Skip to content. | Skip to navigation

solving complex IT problems!

Personal tools

You are here: Home / Blog

Blog

Feeds

RSS 1.0-, RSS 2.0- oder Atom-Feed.

Python Pattern Matching in Linux Magazine 05/2021

The originally object-oriented programming language Python is to receive a new feature in version 3.10, which is mainly known from functional languages: pattern matching. The change is controversial in the Python community and has triggered a heated debate.
Python Pattern Matching in Linux Magazine 05/2021

Linux Magazin 05/2021

The originally object-oriented programming language Python is to receive a new feature in version 3.10, which is mainly known from functional languages: pattern matching. The change is controversial in the Python community and has triggered a heated debate.

Pattern matching is a symbol-processing method that uses a pattern to identify discrete structures or subsets, e.g. strings, trees or graphs. This procedure is found in functional or logical programming languages where a match expression is used to process data based on its structure, e.g. in Scala, Rust and F#. A match statement takes an expression and compares it to successive patterns specified as one or more cases. This is superficially similar to a switch statement in C, Java or JavaScript, but much more powerful.

Python 3.10 is now also to receive such a match expression. The implementation is described in PEP (Python Enhancement Proposal) 634. [1] Further information on the plans can be found in PEP 635 [2] and PEP 636 [3]. How pattern matching is supposed to work in Python 3.10 is shown by this very simple example, where a value is compared with several literals:

def http_error(status):
      match status:
          case 400:
              return "Bad request"
          case 401:
              return "Unauthorized"
          case 403:
              return "Forbidden"
          case 404:
              return "Not found"
          case 418:
              return "I'm a teapot"
          case _:
              return "Something else"

In the last case of the match statement, an underscore _ acts as a placeholder that intercepts everything. This has caused irritation among developers because an underscore is usually used in Python before variable names to declare them for internal use. While Python does not distinguish between private and public variables as strictly as Java does, it is still a very widely used convention that is also specified in the Style Guide for Python Code [4].

However, the proposed match statement can not only check patterns, i.e. detect a match between the value of a variable and a given pattern, it also rebinds the variables that match the given pattern.

This leads to the fact that in Python we suddenly have to deal with Schrödinger constants, which only remain constant until we take a closer look at them in a match statement. The following example is intended to explain this:

NOT_FOUND = 404
retcode = 200

match retcode:
    case NOT_FOUND:
        print('not found')

print(f"Current value of {NOT_FOUND=}")

This results in the following output:

not found
Current value of NOT_FOUND=200

This behaviour leads to harsh criticism of the proposal from experienced Python developers such as Brandon Rhodes, author of «Foundations of Python Network Programming»:

If this poorly-designed feature is really added to Python, we lose a principle I’ve always taught students: “if you see an undocumented constant, you can always name it without changing the code’s meaning.” The Substitution Principle, learned in algebra? It’ll no longer apply.

— Brandon Rhodes on 12 February 2021, 2:55 pm on Twitter [5]

Many long-time Python developers, however, are not only grumbling about the structural pattern-matching that is to come in Python 3.10. They generally regret developments in recent years in which more and more syntactic sugar has been sprinkled over the language. Original principles, as laid down in the Zen of Python [6], would be forgotten and functional stability would be lost.

Although Python has defined a sophisticated process with the Python Enhancement Proposals (PEPs) [7] that can be used to collaboratively steer the further development of Python, there is always criticism on Twitter and other social media, as is the case now with structural pattern matching. In fact, the topic has already been discussed intensively in the Python community. The Python Steering Council [8] recommended adoption of the Proposals as early as December 2020. Nevertheless, the topic only really boiled up with the adoption of the Proposals. The reason for this is surely the size and diversity of the Python community. Most programmers are probably only interested in discussions about extensions that solve their own problems. The other developments are overlooked until the PEPs are accepted. This is probably the case with structural pattern matching. It opens up solutions to problems that were hardly possible in Python before. For example, it allows data scientists to write matching parsers and compilers for which they previously had to resort to functional or logical programming languages.

With the adoption of the PEP, the discussion has now been taken into the wider Python community. Incidentally, Brett Cannon, a member of the Python Steering Council, pointed out in an interview [9] that the last word has not yet been spoken: until the first beta version, there is still time for changes if problems arise in practically used code. He also held out the possibility of changing the meaning of _ once again.

So maybe we will be spared Schrödinger’s constants.


[1]PEP 634: Specification
[2]PEP 635: Motivation and Rationale
[3]PEP 636: Tutorial
[4]https://pep8.org/#descriptive-naming-styles
[5]@brandon_rhodes
[6]PEP 20 – The Zen of Python
[7]Index of Python Enhancement Proposals (PEPs)
[8]Python Steering Council
[9]Python Bytes Episode #221

On the return of Richard Stallman to the board of the Free Software Foundation

Cusy has supported free and open source projects for many years, including those managed by the Free Software Foundation (FSF). Given the circumstances surrounding Richard Stallman’s resignation in 2019, Cusy is appalled that he has now returned to the FSF board. As a result, we will no longer participate in any FSF-hosted events. In addition, many Cusy contributors have indicated that they too no longer plan to participate in FSF-led or supported events.

