Blossom Sky Studio.
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How blossom works

Blossom Sky allows you to be curious about the world.

Benefit
connect data sources, enable generative AI, gain performance
- by running data processing and AI directly at independent data sources
List
enable data collaboration, increase efficiency, gain new insights
- by breaking data silos in a unified manner through a single system view
Mesh
access all data pools, train models on the source, increase probability
- by running training on any and over several data lakes and data formats

Explore the possibilities with Blossom Sky

Blossom Sky trains AI with ultra-sensitive data.

Blossom Sky trains AI with ultra-sensitive data.

Industries:

Health, Space, Public Sector

Blossom Sky pushes Unified Data Intelligence.

Blossom Sky pushes Unified Data Intelligence.

Industries:

Energy, Avionik, Automotive

Blossom Sky infuses semantic decisions.

Blossom Sky infuses semantic decisions.

Industries:

Retail, Public Agencies, Advertising

Enable agnostic federated learning to keep you ahead of the competition.

Blossom Sky's Privacy-Preserving Federated Learning Platform is the state of the art distributed platform for homogeneous and heterogeneous data model training. It provides the user with a complete control of its data while implementing a set of defenses tailored for distributed environments, all while providing a comprehensive, seamless user experience.
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The Blossom Sky Federated Learning platform has been designed to adapt to a wide variety of AI algorithms and models. Our platform is ready for user customization, and our open source technology is scalable to a wide range of possibilities. Blossom Sky supports a wide range of ML and AI algorithms, including Neuronal Networks, k-Means, Regression Decision Trees, Deep Learning Networks, Support Vector Machines and LLM based Generative AI.
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Explore the world with blossom sky
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The challenge of data lakes, data compliance and efficient AI model training

You need data available at any time to increase, but in a decentralized fashion, maximizing performance and data insights, while respecting everyone’s privacy. Sometimes it feels like a walk on a tightrope, and you feel like you can confidently fulfill one of these tasks, but hardly all of them.
Focus
Focus on your real job
Instead of having to prepare, clean, deduplicate and feed your data to intelligent systems before starting the analytics, with Blossom, you bring the intelligence to the data lakes directly. Hence, you will no longer have to deal with the heterogeneity of such systems, thanks to your able assistant Blossom.

You just code your applications on top of Blossom, and Blossom takes care of any required data movement and transformation. Thus, it provides you with the freedom to build your data driven idea and enables you to focus on the logic computation of your data analytics.
Speed
Benefit from decentralized processing performance
You need to perform a query over multiple datasets, stored in different formats? Well, good luck! The extent of transform the data to perform disparate queries is not only a back-breaking and highly time-consuming job, but also dangerously error-prone.

Blossom not only breaks up such complex analytics, but also selects the right data format to execute each query for you.
Invisible to its users, Blossom kindly complements the capabilities of data processing platforms with each other, thereby enabling them to perform complex analytics.
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The Benefits of Implementing Federated Learning

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Train AI models efficiently

save time and resources by achieving resilience and viable results quicker, even with limited databases.

Break data silos and tap into new data sources

enabling you to compute data from various different sources, formats and systems.

Efficient visual AI training composition

data sources are easily composed visually or programmatically and submitted by a single click or command line.

Data compliance over multiple data lakes

enjoy highly heterogeneous data in a homogenized and easy-to-read format, always respecting privacy policies.

Intelligent cross-platform selection

it automatically decides the best data processing platform to use to run data tasks and AI model training.

AI advisor for query composition

assisting you in achieving a more reliable and accurate outcome much faster.

Databloom is the perfect partner for your business. Put the power of AI to work and you will soon see why Databloom is a leader in scalable machine learning solutions.

