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python

Coiled Webinar ‘Science Thursday’: Data Processing at Blue Yonder - One Supply Chain at a Time

Coiled Webinar “Science Thursday”: Data Processing at Blue Yonder - One Supply Chain at a Time

Python calling C++

Introducing Cube Functionality To Kartothek

Dask Usage at Blue Yonder

Karlsruhe to D.C. ― a Dask story

Write-Up of the PyConDE & PyData Berlin 2019 conference

Run-length Encoding for Pandas

Not All Problems Are of Your Own Making - a Case Study in Tracking down a Dask Data Loss Bug

Introducing Kartothek - Consistent parquet table management powered by Apache Arrow and Dask

PyCon.DE 2018 Part 8: Grammar of Graphics in Python

Part 8 of our PyCon.DE 2018 series introduces the concepts behind the grammar of graphics and how to use them in Python by means of the Altair library and th...

PyCon.DE 2018 Part 7: Strongly typed datasets in a weakly typed world

In Part 7 of our PyCon.DE 2018 series, we will have a look at the issues with exchanging data in a Pandas-driven environment, where types are rather unstable...

PyCon.DE 2018 Part 6: Where the heck is my memory?

The 6th Part of the PyCon.DE 2018 series is about the Python memory management and why you should know a few details about it even while writing pure Python ...

PyCon.DE 2018 Part 5: Cloud chat bot for lazy people

Part 5 of our PyCon.DE 2018 series is about a cloud-based chatbot which will answer easy questions like the health of a service the owner of the bot is respo...

PyCon.DE 2018 Part 4: Fulfilling Apache Arrow’s Promises: Pandas on JVM memory without a copy

In Part 4 of the PyCon.DE 2018 series, we will discuss how to use Pandas, Apache Arrow and Python to reduce the overhead of working with columnar data betwee...

PyCon.DE 2018 Part 3: Python with and without Pants

In the 3rd Part of the PyCon.DE 2018 series, we will talk about building, packaging, and deploying Python code with Pants and PEX.

PyCon.DE 2018 Part 2: Python Dependency Management

Part 2 of our PyCon.DE 2018 series is about the current state of dependency management in Python: Which tools are present and how can they be used?

PyCon.DE 2018 Part 1: Scalable Scientific Computing using Dask

In Part 1 of the PyCon.DE 2018 series, we show how to avoid the limitation (only using the main memory of a single machine and most of the time also only a s...

Oxidizing Python: Speeding up URL quoting by 10x using Rust

Motivation

Meet blueyonder.ai @ EuroSciPy & SRECon 2018

A group of Blue Yonder data scientists and engineers will be at the EuroSciPy 2018 in Trento, Italy from August 28th to September 1st as well as at the SREC...

PyCon.DE Part 5: Using Ansible properly

PyCon.DE Part 4: Observing applications with Sentry and Prometheus

In Part 4 of our PyCon.DE series we´ll demonstrate how to observe your applications with Sentry and Prometheus. If you have services running in production, s...

PyCon.DE Part 3: Connecting Python to Big Data Landscapes using Arrow and Parquet

At last year´s PyCon.DE, from October 25th till 27th, Blue Yonder held several interesting talks around Python. In case you missed out the event, we would li...

PyCon.DE Part 2: The Snake in the Tar Pit - Complex Systems with Python

The Zen of Python motivates us to build software that is easy to maintain and to extend. In reality however, we often end up with systems that are quite the ...

PyCon.DE Part 1: Turbodbc - Turbocharged database access for data scientists

At last year´s PyCon.DE, from October 25th till 27th, Blue Yonder held several interesting talks around Python. In case you missed out the event, we would li...

Open text files in legacy Python

At a first glance, opening files in Python is easy. All you have to do is call the built-in function open() and then you start reading from the file. However...

Turbodbc and Apache Arrow

Public cloud cost control with Prometheus

Over the past decade, the entire IT industry has been pushing toward cloud computing. A recent article by Gartner argues the IT industry will be entering a s...

Making of turbodbc (part 2): C++ to Python

In the first part of this series, we learned how turbodbc builds a few layers of C++ code on top of the open database connectivity (ODBC) C API. We ended up ...

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 t...

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 scien...

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technology

Ensuring Code Quality: A Guide to Dynamic GitHub Pull Request Gates

Coiled Webinar ‘Science Thursday’: Data Processing at Blue Yonder - One Supply Chain at a Time

Coiled Webinar “Science Thursday”: Data Processing at Blue Yonder - One Supply Chain at a Time

Java: Giants and Infinite Loops

Python calling C++

Introducing Cube Functionality To Kartothek

Dask Usage at Blue Yonder

Karlsruhe to D.C. ― a Dask story

Run-length Encoding for Pandas

Not All Problems Are of Your Own Making - a Case Study in Tracking down a Dask Data Loss Bug

Introducing Kartothek - Consistent parquet table management powered by Apache Arrow and Dask

Oxidizing Python: Speeding up URL quoting by 10x using Rust

Motivation

Open text files in legacy Python

At a first glance, opening files in Python is easy. All you have to do is call the built-in function open() and then you start reading from the file. However...

