Systems | Development | Analytics | API | Testing

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How to source data from AWS DynamoDB to Confluent using the Open-Source Connector

This is a one-minute video showing an animated architectural diagram of an integration between Amazon DynamoDB and Confluent Cloud using an open-source Kafka connector. The integration allows you to avoid maintaining custom code, and gives you the ability to automatically discover and adapt to changes in DynamoDB tables. All details are provided.

The strategic advantage of iPaaS for ERP providers

ERP providers constantly seek ways to enhance their offerings, streamline operations, and provide superior value to their clients in the ever-evolving landscape of enterprise software. One of the most transformative strategies in this domain is the integration of iPaaS (Integration Platform as a Service). This blog post explores how partnering with an iPaaS can significantly boost an ERP provider’s capabilities, offering strategic advantages and substantial ROI.

The Ultimate Guide to Sanity Testing

Software applications constantly evolve, with frequent updates, bug fixes, and new feature additions. However, these changes can introduce new issues or disrupt existing functionalities. Testlio September 26th, 2024 Explore the Differences Between Smoke Testing and Sanity Testing According to the Systems Sciences Institute at IBM, in addition to potential damage to your brand’s reputation, the cost of fixing a bug post-release can be up to 100 times more than during the design phase.

Data AI Summit | Expanding Log Analytics and Threat Hunting Natively in Databricks

ChaosSearch + Databricks Deliver on the best of Databricks (open Spark-based data lakehouse) and ELK (efficient search, flexible live ingestion, API/UI) via ChaosSearch on Databricks. Log analytics for observability / security with unlimited retention at a fraction of the cost now with Databricks’ AI/ML. Watch as ChaosSearch CEO, Ed Walsh, shares the power of ChaosSearch in your Databricks environment.

Streamlining Generative AI Deployment with New Accelerators

The journey from a great idea for a Generative AI use case to deploying it in a production environment often resembles navigating a maze. Every turn presents new challenges—whether it’s technical hurdles, security concerns, or shifting priorities—that can stall progress or even force you to start over.

Enhancing Convenience and Customer Experience: The Case for Investing in Self-Service Kiosks

Self-service kiosks have rapidly become a staple in the retail landscape, symbolizing the growing trend toward automation in customer interactions. In 2023, the global self-checkout market was valued at approximately $4.5 billion, with estimates suggesting it could reach between $5.2 and $6.0 billion by the end of 2024. This surge is driven by increasing consumer demand for convenience, technological advancements, and the pursuit of more seamless shopping experiences.

How To Reduce Technical Debt - A Comprehensive Guide

According to Stack Overflow’s recent survey, 62% of developers share a common concern – a growing and never-ending technical debt. Arpita Goala , Content Marketing Manager September 26th, 2024 Technical debt or tech debt is often an unintended and unavoidable consequence of software development. With the speed at which technologies and products evolve, technical debt is something that every quality assurance (QA) team must deal with.

Want to Unlock Gen AI's Promise in Retail? Start with the Data Foundation.

There’s no question which technology everyone’s talking about in retail. Generative AI continues to promote incredible levels of interest with its promise of next-level productivity and new kinds of employee and customer experience. It’s all happening at light speed. When ChatGPT burst onto the scene, it gained hundreds of millions of users in a matter of months.

5 Ways to Approach Data Analytics Optimization for Your Data Lake

While data lakes make it easy to store and analyze a wide variety of data types, they can become data swamps without the proper documentation and governance. Until you solve the biggest data lake challenges — tackling exponential big data growth, costs, and management complexity — efficient and reliable data analytics will remain out of reach.