PyCon.DE Part 5: No Compromise: Using Ansible properly

Efficient DataFrame Storage with Apache Parquet

In our daily work as data scientists, we deal with a lot with tabular data, also called DataFrames. At Blue Yonder, we use Pandas DataFrames to analyze and transform the data we need. One of our main challenges when we integrate new systems … Read More

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A logo illustrating the construction on turbodbc with a crane and a hardhat

Making of turbodbc (part 1): Wrestling with the side effects of a C API

Databases are the core of modern information technology. Databases are used by web servers to store content, by businesses to place orders, and by data scientists as their data sources. To give Python users easy database access, the Python community … Read More

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Data Science and DevOps: A Success Story

What is “Data Science”? There are many definitions of what data science is though some are not really serious: “Data science is statistics on a Mac. ” @bigdataborat Or this one, which is surprisingly to the point: “Data scientist (n.): Person … Read More

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Apache Big Data

Blue Yonder at Apache Big Data Europe

At Blue Yonder, we make use of several Apache projects and actively contribute to three projects at the moment (Apache Aurora, Apache Arrow, and Apache Parquet) as project management committee (PMC) members and committers. The Apache Big Data Europe conference … Read More

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It’s fresh. It’s scientific. It’s free.

Blue Yonder’s python library tsfresh automatically extracts features from time series or sequences and it has just been released as an open source project. Time series: They’re everywhere The computerization and automation of manufacturing operations as part of Industry 4.0 … Read More

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Apache Aurora

Scalable Data Science: Apache Aurora at Blue Yonder

As a cloud-based provider, we at Blue Yonder care about the full lifecycle of predictive applications. It is not enough to build, train and run machine learning models once. We have to continuously operate and maintain them in order to … Read More

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