In 2019, Stallman was forced to resign from his board position at the FSF as well as his position at MIT due to public pressure following his controversial comments on the Jeffrey Epstein child trafficking scandal. At the time, we hoped that the opportunity created by his resignation would be used by the Free Software Foundation to transform itself into a diverse and inclusive organisation. Unfortunately, the FSF has taken few steps in this direction. Richard Stallman’s return now dashed our hopes for the time being, and the Board’s preliminary statement also sounds less than credible to us.

Stallman himself was allowed to announce his return to the board of the Free Software Foundation:

Some of you will be happy at this, and some might be disappointed but who knows. In any case, that’s how it is and I’m not planning to resign a second time.

– Richard Stallman at LibrePlanet 2021 [1]

On its website, however, the FSF emphasises:

Free software is only as successful as the communities that create, use, support, and advocate for it.

It is overtly clear that the FSF must change fundamentally and permanently to regain the squandered trust of the free software movement and to fulfil its statutory, public good mission.


[1]Video

Security gaps and other incalculable risks at Microsoft – time to switch to open source

Tens of thousands of Microsoft Exchange servers in Germany are vulnerable to attack via the internet and are very likely already infected with malware. But Microsoft 365 and the Azure Cloud are also struggling with massive problems. Suitable open source alternatives could offer a remedy here.

Multiple critical vulnerabilities in Microsoft Exchange servers

The German Federal Office for Information Security (BSI) rates the current vulnerabilities in MS Exchange as a business-critical IT threat situation with massive impairments of regular operations [1]. But that is not enough:

In many infrastructures, Exchange servers have a large number of permissions in the Active Directory by default (sometimes unjustified). It is conceivable that further attacks with the rights of a taken-over Exchange server could potentially compromise the entire domain with little effort. It must also be taken into account that the vulnerability could also be exploited from the local network if, for example, an attacker gains access to Outlook Web Access via an infected client.

– BSI: Mehrere Schwachstellen in MS Exchange, 17.03.2021

The Computer Emergency Response Team of the Federal Administration (CERT-Bund) considers a compromise of an Outlook Web Access server (OWA) accessible from the Internet to be probable as of 26 February [2]. If admins now apply the security patches, the known security gaps will be closed, but a possible malware will not be eliminated. In addition, it is necessary to check whether servers in the domain have already been the target of such an attack and, for example, whether backdoors have already been opened.

Even today, about 12,000 of 56,000 Exchange servers with open OWA in Germany are said to be vulnerable to ProxyLogon [3] and another 9,000 Exchange servers have been taken offline or prevented from accessing OWA from the Internet in the last two weeks [4]:

Exchange server with open OWA in Germany. Vulnerability for ProxyLogon (CVE-2021-26855 et al.). Status: 16.03.2021

Source: CERT-Bund

Only a month ago, the CERT-Bund warned that even one year after the release of the security update, 31-63% of Exchange servers in Germany with OWA openly accessible from the internet were still vulnerable to the critical vulnerability CVE-2020-0688 [5].

Problems also with Microsoft 365 and Azure Cloud

Several Microsoft services failed on the evening of 15 March 2021. The problems were caused by an automatically deleted key in the Azure Active Directory (AAD). This prevented users from logging in for hours. This also affected other services that are based on the Azure Cloud, including Microsoft Teams, XBox Streams and Microsoft Dynamics. In September 2020 and February 2019, there were already outages of Microsoft cloud services lasting several hours, which were also due to errors in the Azure Active Directory Service (AAD) [6].

However, these were by no means the only problems with Microsoft 365 and the Azure Cloud: according to a study by Sapio Research on behalf of Vectra AI [7] of 1,112 IT security decision-makers in companies that use Office 365 and have more than a thousand employees, attackers can take over Office 365 accounts in most companies.

The reason for the study was that remote working has become normal as a result of the global Corona pandemic and companies have quickly moved to the cloud. Office 365 has been a common choice. However, with 258 million users, they have also become a tempting target for cyber attacks. According to the survey, in the last 12 months

  • 82% have experienced an increased security risk to their organisation
  • 70% have seen the takeover of accounts of authorised users, with an average of seven accounts hijacked
  • 58% have seen a widening gap between the capabilities of attackers and defenders.

Most respondents see a growing threat in moving data to the cloud. Above all, the effort required to secure the infrastructure was initially underestimated.

Microsoft vs. privacy

Finally, the State Commissioner for Data Protection in Mecklenburg-Western Pomerania and the State Audit Office are now demanding that the state government stop using Microsoft products with immediate effect:

Among others, products of the company Microsoft are affected. However, a legally compliant use of these products solely on the basis of standard data protection clauses is not possible due to the principles established by the European Court of Justice. Without further security measures, personal data would be transmitted to servers located in the USA.

– Press release of the State Commissioner for Data Protection and Freedom of Information Mecklenburg-Western Pomerania, 17.03.2021 [8].

In fact, however, this demand does not come as a surprise: The Conference of the Independent Data Protection Authorities of the Federation and the States already pointed out these dangers in 2015 [9].

Consequently, the State Audit Office of Mecklenburg-Western Pomerania concludes that software that cannot be used in compliance with the law cannot be operated economically.

Outlook

The news of the last few days make it clear that Microsoft can be operated neither securely nor reliably even if the operating costs were significantly increased. Finally, Microsoft services are not likely to be used in a legally compliant manner in Europe. With open source alternatives, more transparency, security and digital sovereignty could be achieved again. For example, last year we began advising a large German research institution on alternatives to Microsoft 365: Microsoft alternatives – migration to open source technologies. In the process, we are gradually replacing Microsoft products with OpenSource products over several years.