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The future of intelligent data analytics

Blossom Sky is the key to unlocking the full value of your data. We can help you save time and money while increasing productivity by making it easy to leverage the power of your AI and machine learning tools.
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On point, on time
Time is the essence. Save time by doing everything you can to streamline the process and get to the point where your expertise is needed. Automate as much as possible to free up time and energy so that you can focus on the most valuable activities. Blossom Sky delivers all the support you need to give a stellar performance. Click the button below to experience Blossom's amazing features in a quick, free tour!
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Why users love Blossom Sky

“Love the automated determination and training of source data. Support for multicloud with a single tool. Low code and easy to integrate.”
Shaima H., MLOps
“What I like the most about this platform is its ease of use. One has to only express thebusiness logic within its API, and then the platform optimizes for the underlyingsystem usage. This way, one does not need to implement system-specific details.”
Haralampos G., Research Associate
"Blossom supports a wide array of data processing platforms. Seamless data analyticsacross sources. Easy to integrate into existing applications.”
Kaustubh B., Senior Data Analyst
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How the community talks about Wayang

Scaling with Apache Wayang
Apache Wayang (incubating) is an API for big data cross-platform processing. It provides an abstraction over other platforms like Apache Spark and Apache Flink as well as a default built-in stream-based “platform”. The goal is to provide a consistent developer experience when writing code regardless of whether a light-weight or highly-scalable platform may eventually be required. Execution of the application is specified in a logical plan which is again platform agnostic. Wayang will transform the logical plan into a set of physical operators to be executed by specific underlying processing platforms.

The JVM Programming Advent Calendar 2022
Gaining insights from our data with Blossom
"Blossom provides a middleware to run any data flow task on different platforms.I could execute my spark job on Flink by changing only one line of code. I also liked a lot the optimizer that can select the platform based on a cost model."
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databloom.ai on Capterra
“Wayang is a Java library typically used in Big Data applications. Incubator-wayang has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License, and it has low support. You can download it from GitHub.
In contrast to traditional data processing systems that provide one dedicated execution engine, Apache Wayang (incubating) is a cross-platform data processing system: Users can specify any data processing application using one of Wayang's APIs and then Wayang will choose the data processing platform(s), e.g., Postgres or Apache Spark, that best fits the application.”

kandi X-RAY (about Wayang, the API for Big Data)
“Execution of the application is specified in a logical plan which is again platform-agnostic. Wayang will transform the logical plan into a set of physical operators to be executed by specific underlying processing platforms.Wayang selects which platform(s) will run our application. It has numerous capabilities whereby cost functions and load estimators can be used to influence and optimize how the application is run. For our simple example, it is enough to know that even though we specified Java or Spark as options, Wayang knows that for our small data set, the Java streams option is the way to go.”

The Apache Software Foundation

Who is Databloom?

We are a remote business with an open culture that prioritizes individuals. Our solutions assist our clients accomplish their own objectives in addition to enabling and enhancing the data-driven economy.
2012
Back at Cloudera, Alexander and Jorge initially bonded. The research publications by Jorge and his colleagues eventually led to the development of the first in-memory query engine for Apache Hadoop and the study of distributed large data processing.
2015
Jorge and his associates at QRCI and HPI began looking into the subject of distributed data management and mesh data processing.
2016
The group led by Jorge created the first data mesh controller, Rheem, and presented the software stack at the Spark Summit in 2017, as well as at a number of conferences after that.
2019
When Jorge and Alexander reconnected, they both saw the enormous potential in working together and decided to establish a business to commercialize this technology. They then bootstrapped the subsequent development with the goal of creating the most complete data mesh platform.
2022
Databloom was founded, established operations in Miami, and pioneered 100% remote work in addition to 4 full workdays every week. Out of more than 4,000 looked at startups, Databloom placed in the Top 50 at the famous Pepperdine "Most Fundable Companies" competition. Databloom was highlighted in several international conferences.

About us

In 2022, the team founded DataBloom AI, Inc. in the United States to deal with the increased interest around the Bay Area, Florida and Texas.

Members of our team are frequent speakers at large conventions and meetups, like newWork summit, SXSW, Big Data World, Apache Con, BOSS, Developer Week, etc.
Datablom AI team

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Databloom is a leading contributor to Apache Wayang, working on "Blossom Sky," an AI-focused data mesh platform that will enable decentralized data processing on the edge in order to train large AI models directly at the source.
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© 2023 Databloom AI, Inc.
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