Systems | Development | Analytics | API | Testing

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Top 3 Data + AI Predictions for Manufacturing in 2024

Investment in AI for manufacturing is expected to grow by 57% by 2026. That’s hardly surprising — with AI’s ability to augment worker productivity, improve efficiency and drive innovation, its potential in manufacturing is vast. AI’s predictive capabilities can help manufacturing leaders anticipate market trends and make data-driven decisions, creating financial opportunities for suppliers as well as customers.

Accelerating Queries on Iceberg Tables with Materialized Views

This blog post describes support for materialized views for the Iceberg table format in Cloudera Data Warehouse. Apache Iceberg is a high-performance open table format for petabyte-scale analytic datasets. It has been designed and developed as an open community standard to ensure compatibility across languages and implementations.

REST APIs for Government

REST APIs are emerging as a simple and secure way to access sensitive government data, serving as conduits that enable seamless access and manipulation of data across various government systems. Government agencies face the pressing need to modernize their IT infrastructure to improve service delivery, enhance operational efficiency, and ensure the security of data exchanges.

Optimizing Quality Assurance: Harnessing the Power of AI for Efficient and Effective Software Testing

In the present digital period, Artificial Intelligence (AI) is impacting the future of various aspects of Quality Assurance (QA). This evolution has resulted in strategies for ensuring quality is effectively integrated into development processes.

What is the Transactional Outbox Pattern? | Designing Event-Driven Microservices

The transactional outbox pattern leverages database transactions to update a microservice's state and an outbox table. Events in the outbox will be sent to an external messaging platform such as Apache Kafka. This technique is used to overcome the dual-write problem which occurs when you have to write data to two separate systems such as a database and Apache Kafka. The database transactions can be used to ensure atomic writes between the two tables. From there, a separate process can consume the outbox and update the external system as required.

How to Automate an Enterprise Process From Beginning to End

Automating a process can be complex, especially when it’s a long-running process with many steps and complex business rules, cross-cutting multiple departments and systems in an enterprise. To automate a process like this from end to end, you’ll need a process automation strategy and software. Assuming you’ve ticked both of those boxes, the next step is putting those things to work, which is the focus of this post.

LLMOps vs. MLOps: Understanding the Differences

Data engineers, data scientists and other data professional leaders have been racing to implement gen AI into their engineering efforts. But a successful deployment of LLMs has to go beyond prototyping, which is where LLMOps comes into play. LLMOps is MLOps for LLMs. It’s about ensuring rapid, streamlined, automated and ethical deployment of LLMs to production. This blog post delves into the concepts of LLMOps and MLOps, explaining how and when to use each one.

Deploy your app to App Store Connect with Codemagic CLI tools and GitHub Actions

The process of building, code signing, and publishing mobile apps can be tedious and time-consuming, especially when working in a large team and also needing to share builds with QA engineers. That’s why Codemagic offers a cloud-based CI/CD service for mobile apps that automates the whole workflow with minimal time-effort on configuration. But what if you want to use Codemagic’s features locally or in another CI/CD environment, such as GitHub Actions?