Update: 13 April 2021

There are again vulnerabilities in Microsoft Exchange Server, for which Microsoft has published updates for Exchange Server as part of its patch day [10]. This should be installed immediately. The BSI considers this to be a security vulnerability with increased monitoring of anomalies with temporary impairment of regular operation [11].

Springer Nature: Lemma management

The lemma management system is used to manage the contents of the following encyclopaedias:

A distinction is made between the following article types:

  • Lemma/Sublemma
  • Author/co-author
  • Volume
  • Subject

Rust for cryptography

The programming language Rust [1] is becoming more and more popular and is increasingly used for cryptography. In Rust’s favour is the fact that the language promises very secure memory management, making errors such as buffer overflows and use-after-free less likely. Considering one of the best-known TLS vulnerabilities, the OpenSSL Heartbleed bug [2], which violates memory security, this development is not surprising.

For example, a new TLS backend with Rustls [3] was recently announced for the curl library [4]. Hyper [5], an HTTP library written in Rust, is also to be made available as a backend for curl [6].

The Internet Security Research Group (ISRG) [7] also announced that they will support a Rust-based TLS module for the Apache web server [8]. This is funded as part of Google’s and the ISRG’s efforts to move ports of critical open source software into memory-safe languages [9].

The move of the cryptography package from Python, however, led to heated discussions in the community, as especially some older platforms would no longer be supported without the Rust compiler [10]. The cryptography [#]_project has already started to reimplement parts of its ASN1 parsing code in Rust, [11] [12] as ASN1 parsers often had memory security vulnerabilities in the past.


[1]Rust
[2]The Heartbleed Bug
[3]Rustls
[4]curl supports rustls
[5]Hyper
[6]Rust in curl with hyper
[7]Internet Security Research Group
[8]A Memory Safe TLS Module for the Apache HTTP Server
[9]Google Security Blog: Mitigating Memory Safety Issues in Open Source Software
[10]Dependency on rust removes support for a number of platforms #5771
[11]github.com/pyca/cryptography
[12]Port a tiny tiny bit of the ASN.1 parsing to Rust
[13]Rust in pyca/cryptography

«Trust is the new oil» in iX 03/2021

How can software developers and operators gain the trust of users in the age of Big Data?
«Trust is the new oil» in iX 03/2021

«Trust is the new oil» in iX 03/2021

This is the central question of this article [1].

No growth without trust

In their European Data Market Monitoring Tool Report [2], the International Data Corporation (IDC) looked at the impact of a climate of trust on the European data economy. They map three different scenarios, whereby they see the most significant factor as the political framework conditions, more precisely data protection and privacy, the common digital market and openness, standardisation and interoperability of data. They expect the greatest growth with a globally applicable GDPR. So it becomes clear: it is not data that is the oil of the digital age, no, it is trust.

Incisive – the Corona Warning App

When, with the corona pandemic, the German government and the Robert Koch Institute (RKI) decided to introduce a contact tracing app, data protection moved into the consciousness of the general population: many feared being stigmatised as COVID-19 patients if this information was passed on to government agencies. Widespread public mistrust of the Corona warning app would have prevent the success. After all, in order to effectively break chains of infection, as many people as possible must use the tracing app. So a completely new question arose: How can we design the software so that enough people trust the app and use it every day?

In April, the Chaos Computer Club published ten touchstones for the assessment of contact tracing apps [3]. For the CCC, the social requirements include purpose limitation, voluntariness and non-discrimination, the protection of privacy as well as transparency and auditability. The technical requirements are the absence of a central entity that must be trusted, data economy, anonymity, no linking with personal data, movement and contact profiles, and confidentiality of communication. The federal government and the RKI did the only right thing: they listened to the advice of the data protectionists and decided – contrary to the original planning – in favour of such a decentralised and anonymous procedure. As expected, trust in the Corona warning app ends at the limits of the operating systems. Whoever controls the technical infrastructure has the power. And it still seems questionable whether and how European data protection standards can ever be enforced against US corporations.

Transparency and openness

In order to be able to judge in an informed way whether a programme fulfils its purpose and has no hidden functions, the source code must be publicly accessible. To ensure full transparency, not only the source code of the programme must be publicly accessible, but also all libraries, protocols and interfaces that a programme uses. The entire software architecture should be based on the open source model for this purpose.

Privacy by design, decentralisation and data sovereignty

If the first two requirements are met, the public can decide whether a software system offers privacy by design or not. Trustworthy software must protect the privacy of users from the outset.

No more blind trust

For decades, users have blindly trusted software manufacturers and service providers. This is different today:

Software must earn our trust. The future therefore belongs to transparent open source development models that guarantee privacy by design and comprehensive data protection.

– Veit Schiele, founder and CEO of the Cusy GmbH


[1]iX 03/2021: Vertrauen ist das neue Öl – Consent Management in der Softwareentwicklung
[2]IDC: How the power of data will drive EU economy. The European Data MarketMonitoring Tool Report
[3]CCC: 10 Prüfsteine für die Beurteilung von „Contact Tracing“-Apps

GitLab as quality management system for ISO 13485

The international standard ISO 13485 defines the requirements for the design, manufacture and marketing of medical devices. GitLab offers many functions and controls that can be used to meet the requirements of ISO 13485.

