Built with BigQuery: How to supercharge your product data with Google Cloud and Harmonya
Harmonya relies on BigQuery to build and maintains data pipelines and train and serve machine learning models for its product enrichment service.
Harmonya relies on BigQuery to build and maintains data pipelines and train and serve machine learning models for its product enrichment service.
At Snowflake, we’re helping data scientists, data engineers, and application developers build faster and more efficiently in the Data Cloud. That’s why at our annual user conference, Snowflake Summit 2023, we unveiled new features that further extend data programmability in Snowflake for their language of choice, without having to compromise on governance.
In today's fast-paced, data-driven world, deeper data insights and faster time to value are paramount if you want your business to stay competitive and thrive. Decision-makers need instant access to all their data sources to make sound business decisions — and they need to have trust in their data. However, data quality is often overlooked. According to Gartner, poor data quality costs organizations an average of $12.9 million annually. What’s going on?
During the COVID-19 pandemic, telcos made unprecedented use of data and data-driven automation to optimize their network operations, improve customer support, and identify opportunities to expand into new markets. This is no less crucial today, as telcos balance the needs to cut costs and improve efficiencies while delivering innovative products and services.
The world is undergoing a remarkable transformation fueled by data. Organizations have accumulated silos across their data infrastructure to support various workloads, languages, tools, and formats because of technology limitations. These silos can have major consequences in the form of greater operational burden, security vulnerabilities, increased total cost of ownership, incomplete insights, and reduced agility.
Containers have emerged as the modern approach to package code in any language to ensure portability and consistency across environments, especially for sophisticated AI/ML models and full-stack data-intensive apps. These types of modern data products frequently deal with massive amounts of proprietary data.