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Cheat sheets for our Python seminars

For our Python seminars we have created cheat sheets that allow the course participants to quickly reuse what they have learned:

Python Cheat Sheet

Python Cheat Sheet Python Cheat Sheet 2

python-for-beginners-cheat-sheet.pdf, PDF, 70.4 KB

Pandas Cheat Sheet

Pandas Cheat Sheet Pandas Cheat Sheet 2

pandas-cheat-sheet.pdf, PDF, 52 KB

Git Cheat Sheet

Git Cheat Sheet Git Cheat Sheet 2

git-cheatsheet-web.pdf, PDF, 437 KB

And if you have any further questions about our training courses, please give us a call on +49 30 22430082 or send us an email to training@cusy.io.

Are Jupyter notebooks ready for production?

Are Jupyter notebooks ready for production?

Jupyter Notebook

In recent years, there has been a rapid increase in the use of Jupyter notebooks, s.a. Octoverse: Growth of Jupyter notebooks, 2016-2019. This is a Mathematica- inspired application that combines text, visualisation, and code in one document. Jupyter notebooks are widely used by our customers for prototyping, research analysis and machine learning. However, we have also seen that the growing popularity has also helped Jupyter notebooks be used in other areas of data analysis, and additional tools have been used to run extensive calculations with them.

However, Jupyter notebooks tend to be inappropriate for creating scalable, maintainable, and long-lasting production code. Although notebooks can be meaningfully versioned with a few tricks, automated tests can also run, but in complex projects, mixing code, comments and tests becomes an obstacle: Jupyter notebooks can not be sufficiently modularized. Although notebooks can be imported as modules, these options are extremely limited: the notebooks must first be fully loaded into memory and a new module must be created before each cell can run in it.

As a result, it came to the first notebook war, which was essentially a conflict between data scientists and software engineers.

How To Bridge The Gap?

Notebooks are rapidly gaining popularity among data scientists and becoming the de facto standard for rapid prototyping and exploratory analysis. Above all, however, Netflix has created an extensive ecosystem of additional tools and services, such as Genie and Metacat. These tools simplify complexity and support a broader audience of analysts, scientists and especially computer scientists. In general, each of these roles depends on different tools and languages. Superficially, the workflows seem different, if not complementary. However, at a more abstract level, these workflows have several overlapping tasks:

data exploration occurs early in a project

This may include displaying sample data, statistical profiling, and data visualization

Data preparation

iterative task

may include cleanup, standardising, transforming, denormalising, and aggregating data

Data validation

recurring task

may include displaying sample data, performing statistical profiling and aggregated analysis queries, and visualising data

Product creation

occurs late in a project

This may include providing code for production, training models, and scheduling workflows

A JupyterHub can already do a good job here to make these tasks as simple and manageable as possible. It is scalable and significantly reduces the number of tools.

To understand why Jupyter notebooks are so compelling for us, we highlight their core functionalities:

  • A messaging protocol for checking and executing language-independent code
  • An editable file format for writing and capturing code, code output, and markdown notes
  • A web-based user interface for interactive writing and code execution and data visualisation

Use Cases

Of our many applications, notebooks are today most commonly used for data access, parameterization, and workflow planning.

Data access

First we introduced notebooks to support data science workflows. As acceptance grew, we saw an opportunity to leverage the versatility and architecture of Jupyter notebooks for general data access. Mid-2018, we started to expand our notebooks from a niche product to a universal data platform.

From the user’s point of view, notebooks provide a convenient interface for iteratively executing code, searching and visualizing data – all on a single development platform. Because of this combination of versatility, performance, and ease of use, we have seen rapid adoption across many user groups of the platform.

Parameterization

Along with increasing acceptance, we have introduced additional features for other use cases. From this work notebooks became simply paramatable. This provided our users with a simple mechanism to define notebooks as reusable templates.

Workflow planning

As a further area of notebook ​​applications, we have discovered the planning of workflows. They have the following advantages, among others:

  • On the one hand, notebooks allow interactive work and rapid prototyping and on the other hand they can be put into production almost without any problems. For this the notebooks are modularized and marked as trustworthy.
  • Another advantage of notebooks are the different kernels, so that users can choose the right execution environment.
  • In addition, errors in notebooks are easier to understand because they are assigned to specific cells and the outputs can be stored.

Logging

In order to be able to use notebooks not only for rapid prototyping but also for long-term productivity, certain process events must be logged so that, for example, errors can be diagnosed more easily and the entire process can be monitored. IPython Notebboks can use the logging module of the standard Python library or loguru, see also Jupyter-Tutorial: Logging.

Testing

There have been a number of approaches to automate the testing of notebooks, such as nbval, but with ipytest writing notebook tests became much easier, see also Jupyter Tutorial: ipytest.

Summary

Over the last few years, we have been promoting close collaboration between Software Engineers and data scientists to achieve scalable, maintainable and production-ready code. Together, we have found solutions that can provide production-ready models for machine learning projects as well.