Note

The information provided on this website is for informational purposes only. This information is not legal advice. It’s not comprehensive and does not in itself guarantee compliance with ISO 13485. To achieve ISO 13485 compliance, we recommend consulting with specialists.

Create and manage documentation

The GitLab Wiki can be used as a documentation system. Wiki pages can be created and managed via a user-friendly web interface or Git for advanced users. A complete history is guaranteed via version management with Git.

Define and enforce processes

GitLab provides several tools for enforcing processes, standards, reviews and approvals:

  • Permissions for merge requests can be used to enforce review of changes before merging.
  • Protected development branches can be used to define rules for creating, moving and deleting.
  • Requirements management can be enabled in a structured way using issues, labels and relations, with processes enforced and reviewed in a traceable way.

Software validation according to ISO 13485

For the validation of QM software, procedures for the evaluation have to be created. GitLab facilitates this via templates within you can upload forms and other files, create task lists and much more. All actions, including changing the description or commenting, are logged.

Task lists and Kanban boards provide an easy way to organise validation.

GitLab Compliance Features

GitLab can be configured so that one instance ensures general compliance standards.
SSH keys
Minimum SSH key requirements can be set at the instance level.
Fine-grained user roles and flexible permissions

Authorisations can be managed via five different roles. Assignments can be made differently not only for the instance, but also for groups and projects. The following table gives an overview of the essential authorisations:

Action Guest Reporter Developer Maintainer Owner
comment
View code
View GitLab pages
View wiki pages
View job list
View job protocol
Create issues
Assign issues  
Assign reviewer  
Assign labels  
Manage labels  
Lock issues  
Manage issue tracker  
View commit status  
View container registry  
View environments  
View list of merge requests  
Create merge request  
View CI/CD analysis  
Manage token      
Change security level        
Move project        
Rename project        
Delete Project        
Archive project        
Delete issues        
Delete pipelines        
Deactivate notification mails        
Force acceptance of terms of use
Acceptance of the terms of use can be enforced instance-wide.

IEC 62304 compliance with GitLab

The international standard IEC 62304 defines the requirements for the life cycle of medical device software. It prescribes processes, activities and tasks to improve the safety and effectiveness of a medical device through a comprehensive, risk-based approach to software development. In doing so, GitLab, as a platform that provides all the tools from project planning to safety testing, can contribute well to compliance with the requirements of IEC 62304.

Note

The information provided on this website is for informational purposes only. This information is not legal advice. It’s not comprehensive and does not in itself guarantee IEC 62304 compliance. To achieve IEC 62304 compliance, we recommend consulting with specialists.

Process control

Software development plans and processes can be created, managed and referenced using the GitLab Wiki. It can be used as a comprehensive documentation system to seamlessly reference and integrate your plans and processes throughout the software development lifecycle.

Requirements management

Templates can be created for system, development and customer requirements and easily incorporated into the development process. Task lists and Kanban boards provide both developers and reviewers with easy ways to plan and track tasks.

To enforce requirements and coding standards, you can use Merge Reuqests, an approval process that only allows authorised reviewers to merge the changes made. Using protected branches, you can set detailed permissions on who can make changes and where.

GitLab configured as a service desk allows interaction with customers and external stakeholders to get feedback from them and interact with them.

Traceability

Traceability can be maintained through labels and relations between issues throughout the software development lifecycle. Labels not only allow tasks and merge requests to be facilitated, but notification can also be ensured by subscribing to individual labels.

With the activity log, it remains traceable when which changes were made and by whom.

Risk management

Every code change can be automatically checked for security vulnerabilities with static program analysis. After a scan, a report is generated directly at the merge request, ensuring traceability.

Once risks have been identified, efforts to address them can be planned and documented with issues.

Compliance management with Gitlab

A compliance management system comprises all measures, structures and processes that are to be carried out in compliance with rules. With GitLab, compliance management can be realised that integrates seamlessly into the software development process and can be connected to other systems. This makes it easier for teams to keep up with changing regulations and emerging risks.

In detail, GitLab supports

  • managing rules and policies
  • automating compliance workflows
  • audit management that logs activities, identifies incidents and proves compliance with rules
  • security management that checks the security of the source code to track and manage vulnerabilities (→ DevSecOps).

Policy management

Rules and policies to be followed can be defined, both internal company policies and policies based on legal or regulatory frameworks such as GDPR, SOC2, PCI-DSS, SOX, HIPAA, ISO, COBIT, FedRAMP, etc. GitLab provides the following functions for this purpose:

Fine-grained user roles and permissions
GitLab supports five different roles with different permissions
Compliance settings
Different compliance policies can be set for different projects.
Inventory
All actions are inventoried.

Automate compliance workflows

Once the policies and rules are established, the processes can be automated, e.g. through

Project templates
Project templates with specific audit protocols and audit trails, such as HIPAA, can be created.
Project label
Depending on the policy, different labels can be predefined for projects and tasks.

Audit management

Compliance audits require the traceability of various events such as user actions, changes to authorisations or approvals.


[1]ISO 19600:2014 Compliance management systems — Guidelines

Software reviews – does high-quality software justify its cost?

