Considering BigQuery vs. Redshift for your data warehousing needs? This guide is for you. Both BigQuery and Redshift stand as leading cloud data warehouse solutions each offering a multitude of features catering to multiple use cases. Google’s BigQuery offers seamless scalability and performance within its cloud platform, while Amazon’s Redshift provides great parallel processing and tuning options.

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.

In this post, we'll add file upload capabilities to a Phoenix LiveView application and directly upload files to Amazon S3. Without further ado, let's get started!

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.

Enterprise Strategy Group compared BigQuery to alternative cloud-based enterprise data warehouse solutions.

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.

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.

What does it take for an architecture, engineering, or construction business to be profitable? Many look toward aggressive growth and expansion, either by geography or acquisition or both. However, growth requires significant spending on resources from new employees to acquisitions, and it takes time to see a return on your investment.

As business leaders cautiously navigate the uncertainties of 2024, marked by the mainstreaming of AI, heightened regulatory oversight, cost pressures, and supply chain disruptions, the need for agile and efficient business processes has never been more pressing. Amidst this backdrop of uncertainty, cloud BPM shines as a beacon of opportunity, offering organizations a transformative approach to streamline operations, drive innovation, and achieve better business outcomes.