Systems | Development | Analytics | API | Testing

Video footage is widely available in the current era of digital communication. Moving images have developed into a crucial method for message delivery, from brief social media posts to extensive online courses. The development of technologies that make the production of such material not only feasible but also effective and artistically impressive has exploded during the past 10 years.

Automation, reliability and scale are the seeds for growing innovation.

In the dynamic realm of technology, how we conceive, construct, and uphold applications exerts a significant influence on their scalability, ease of maintenance, and ultimate prosperity. Throughout this blog, we will embark on an expedition through the evolutionary trajectory of software architecture.

Semantic layers are a game changer, allowing organizations to define metrics and business logic in one, centralized location. Because business users can trust that their data is built on a single source of truth, the semantic layer also empowers self-service analytics. Looker Modeler has become a leader among semantic layers, allowing users to seamlessly layer on top of their business data.

This year, we’ve witnessed a significant shift in the tech landscape. Industry giants like Amazon (AWS), Facebook, Twitter, GitHub, and others are embracing GraphQL APIs, both for their internal operations and external interactions. GraphQL, hailed as the SQL for APIs, is more than a query language – It's a runtime that efficiently retrieves data from databases.

Data management is a critical aspect of any business, and secure, efficient data transfer mechanisms are an absolute must. This is where Secure File Transfer Protocol (SFTP) comes into play, offering a method to transfer files securely over networks. However, with the rise of cloud computing, a more accessible, scalable, and cost-effective solution has emerged: Cloud SFTP.

Domain-driven designs are popular in organizations that have complex domain models and wish to organize engineering around them. REST-based architectures are a common choice for implementing the API entry point into these domains. REST-based solutions are straightforward for the API builder and for API consumers concerned with data from a single API. But what about developers tasked with aggregating information across domains?

Monolithic architectures have long been the go-to when it comes to software design and building applications. However, monolithic architectures have their fair share of drawbacks and make it harder to make changes to an application efficiently. Therefore, monolithic architectures have given way to a new type of architecture that allows developer teams to make faster and more efficient changes, known as microservices architecture.

In the first two parts of our Inside Flink blog series, we explored the benefits of stream processing with Flink and common Flink use cases for which teams are choosing to leverage the popular framework to unlock the full potential of streaming. Specifically, we broke down the key reasons why developers are choosing Apache Flink® as their stream processing framework, as well as the ways in which they are putting it into practice.

In part one of this series, we discussed the evolution of Yellowfin’s Guided NLQ solution and focused on aspects of Guided NLQ that stand apart from the competition. In part two, we then compared Guided NLQ to Sisense's equivalent NLQ solution, Sisense Simply Ask. In part three, we will look deeper at another competitor’s NLQ offering, Microsoft Power BI and its Q&A feature.