Reviews take place throughout our entire software development process, starting with the analysis of requirements and the appropriate software architecture and ending with code and testing analyses. However, we also conduct audits of our customers software so that they receive an assessment for the further development and operation of their software.

What are the benefits of software reviews?

Software reviews lead to significantly fewer errors. According to a study of 12 thousand projects by Capers Jones [1], these are reduced by

  • in requirements reviews by 20–50%
  • in top-level design reviews by 30–60%
  • in detailed functional design reviews by 30–65%.
  • for detailed logical design reviews by 35–75%.

The study concludes:

Poor code quality is cheaper until the end of the code phase; after that, high quality is cheaper.
Costs across the development phases of requirement, design, coding, testing and operation

The study still assumes a classic waterfall model from requirements to software architecture, first to code, then to tests and operation. In agile software development, however, this process is repeated in many cycles, so that we spend most of our time developing software by changing or adding to the existing code base. Therefore, we need to understand not only the change requirements but also the existing code. Better code quality makes it easier for us to understand what our application does and how the changes make sense. If code is well divided into separate modules, I can get an overview much faster than with a large monolithic code base. Further, if the code is clearly named, I can more quickly understand what different parts of the code do without having to go into detail. The faster I understand the code and can implement the desired change, the less time I need to implement it. To make matters worse, the likelihood that I will make a mistake increases. More time is lost in finding and fixing such errors. This additional time is usually accounted for as technical debt.

Conversely, I may be able to find a quick way to provide a desired function, but one that goes beyond the existing module boundaries. Such a quick and dirty implementation, however, makes further development difficult in the weeks and months to come. Even in agile software development without adequate code quality, progress can only be fast at the beginning, the longer there is no review, the tougher the further development will be. In many discussions with experienced colleagues, the assessment was that regular reviews and refactorings lead to higher productivity after just a few weeks.

Which types of review solve which problems?

There are different ways to conduct a review. These depend on the time and the goals of the review.

The IEEE 1028 standard [2] defines the following five types of review:

Walkthroughs
With this static analysis technique we develop scenarios and do test runs, e.g. to find anomalies in the requirements and alternative implementation options. They help us to better understand the problem, but do not necessarily lead to a decision.
Technical reviews
We carry out these technical reviews, e.g. to evaluate alternative software architectures in discussions, to find errors or to solve technical problems and come to a (consensus) decision.
Inspections
We use this formal review technique, for example, to quickly find contradictions in the requirements, incorrect module assignments, similar functions, etc. and to be able to eliminate these as early as possible. We often carry out such inspections during pair programming, which also provides less experienced developers with quick and practical training.
Audits
Often, before a customer’s software is put into operation, we conduct an evaluation of their software product with regard to criteria such as walk-through reports, software architecture, code and security analysis as well as test procedures.
Management reviews
We use this systematic evaluation of development or procurement processes to obtain an overview of the project’s progress and to compare it with any schedules.

Customer testimonials

Thank you very much for the good work. We are very happy with the result!

– Niklas Kohlgraf, Projektmanagement, pooliestudios GmbH

Get advice on software reviews from Cusy today:

Veit Schiele

Veit Schiele
Phone: +49 30 22430082

We will also be happy to call you!

Request now


[1]Capers Jones: Software Quality in 2002: A Survey of the State of the Art
[2]IEEE Standard for Software Reviews and Audits 1028–2008

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

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

Choosing the right NoSQL database

Relational databases dominated the software industry for a long time and are very mature with mechanisms such as redundancy, transaction control and standard interfaces. However, they were initially only able to react moderately to higher demands on scalability and performance. Thus, from the beginning of 2010, the term NoSQL was increasingly used to describe new types of databases that better met these requirements.

NoSQL databases should solve the following problems:

  • Bridging the internal data structure of the application and the relational data structure of the database.
  • Moving away from the integration of a wide variety of data structures into a uniform data model.
  • The growing amount of data increasingly required clusters for data storage

Aggregated data models

Relational database modelling is very different from the types of data structures that application developers use. The use of data structures modelled by developers to solve different problem domains has led to a move away from relational modelling towards aggregate models. Most of this is inspired by Domain Driven Design. An aggregate is a collection of data that we interact with as a unit. These aggregates form the boundaries for ACID operations, where Key Values, Documents and Column Family can be seen as forms of an aggregator-oriented database.

Aggregates make it easier for the database to manage data storage on a cluster, as the data unit can now be on any computer. Aggregator-oriented databases work best when most data interactions are performed with the same aggregate, e.g. when a profile needs to be retrieved with all its details. It is better to store the profile as an aggregation object and use these aggregates to retrieve profile details.

Distribution models

Aggregator-oriented databases facilitate the distribution of data because the distribution mechanism only has to move the aggregate and doesn’t have to worry about related data, since all related data is contained in the aggregate itself. There are two main types of data distribution:

Sharding
Sharding distributes different data across multiple servers so that each server acts as a single source for a subset of data.
Replication

Replication copies data across multiple servers so that the same data can be found in multiple locations. Replication takes two forms:

Master-slave replication makes one node the authoritative copy, processing writes, while slaves are synchronised with the master and may process reads.

Peer-to-peer replication allows writes to any node. Nodes coordinate to synchronise their copies of the data.

Master-slave replication reduces the likelihood of update conflicts, but peer-to-peer replication avoids writing all operations to a single server, thus avoiding a single point of failure. A system can use one or both techniques.

