Lars Albertsson

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big data #bigdata #dataops #dataengineering data engineering stream processing spark kafka hadoop data processing dataops batch processing analytics luigi privacy #ai software testing workflow orchestration bigdata machine learning #scala data quality #datastrategy software development #testing devsecops devops agile data science data strategy time #corpops event sourcing scala cassandra testing project management #generative ai #llm #risk management organising #functionalprogramming #softwarearchitecture #bigd recommendation systems count-min sketch #infosec #security spotify #functionaldataengineering crunch #shiftleft security supply chain lean #ethics risk management shiftleft covid-19 safety #lean data democratization data en aviation python openshift aws kubernetes ai scalameta gdpr organization #datawarehouse

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Lars Albertsson

Presentaciones

Data Infrastructure for a World of Music

Scalable real-time processing techniques

Functional architectural patterns

Organising for Data Success

Data pipelines from zero

Data pipelines from zero to solid

Building real time data-driven products

Test strategies for data processing pipelines

A primer on building real time data-driven products

Testing data streaming applications

Protecting privacy in practice

10 ways to stumble with big data

Test strategies for data processing pipelines, v2.0

Privacy by design

Big data == lean data

Don't build a data science team

Kubernetes as data platform

Data ops in practice

Eventually, time will kill your data pipeline

Taming the reproducibility crisis

Eventually, time will kill your data processing

Engineering data quality

Data democratised

The lean principles of data ops

Data ops in practice - Swedish style

Mortal analytics - Covid-19 and the problem of data quality

The right side of speed - learning to shift left

Ai legal and ethics

DataOps - Lean principles and lean practices

Secure software supply chain on a shoestring budget

Recomendaciones

Culture over-process

Recommender Systems (Machine Learning Summer School 2014 @ CMU)

The Mechanics of Testing Large Data Pipelines (QCon London 2016)

Comparison of various streaming technologies

Interactive Recommender Systems with Netflix and Spotify

10 more lessons learned from building Machine Learning systems