Systems | Development | Analytics | API | Testing

Amazon Web Services, or AWS, offers a variety of cloud services ranging from AWS resources such as CDNs and data lakes to cloud computing and transformation services such as compute resources, virtual servers, and dynamic availability zones. For this reason, AWS cloud is one of the most broadly adopted cloud solutions, offering a global network of solutions at generally lower costs compared to on-premises solutions.

The digital landscape is rapidly transforming, with Gartner projecting cloud-native platforms will dominate over 95% of new workloads in 2025. While this shift unlocks agility and innovation, it also introduces significant complexities. Traditional testing methods struggle to keep pace with the dynamic nature of microservices, containers, and distributed systems inherent in cloud-native applications. This is precisely why specialized cloud native testing isn't just beneficial – it's essential.

Google Cloud Platform, or GCP, is a cloud resources collection offered by Google for enterprise and standard users. GCP offers a wide range of cloud services, including compute, storage, networking, security, analytics, and even machine learning models. Google Cloud products are the backbone of many cloud applications. Google Cloud allows flexibility with the scalable and predictable cost management.

Centralized data allows data teams to dedicate their effort to last-mile analytics and AI challenges.

Cloud computing has reshaped the digital landscape, offering unparalleled scalability, flexibility, and cost-effectiveness to businesses worldwide. Imagine entrusting your most valuable possessions to a high-tech, automated storage facility—accessible anytime, anywhere, but also potentially vulnerable if not adequately protected. This analogy perfectly encapsulates cloud computing: convenient, yet requiring robust security measures to guard against threats.

Confluent has been instrumental in enabling customers from various industries to develop real-time stream processing solutions using Apache Kafka. While many of these use cases demand low-latency and real-time processing, stream processing is also increasingly being utilized for ingesting logging and telemetry data. This type of data typically features a high ingest rate, but allows for a higher tolerance for end-to-end processing time.

The performance of your AI applications depends on your underlying infrastructure. Whether leveraging high-performance GPUs, accelerators, or CPUs, AI workloads require high-performance hardware. With a range of different GPUs and accelerators available, choosing the best one for your specific workload is critical. On top of selecting the best GPU for your workload's needs, efficiently running AI workloads in production and at scale is a challenge.

Discover how Paylocity, Vida Health, Nando’s, and Trinny London use Fivetran and Google Cloud to drive efficiency and cut costs across their global operations.

Orbit Codes is solving how blockchain data is indexed and analyzed within the COSMOS ecosystem. It extracts raw blockchain data, transforms it into a structured PostgreSQL database, and provides easy access through a GraphQL interface. For deeper insights, data is replicated to ClickHouse, enabling advanced analytics on transactions, fees, and trends. Designed for blockchains, not just users, Orbit Codes offers white-label solutions and custom branding to integrate seamlessly into existing ecosystems.