CAP Theorem

In distributed systems, the following three aspects are important:

  • Consistency
  • Availability
  • Partition tolerance

Eric Brewer has established the CAP theorem, which states that in any distributed system we can only choose two of the three options. Many NoSQL databases try to provide options where a setup can be chosen to set up the database according to your requirements. For example, if you consider Riak as a distributed key-value database, there are essentially the three variables

r
Number of nodes to respond to a read request before it is considered successful
w
number of nodes to respond to a write request before it is considered successful
n
Number of nodes on which the data is replicated, also called replication factor

In a Riak cluster with 5 nodes, we can adjust the values for r, w and n so that the system is very consistent by setting r = 5 and w = 5. However, by doing this we have made the cluster vulnerable to network partitions, as no write is possible if only one node is not responding. We can make the same cluster highly available for writes or reads by setting r = 1 and w = 1. However, now consistency may be affected as some nodes may not have the latest copy of the data. The CAP theorem states that when you get a network partition, you have to balance the availability of data against the consistency of data. Durability can also be weighed against latency, especially if you want to survive failures with replicated data.

Often with relational databases you needed little understanding of these requirements; now they become important again. So you may have been used to using transactions in relational databases. In NoSQL databases, however, these are no longer available to you and you have to think about how they should be implemented. Does the writing have to be transaction-safe? Or is it acceptable for data to be lost from time to time? Finally, sometimes an external transaction manager like ZooKeeper can be helpful.

Different types of NoSQL databases

NoSQL databases can be roughly divided into four types:

Key-value databases

Key-value databases are the simplest NoSQL data stores from an API perspective. The client can either retrieve the value for the key, enter a value for a key or delete a key from the data store. The value is a blob that the datastore just stores without caring or knowing what is inside. It is solely the responsibility of the application to understand what has been stored. Because key-value databases always use primary key access, they generally have high performance and can be easily scaled.

Some of the most popular key-value databases are

Riak KV
Home | GitHub | Docs
Redis
Home | GitHub | Docs
Memcached
Home | GitHub | Docs
Berkeley DB
Home | GitHub | Docs
Upscaledb
Home | GitHub | C API Docs

You need to choose them carefully as there are big differences between them. For example, while Riak stores data persistently, Memcached usually does not.

Document databases

These databases store and retrieve documents, which may be XML, JSON, BSON, etc. These documents are hierarchical tree data structures that can consist of maps, collections and scalar values. Document databases provide rich query languages and constructs such as databases, indexes, etc. that allow for an easier transition from relational databases.

Some of the most popular document databases are

MongoDB
Home | GitHub | Docs
CouchDB
Home | GitHub | Docs
RavenDB
Home | GitHub | Docs
Elasticsearch
Home | GitHub | Docs
eXist
Home | GitHub | Docs

Column Family Stores

These databases store data in column families as rows assigned to a row key. They are excellent for groups of related data that are frequently accessed together. For example, this could be all of a person’s profile information, but not their activities.

While each Column Family can be compared to the row in an RDBMS table where the key identifies the row and the row consists of multiple columns, in Column Family Stores the different rows do not have to have the same columns.

Some of the most popular Column Family Stores are

Cassandra
Home | GitHub | Docs
HBase
Home | GitHub | Docs
Hypertable
Home | GitHub | Docs

Cassandra can be described as fast and easily scalable because writes are distributed across the cluster. The cluster does not have a master node, so reads and writes can be performed by any node in the cluster.

Graph database

In graph databases you can store entities with certain properties and relationships between these entities. Entities are also called nodes. Think of a node as an instance of an object in an application; relationships can then be called edges, which can also have properties and are directed.

Graph models
Labeled Property Graph
In a labelled property graph, both nodes and edges can have properties.
Resource Description Framework (RDF)
In RDF, graphs are represented using triples. A triple consists of three elements in the form node-edge-node subject --predicate-> object, which are defined as resources in the form of a globally unique URI or as an anonymous resource. In order to be able to manage different graphs within a database, these are stored as quads, whereby a quad extends each triple by a reference to the associated graph. Building on RDF, a vocabulary has been developed with RDF Schema to formalise weak ontologies and furthermore to describe fully decidable ontologies with the Web Ontology Language.
Algorithms

Important algorithms for querying nodes and edges are:

Breadth-first search, depth-first search
Breadth-first search (BFS) is a method for traversing the nodes of a graph. In contrast to depth-first search (DFS), all nodes that can be reached directly from the initial node are traversed first. Only then are subsequent nodes traversed.
Shortest path
Path between two different nodes of a graph, which has minimum length with respect to an edge weight function.
Eigenvector
In linear algebra, a vector different from the zero vector, whose direction is not changed by the mapping. An eigenvector is therefore only scaled and the scaling factor is called the eigenvalue of the mapping.
Query languages
Blueprints
a Java API for property graphs that can be used together with various graph databases.
Cypher
a query language developed by Neo4j.
GraphQL
an SQL-like query language
Gremlin
an open source graph programming language that can be used with various graph databases (Neo4j, OrientDB).
SPARQL
query language specified by the W3C for RDF data models.
Distinction from relational databases

When we want to store graphs in relational databases, this is usually only done for specific conditions, e.g. for relationships between people. Adding more types of relationships then usually involves many schema changes.

