InfoQ Homepage Database Content on InfoQ
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/articles/how-to-choose-stream-processor/en/smallimage/how-to-choose-stream-processor-logo-1534706730559.jpeg)
How to Choose a Stream Processor for Your App
Choosing a stream processor for your app can be challenging with many options to choose from. The best choice depends on individual use cases. In this article, the authors discuss a stream processor reference architecture, key features required by most streaming applications and optional features that can be selected based on specific use cases.
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Analyzing and Preventing Unconscious Bias in Machine Learning
This article is based on Rachel Thomas’s keynote presentation, “Analyzing & Preventing Unconscious Bias in Machine Learning” at QCon.ai 2018. Thomas talks about the pitfalls and risk the bias in machine learning brings to the decision-making process. She discusses three use cases of machine learning bias.
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Q&A on the Book Testing in the Digital Age
The Book Testing in the Digital Age by Tom van de Ven, Rik Marselis, and Humayun Shaukat, explains the impact that developments like robotics, artificial intelligence, internet of things, and big data are having in testing. It explores the challenges and possibilities that the digital age brings us when it comes to testing software systems.
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Democratizing Stream Processing with Apache Kafka and KSQL - Part 1
In this article, author Michael Noll discusses the stream processing with KSQL, the streaming SQL engine for Apache Kafka. Topics covered include challenges of stateful stream processing and how KSQL addresses them, and how KSQL helps to bridge the world of streams and databases through streams and tables.
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Picking an Active-Active Geo Distribution Strategy: Comparing Merge Replication and CRDT
Modern distributed applications are fuelling the growing demand for distributed active-active, multi-master databases. While most popular databases support multi-master deployment, different databases employ different techniques. LWW, MVCC, merge replication and CRDTs deliver eventual consistency, offering read and write access with local latency and remaining available during network partitions.
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Columnar Databases and Vectorization
In this article, author Siddharth Teotia discusses the Dremio database which is based on Apache Arrow with vectorization capabilities.
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Q&A on the Book Software Wasteland
Almost all Enterprise Information Systems now cost vastly more to implement than they should. When you have hundreds or thousands of complex applications, you are stuck in the Application Centric Quagmire. In the book Software Wasteland Dave McComb explores what is causing application development waste and how visualizing the cost of change and becoming data-centric can help to reduce the waste.
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Monitoring SRE's Golden Signals
Golden signals are increasingly popular these days due to the rise of SRE. This article outlines what golden signals are, and how to monitor and use them in the context of various common services.
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Polyglot Persistence Powering Microservices
At Netflix, the cloud database engineering team is responsible for providing several flavors of data persistence as a service to microservice development teams. Roopa Tangirala explained how her team has created self-service tools that help developers easily implement the appropriate data store for each project's needs.
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GDPR for Operations
With GDPR, taking care of personal data is an organisation-wide responsibility, but in the operations we can provide a lot of supporting tools to help deal with the multiple facets of this problem.
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Why and How Database Changes Should Be Included in the Deployment Pipeline
Eduardo Piairo on why databases and applications should coexist in the same deployment pipeline and different scenarios and steps to achieve it.
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What Do Data Scientists and Data Engineers Need to Know about GDPR?
Andrew Burt on the implications of GDPR on data collection, storage and use for any organization dealing with customer data in the EU. Burt explains what's the minimum an org needs to pass the GDPR test, as well as how to take the opportunity to improve their overall data governance.