Data protection in times of Covid-19

Companies and organizations have data that they do not want to make available to others. They also have a special responsibility for their customers, partners and employees. Not being sovereign of this data means not only a loss of trust, but usually also commercial losses.

Show your customers, partners and employees that data protection is important to you and that you take responsibility to protect their privacy. Show that you have implemented the rules of the European General Data Protection Regulation (GDPR) from May 2018.

Therefore, do without Google services and use alternatives. Google makes money from the data you provide Google:

With your permission you give us more information about you, about your friends, and we can improve the quality of our searches. We don’t need you to type at all. We know where you are. We know where you’ve been. We can more or less know what you’re thinking about. [1]

This statement by the Google CEO, Eric Schmidt, is more relevant than ever. It can get scary when you think that a company knows more or less what you think about. The group only reveals part of this information if you still have a Google account – saved graphs and other evaluations will remain hidden from you.

In the following we would like to introduce you to some privacy-friendly alternatives to Google services:

… for your office work

  • Jitsi instead of Google Hangout, Zoom or Microsoft Teams
  • Mattermost instead of Slack
  • Nextcloud and OnlyOffice instead of Google Docs, Google Sheets, Google Slides, Google Calendar and Google Drive

… for your website

… for your apps

For further reading

Telearbeit und Mobiles Arbeiten
Information from the Federal Commissioner for Data Protection and Freedom of information (BfDI), January 2019
Top Tips for Cybersecurity when Working Remotely
Article by the European Union Agency for Cybersecurity (ENISA), March 2020
Home-Office? – Aber sicher!
Information from the Federal Office for Information Security (BSI), March 2020

[1]Google’s CEO: ‹The Laws Are Written by Lobbyists›, 2010.

Migration from Jenkins to GitLab CI/CD

Our experience is that migrations are often postponed for a very long time because they do not promise any immediate advantage. However, when the tools used are getting on in years and no longer really fit the new requirements, technical debts accumulate that also jeopardise further development.

Advantages

The advantages of GitLab CI/CD over Jenkins are:

Seamless integration
GitLab provides a complete DevOps workflow that seamlessly integrates with the GitLab ecosystem.
Better visibility
Better integration also leads to greater visibility across pipelines and projects, allowing teams to stay focused.
Lower cost of ownership
Jenkins requires significant effort in maintenance and configuration. GitLab, on the other hand, provides code review and CI/CD in a single application.

Getting started

Migrating from Jenkins to GitLab doesn’t have to be scary though. Many projects have already been switched from Jenkins to GitLab CI/CD, and there are quite a few tools available to ease the transition, such as:

  • Run Jenkins files in GitLab CI/CD.

    A short-term solution that teams can use when migrating from Jenkins to GitLab CI/CD is to use Docker to run a Jenkins file in GitLab CI/CD while gradually updating the syntax. While this does not fix the external dependencies, it already provides better integration with the GitLab project.

  • Use Auto DevOps

    It may be possible to use Auto DevOps to build, test and deploy your applications without requiring any special configuration. One of the more involved tasks of Jenkins migration can be converting pipelines from Groovy to YAML; however, Auto DevOps provides predefined CI/CD configurations that create a suitable default pipeline in many cases. Auto DevOps offers other features such as security, performance and code quality testing. Finally, you can easily change the templates if you need further customisation.

Best Practices

  • Start small!

    The Getting Started steps above allow you to make incremental changes. This way you can make continuous progress in your migration project.

  • Use the tools effectively!

    Docker and Auto DevOps provide you with tools that simplify the transition.

  • Communicate transparently and clearly!

    Keep the team informed about the migration process and share the progress of the project. Also aim for clear job names and design your configuration in such a way that it gives the best possible overview. If necessary, write comments for variables and code that is difficult to understand.

Let me advise you

I will be happy to advise you and create a customised offer for the migration of your Jenkins pipeline to GitLab CI/CD.

Veit Schiele

Veit Schiele
Phone: +49 30 22430082

I will also be happy to call you!

Request now

Atlassian discontinues the server product range

Atlassian announced in mid-October 2020 that it would completely discontinue its server product line for the products Jira, Confluence, Bitbucket and Bamboo on 2 February 2021. Existing server licences will still be able to be used until 2 February 2024, although it is doubtful that Atlassian will actually continue to provide extensive support until 2 February 2024.

The product series will be phased out in stages:

  • 2 February 2021: New server licences will no longer be sold and price increases will come into effect.
  • 2 February 2022: Upgrades and downgrades will no longer be possible
  • 2 February 2023: App purchases for existing server licences will no longer be possible
  • 2 February 2024: End of support

While Atlassian recommends migrating to the cloud, many of our customers refuse to do so due to business requirements or data protection reasons. We work with our customers to analyse the requirements of their existing Jira, Confluence, Bitbucket and Bamboo servers and then develop suitable migration plans, e.g. to GitLab.

Let me advise you

Even if you are not yet a customer of ours, I will be happy to advise you and create a customised offer for the migration of your Atlassian servers.

Veit Schiele

Veit Schiele
Phone: +49 30 22430082

I will also be happy to call you!

Request now