In graph databases, traversing the links or relationships is very fast because the relationship between nodes doesn’t have to be calculated at query time.

Some of the most popular graph databases are

Neo4j
Home | GitHub | Docs
InfiniteGraph
Home

Selecting the NoSQL database

What all NoSQL databases have in common is that they don’t enforce a particular schema. Unlike strong-schema relational databases, schema changes do not need to be stored along with the source code that accesses those changes. Schema-less databases can tolerate changes in the implied schema, so they do not require downtime to migrate; they are therefore especially popular for systems that need to be available 24/7.

But how do we choose the right NoSQL database from so many? In the following we can only give you some general criteria:

Key-value databases
are generally useful for storing sessions, user profiles and settings. However, if relationships between the stored data are to be queried or multiple keys are to be edited simultaneously, we would avoid key-value databases.
Document databases
are generally useful for content management systems and e-commerce applications. However, we would avoid using document databases if complex transactions are required or multiple operations or queries are to be made for different aggregate structures.
Column Family Stores
are generally useful for content management systems, and high volume writes such as log aggregation. We would avoid using Column Family Stores databases that are in early development and whose query patterns may still change.
Graph databases
are well suited for problem areas where we need to connect data such as social networks, geospatial data, routing information as well as recommender system.

Conclusion

The rise of NoSQL databases did not lead to the demise of relational databases. They can coexist well. Often, different data storage technologies are used to store the data to match your structure and required query.

Microsoft alternatives – migration to open source technologies

We are developing a migration strategy from Microsoft 365 to open source technologies for a large German research company. On the one hand, it’s a question of regaining cyber souvereignty and, on the other hand, of meeting increased security requirements. This is to be achieved by using free and open source software (FLOSS). Overall, the project is similar to the Microsoft Alternatives Project (MAlt) at CERN and the Project Phoenix of Dataport.

The principles of the project are:

  • The same service should be offered to all employees
  • Vendor lock-ins should be avoided in order to reduce the risk of dependency
  • Most of the data should be owned by the research company

Plans for 2020

For 2020 it is planned to evaluate alternative solutions for many services and to publish prototypes and pilot projects.

Product group Service Product to evaluate Status
Identity and access management LDAP OpenLDAP 🏭 Production
Personal Information Management eMail Zimbra 🚦 Evaluation
Calendar Zimbra 🚦 Evaluation
Contacts Zimbra 🚦 Evaluation
Collaboration File sharing NextCloud 🏭 Production
Office integration NextCloud 🚦 Evaluation
Direct mails/chat Mattermost 🏭 Production
Video conferences Jitsi Meet 🏭 Production
Search Search engine Elasticsearch 🚦 Evaluation
Frontend/ Visualisation Kibana 🚦 Evaluation
Authentication/ Access control Open Distro Security 🚦 Evaluation
k-nearest neighbors Open Distro KNN 🚦 Evaluation
Project management Issues/Milestones GitLab 🚦 Evaluation
Time tracking gitlabtime 🚦 Evaluation
Documentation GitLab Wiki 🚦 Evaluation
Research software [1], [2] Package manager Spack 🏭 Production
IDE JupyterHub 🏭 Production
Development environments Jupyter Kernels 🏭 Production
Software versioning Git 🏭 Production
Data versioning DVC 🏭 Production
Gathering and storing data Intake 🛫 Pilot
Spreadsheet ipysheet 🏭 Production
Geospatial data PostGIS 🏭 Production
Map creation OpenStreetMap 🏭 Production
DevOps GitLab CI/CD Pipeline 🛫 Pilot
Documentation Sphinx 🏭 Production

Hosting strategy

There are essentially three different hosting variants within the research company:

Society-wide infrastructure
Infrastructure, which is used by most of the research projects and administrations across the institutes, is to be provided by the research company’s central IT.
Institute-wide infrastructure
Infrastructure that is required for the special research areas of one institute or that needs IT support on site should be provided by the IT of the respective institute.
Operational and geo-redundancy
These are mainly produced through institute cooperation or through cooperation between individual institutes and the IT of the research society. In terms of technology, floating IPs and the Virtual Router Redundancy Protocol (VRRP) are used for this, with decisions being made on the basis of BGP announcements.

[1]

There is extensive German documentation for the infrastructure on which the research software is developed, which is published under the BSD 3 Clause license:

[2]The planned uniform API represents a significant simplification here; see also Announcing the Consortium for Python Data API Standards.

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.

Technical debts

We usually develop the functionality requested by our customers in short cycles. Most of the time, we clean up our code even after several cycles when the requirements hardly change. Sometimes, however, code that has been developed quickly must also be put into production. Technical debt is a wonderful metaphor in this regard, introduced by Ward Cunningham to think about such problems. Similar to financial debt, technical debt can be used to bridge difficulties. And similar to financial debt, technical debt requires interest to be paid, namely in the form of extra effort to develop the software further.

Unlike financial debt, however, technical debt is very difficult to quantify. While we know that it hinders a team’s productivity in developing the software further, we lack the calculation basis on which to quantify this debt.

To describe technical debt, a distinction is usually made between deliberate and inadvertent technical debt, and between prudent or reckless debt. This results in the following quadrant:

  inadvertent deliberate
prudent «Now we know how we should do it!» sollten!»* «We have to deliver now and deal with the consequences »
reckless «WWhat is software design?» «We don’t have time Rfor software design!»