Turbodbc and Apache Arrow

Public cloud cost control with Prometheus

Over the past decade, the entire IT industry has been pushing toward cloud computing. A recent article by Gartner argues the IT industry will be entering a s...

Making of turbodbc (part 2): C++ to Python

In the first part of this series, we learned how turbodbc builds a few layers of C++ code on top of the open database connectivity (ODBC) C API. We ended up ...

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 t...

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 scien...

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 learn...

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conference

Write-Up of the PyConDE & PyData Berlin 2019 conference

PyCon.DE 2018 Part 8: Grammar of Graphics in Python

Part 8 of our PyCon.DE 2018 series introduces the concepts behind the grammar of graphics and how to use them in Python by means of the Altair library and th...

PyCon.DE 2018 Part 7: Strongly typed datasets in a weakly typed world

In Part 7 of our PyCon.DE 2018 series, we will have a look at the issues with exchanging data in a Pandas-driven environment, where types are rather unstable...

PyCon.DE 2018 Part 6: Where the heck is my memory?

The 6th Part of the PyCon.DE 2018 series is about the Python memory management and why you should know a few details about it even while writing pure Python ...

PyCon.DE 2018 Part 5: Cloud chat bot for lazy people

Part 5 of our PyCon.DE 2018 series is about a cloud-based chatbot which will answer easy questions like the health of a service the owner of the bot is respo...

PyCon.DE 2018 Part 4: Fulfilling Apache Arrow’s Promises: Pandas on JVM memory without a copy

In Part 4 of the PyCon.DE 2018 series, we will discuss how to use Pandas, Apache Arrow and Python to reduce the overhead of working with columnar data betwee...

PyCon.DE 2018 Part 3: Python with and without Pants

In the 3rd Part of the PyCon.DE 2018 series, we will talk about building, packaging, and deploying Python code with Pants and PEX.

PyCon.DE 2018 Part 2: Python Dependency Management

Part 2 of our PyCon.DE 2018 series is about the current state of dependency management in Python: Which tools are present and how can they be used?

PyCon.DE 2018 Part 1: Scalable Scientific Computing using Dask

In Part 1 of the PyCon.DE 2018 series, we show how to avoid the limitation (only using the main memory of a single machine and most of the time also only a s...

Meet blueyonder.ai @ EuroSciPy & SRECon 2018

A group of Blue Yonder data scientists and engineers will be at the EuroSciPy 2018 in Trento, Italy from August 28th to September 1st as well as at the SREC...

PyCon.DE Part 5: Using Ansible properly

PyCon.DE Part 4: Observing applications with Sentry and Prometheus

In Part 4 of our PyCon.DE series we´ll demonstrate how to observe your applications with Sentry and Prometheus. If you have services running in production, s...

PyCon.DE Part 3: Connecting Python to Big Data Landscapes using Arrow and Parquet

At last year´s PyCon.DE, from October 25th till 27th, Blue Yonder held several interesting talks around Python. In case you missed out the event, we would li...

PyCon.DE Part 2: The Snake in the Tar Pit - Complex Systems with Python

The Zen of Python motivates us to build software that is easy to maintain and to extend. In reality however, we often end up with systems that are quite the ...

PyCon.DE Part 1: Turbodbc - Turbocharged database access for data scientists

At last year´s PyCon.DE, from October 25th till 27th, Blue Yonder held several interesting talks around Python. In case you missed out the event, we would li...

Blue Yonder at PyCon.DE 2017 and PyData Karlsruhe

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 Parqu...

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data-engineering

Coiled Webinar ‘Science Thursday’: Data Processing at Blue Yonder - One Supply Chain at a Time

Coiled Webinar “Science Thursday”: Data Processing at Blue Yonder - One Supply Chain at a Time

Introducing Cube Functionality To Kartothek

Dask Usage at Blue Yonder

Karlsruhe to D.C. ― a Dask story

Run-length Encoding for Pandas

Not All Problems Are of Your Own Making - a Case Study in Tracking down a Dask Data Loss Bug

Introducing Kartothek - Consistent parquet table management powered by Apache Arrow and Dask

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c++

Python calling C++

Making of turbodbc (part 2): C++ to Python

In the first part of this series, we learned how turbodbc builds a few layers of C++ code on top of the open database connectivity (ODBC) C API. We ended up ...

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 scien...

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data-science

Data Science and DevOps: A Success Story

What is “Data Science”?

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.

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azure

Public cloud cost control with Prometheus

Over the past decade, the entire IT industry has been pushing toward cloud computing. A recent article by Gartner argues the IT industry will be entering a s...

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rust

Oxidizing Python: Speeding up URL quoting by 10x using Rust

Motivation

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announcement

Blue Yonder joins JDA

In the month of July 2018, JDA announced the acquisition of Blue Yonder. As part of the integration we are happy to announce that the Blue Yonder Tech blog w...

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