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Cloud

How to Load Data from AWS S3 to Snowflake

According to a study by Statista, the cloud storage market was valued at $90.17 billion in 2022 and will reach a value of $472.47 billion by 2030. These figures indicate a growing shift toward cloud computing and data storage solutions. A typical scenario in modern data management involves data transfer from cloud storage to cloud-based computing platforms. Amazon’s Simple Storage Service (S3) is among the go-to options for the former, and businesses trust Snowflake for the latter.

Set your Data in Motion with Confluent on Google Cloud

Confluent is pioneering a fundamentally new category of data infrastructure focused on data in motion. Confluent’s cloud-native offering is the foundational platform for data in motion – designed to be the intelligent connective tissue enabling real-time data, from multiple sources, to constantly stream across the organization. With Confluent, organizations can meet the new business imperative of delivering rich, digital front-end customer experiences and transitioning to sophisticated, real-time, software-driven backend operations.

Achieve faster time to value with data observability and FinOps for BigQuery

Right now, 88% of companies surveyed are failing to achieve optimal price/performance for their analytics workloads. Why? They don’t have the staff, their skilled engineers spend too much time doing toilsome work, and optimizing data workloads for performance and efficiency. With this in mind, Unravel is hosting a virtual event to help you leverage Unravel to achieve productivity, performance, and cost efficiency with BigQuery.

Confluent announces general availability of Confluent Cloud for Apache Flink®, simplifying stream processing to power next-gen apps

Confluent Cloud for Apache Flink®, a leading cloud-native, serverless Flink service is now available on AWS, Google Cloud, and Microsoft Azure. Confluent's fully managed, cloud-native service for Flink helps customers build high-quality data streams for data pipelines, real-time applications, and analytics.

From Theory to Practice: Real-World Applications of Cloud Platform Integration

Many companies talk about cloud integration in a theoretical way. But cloud technologies aren’t theoretical. They’re a rapidly growing segment of technology that’s changing the way businesses operate. In the following article, we move from theory to practice so you can have a more realistic vision of what to expect when you move more of your on-site tech to the cloud.

Top 7 AWS ETL Tools in 2024

Amazon Web Services (AWS) ETL refers to a cloud-based set of tools and services that help extract data from different sources, make it usable, and store it in a way that makes it easy to analyze and make decisions based on it. AWS ETL tools offer a unique advantage for businesses seeking to streamline their data processes. These tools are efficient, scalable, and adaptable, making them ideal for a wide range of industries, from healthcare and finance to retail and beyond.

The Snowflake Government & Education Data Cloud

In order to deliver on their missions, public agencies and departments must modernize IT to improve citizen services, streamline operational inefficiencies, drive research and innovation, and enable data collaboration across and beyond organizational lines. Unfortunately, the ability of public sector organizations to generate value from data is hindered by several challenges, including technical delays caused by legacy IT infrastructure, policy roadblocks, and institutional status quos. The public sector needs a protected, scalable, and flexible platform to centralize, govern, and securely share mission-critical data.

What is RAG? Retrieval-Augmented Generation for AI

Retrieval-augmented generation (RAG) is an AI framework and powerful approach in NLP (Natural Language Processing) where generative AI models are enhanced with external knowledge sources and retrieval-based mechanisms. These appended pieces of outside knowledge provide the model with accurate, up-to-date information that supplements the LLM’s existing internal representation of information. As the name suggests, RAG models have a retrieval component and a generation component.