Read more

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.

Beuth University: Prototype for a medication app

Beuth University: Prototype for a medication app

Beuth University of Applied Sciences

For the Beuth University, we develop a prototype for a medication app.

The app is intended to improve the safety of the medication and in particular in the monitoring of ingestion rhythm and the knowledge of side effects and influences.

Not only the patients themselves should be able to use this app, but also relatives and caregivers.

In fact, there are already many apps that promise to meet the requirements. However, with more detailed research, they have significant shortcomings.

Professional quality

The professional quality of other apps is rarely discernible and, if the few reviews are taken as a basis, is usually very low. This is all the more problematic when apps promise to point out interactions and double prescriptions for medications with similar effects. For customers who rely on the fact that their app will warn them of dangers, for example with their self-medication requests, are likely to be at serious risk.

User groups

The apps also very rarely provide information about their user groups, neither about

  • Suitability for specific diseases/conditions
  • Suitability for gender, special age groups (or areas) etc.
  • Suitability for certain health professions and settings: clinical, outpatient, at home, …
  • Suitability for physiological and physical impairments, also not the support for TalkBack for Android and VoiceOver for iPhone.
  • Support for country-specific drugs and pack sizes

Privacy

The handling of user data is usually poor. The data protection declarations usually leave customers unclear as to what happens to their information. This is all the more problematic since over 80% of the apps transfer data to infrastructure providers such as Google, Facebook etc. Not even the encrypted transmission of user data was always guaranteed, especially not when data was transmitted by email. The few independent test procedures are unlikely to contribute to clarification, since they mostly rely on self-assessment.

elena international: Web-based planning tool for microgrids

For elena international we realise a web-based planning tool for microgrids where we use Jupyter notebooks and Voilà vuetify to develop presentation logic and user interactions fast, simple and robust.

elena international is a startup company that provides customised solutions for various stages of power system planning.

First we realise a walking skeleton of a web-based planning tool for microgrids.

The chosen software architecture allows elena the further development of its Julia libraries. In the Web tool the adoption of new features takes place in IPython notebooks with PyJulia. These notebooks can also be used to define the voila-vuetify widgets for interacting with users. Finally, with Voilà, these notebooks are converted into an interactive dashboard:

Voilà Dashboard

To harden the notebooks for production, on the one hand we write tests for each method, which run regularly with GitLab CI/CD, and on the other hand we activate logging for fault diagnosis and monitoring.

So we are not only answering the question Are Jupyter notebooks ready for production?, we also extend the possibilities of notebooks: they serve an editor system, in which not only texts can be written and media added, but also the presentation logic of scientific calculations and interactive widgets can be defined.

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.

The Cusy vision: DevOps as an API

DevOps: Halfway there

According to studies recently reported by Heise Developer, half of German developers already work according to the DevOps principle. As an advocate of DevOps, one could therefore sit back contentedly and conclude: half the way is already done.

But this is only half the truth. If you look at how DevOps is implemented in many companies, you see that a large proportion of pro-DevOps companies are only halfway there in terms of implementation. While developers in DevOps teams are now taking responsibility for the code in operations as well, conversely not all sysadmins are taking responsibility for the infrastructure in development as well. Often, developer teams still operate their own infrastructure for development, so that the positive effects of the DevOps principle partially evaporate.

The fact that DevOps is a change in culture and not just the use of a few innovative tools is still not widely known. Even many studies that only ask about the use of certain tools like Docker or Puppet paint a false picture of DevOps.

DevOps is not a toolbox, but a redistribution of responsibility in the company. In classic IT departments, responsibility for development/testing and production is distributed as follows.

It is not uncommon for development and operations to even be located in different departments of a company. In the DevOps culture, the vertical separation between development and operations is overturned and a horizontal interface emerges.

The interface between Dev and Ops is tipping

Application developers take responsibility for their application, regardless of whether it is in development or production. Conversely, however, the system administrators also take responsibility for the entire infrastructure from the development environment to the production environment.

So the paradigm shift occurring with DevOps is essentially one that changes the distribution of responsibility, not one that merely provides new tools like Puppet and Chef.

Cusy is the Ops in DevOps

DevOps is the philosophy of unifying Development and Operations at the culture, practice, and tool levels, to achieve accelerated and more frequent deployment of changes to Production.

– Rob England www.itskeptic.org

Cusy facilitates the transition from the classic IT organisation to agile DevOps structures. As a service provider, Cusy takes responsibility for the entire infrastructure: from development to the test environment to the productive system. Cusy is the Ops in DevOps and relieves companies of the task of maintaining their infrastructure themselves. Cusy provides companies not only with the technical infrastructure, but also with the complete development and operating environment, including all the necessary tools.

Thanks to Cusy, application developers can already concentrate fully on their development. They don’t have to worry about the maintenance and care of the necessary development tools such as project management, document management and version management.

Furthermore, the Cusy platform also provides tools for testing and production such as Continuous Integration and log management & analysis.

Finally, Cusy also provides tools for marketing and support such as web analytics and helpdesk.

In addition to all the necessary tools, Cusy also provides VMs for running applications that allow easy Continuous Delivery with GitLab. For this, Cusy provides an API for creating, modifying and deleting VMs. With this, the vision of Cusy has become reality: DevOps as an API.