InfoQ Homepage Data Content on InfoQ
-
DynamoDB Data Transformation Safety: from Manual Toil to Automated and Open Source
Data transformation remains a continuous challenge in engineering and built upon manual toil. The open source utility Dynamo Data Transform was built to simplify and build safety and guardrails into data transformation for DynamoDB based systems––built upon a robust manual framework that was then automated and open sourced. This article discusses the challenges with Data Transformation.
-
Data Manipulation with Functional Programming and Queries in Ballerina
Ballerina has been designed as a data-oriented programming language and supports a functional programming coding style. The Ballerina query language is similar to SQL in the sense that a query expression is made up of clauses. The Ballerina “Table” data structure can be more effective than maps in representing indexed data collections.
-
Ballerina: a Data-Oriented Programming Language
Ballerina’s flexible type system brings the best of statically typed and dynamically typed languages in terms of safety, clarity, and speed of development. Ballerina treats data as a first-class citizen that can be created without extra ceremony, just like strings and numbers.
-
Software Architecture and Design InfoQ Trends Report—April 2022
An overview of how the InfoQ editorial team sees the Software Architecture and Design topic evolving in 2022, with a focus on what architects are designing for today.
-
Data Patterns for the Edge: Data Localization, Privacy Laws, and Performance
With growing competition to get data that power experiences to the end-user closer and closer and the advent of local data privacy laws, let's look at different enterprise data patterns like “synchronous data retrieval”, “subsequent data retrieval” and “prefetch data retrieval” on data center.
-
Developing Deep Learning Systems Using Institutional Incremental Learning
Institutional incremental learning promises to achieve collaborative learning. This form of learning can address data sharing and security issues, without bringing in the complexities of federated learning. This article talks about practical approaches which help in building an object detection system.
-
AI, ML and Data Engineering InfoQ Trends Report - August 2021
How AI, ML and Data Engineering are evolving in 2021 as seen by the InfoQ editorial team. Topics discussed include deep learning, edge deployment of machine learning algorithms, commercial robot platforms, GPU and CUDA programming, natural language processing and GPT-3, MLOps, and AutoML.
-
GitHub’s Journey from Monolith to Microservices
This article explores GitHub's recent journey towards a microservices architecture. It takes a deeper look at GitHub’s historical and current state, goes over some internal and external factors, and discusses practical consideration points in how Github tackled their migration, including key concepts and best practices of implementing microservices architecture.
-
Data-Driven Decision Making – Optimizing the Product Delivery Organization
The Data-Driven Decision Making Series provides an overview of how the three main activities in the software delivery - Product Management, Development and Operations - can be supported by data-driven decision making. Applying Hypotheses, CD Indicators and SRE’s SLIs / SLOs enables a software delivery organization to optimize for effectiveness, efficiency and service reliability.
-
Data-Driven Decision Making – Product Operations with Site Reliability Engineering
The Data-Driven Decision Making Series provides an overview of how the three main activities in the software delivery - Product Management, Development and Operations - can be supported by data-driven decision making. In Operations, SRE’s SLIs and SLOs can be used to steer the reliability of services in production.
-
Article Series: Data-Driven Decision Making
The Data-Driven Decision Making Series provides an overview of how the three main activities in the software delivery - Product Management, Development and Operations - can be supported by data-driven decision making.
-
Q&A on the Book Agile Machine Learning
The book Agile Machine Learning by Eric Carter and Matthew Hurst describes how the guiding principles of the Agile Manifesto have been used by machine learning teams in data projects. It explores how to apply agile practices for dealing with the unknowns of data and inferencing systems, using metrics as the customer.