Igor Suhorukov
Big Data team lead
Biography
Igor is experienced JVM professional with deep knowledge of open source software and frameworks. Speaker at conferences and popular blogger. Contributor of H2, schemaspy, spring framework(top20) and spring-boot(top20) projects.)
Sessions
Continuous code quality in java projects
Technical debt management is a crucial metrics of large software system evolution. Development team can increase debt for taking shortcuts or workarounds in technical decisions to gain short-term benefit in time-to-market and earlier software release. And extra development efforts arises on late stages of project growth as a result of short-term speedup on first stages.
So let's examine how integrate continuous code quality as part of software system development process. We use SonarQube as part of our continuous integration pipeline and try to reduce code complexity and debt in each sprint. I use the same tooling to help popular open source projects manage and reduce tech debt.
In this report we considering static code analysis, project coding style, unit tests for system architecture validation, automatic documentation generation and automated behavior driven development tests as factors of engineer intention transparency, tech debt management and knowledge sharing.
Almost each agile development team should use automated continuous code quality tools as part of software development process to reduce complexity of artifacts changes in time. I hope that also such approach helps other teams to implements new features in predictable time.
Language:English
Audience Level:Beginner
Talk Format:Talk (~30-45 minutes)
Emulate Amazon Web Services infrastructure in single JMV process to reduce development cost and improve productivity
I emulated AWS infrastructure to speedup development and testing of complex distributed biomedical data processing application: SQS queues, S3 filesystem, Redshift data warehouse, PostgreSQL RDS service. It is not so hard as expected if you use open source libraries and frameworks. It is more performant and more easy approach to run emulation than use of Localstack.You can know "secret" information about Redshift JDBC driver under the hood. Also we will comparare several analitical and column-oriented database as base of warehouse for data science analysis/machine learning purpose.
Language:English
Audience Level:All
Talk Format:Talk (~30-45 minutes)
