Revolutionizing SQL with pipe syntax, now available in BigQuery and Cloud Logging
Support for pipe syntax in GoogleSQL brings the elegance of piped data flow to SQL queries in BigQuery and Cloud Logging.
Analytics
Govern an Open Lakehouse with Snowflake Open Catalog, a Managed Service for Apache Polaris
To enhance security and ease operational burden, many organizations with data lakes or lakehouses want flexibility to securely integrate their tools of choice on a single copy of data. An open standard for storage format and catalog API has helped, but there’s still a need for open standards for the catalog, including a consistent way to apply security access controls to data.
3 Ways to Increase Trust in Your Epicor Data
Epicor’s ability to provide industry-focused, scalable, and customizable ERP solutions has made it a popular choice for organizations across the globe. Epicor’s built-in reporting capabilities are useful for standard reports but can be limiting for organizations that require more advanced analytics. Without deep technical knowledge of Epicor’s data structures, attempting to manually create custom reports can create serious roadblocks to data trust within your organization.
Shift Left: Headless Data Architecture, Part 1
The headless data architecture is an organic emergence of the separation of data storage, management, optimization, and access from the services that write, process, and query it. With this architecture, you can manage your data from a single logical location, including permissions, schema evolution, and table optimizations. And, to top it off, it makes regulatory compliance a lot simpler, because your data resides in one place, instead of being copied around to every processing engine that needs it.
AWS on the future of data lakes, metadata and AI innovation
The Director of Product Management at AWS sat down with Fivetran to unpack how Iceberg and metadata are reshaping the future of data lakes, data warehouses and AI.
Introducing Container Runtime: Enabling Flexible, Scalable Training and Inference on GPUs from a Snowflake Notebook
Predictive machine learning continues to be a cornerstone of data-driven decision-making. However, as organizations accumulate more data in a wide variety of forms, and as modeling techniques continue to advance, the tasks of a data scientist and ML engineer are becoming increasingly complex. Oftentimes, more effort is spent on managing infrastructure, jumping through package management hurdles, and dealing with scalability issues than on actual model development.
The easy button for replicating PostgreSQL data into Snowflake
Snowflake customers can take advantage of Fivetran’s data movement and data replication capabilities as a native application available in the Snowflake Marketplace.
The History of Chatbots: A Timeline of Conversational AI
From ancient Greek myths of talking statues to the modern-day Alexa and Siri, the concept of machines capable of understanding and responding to human language has captivated us for centuries. In recent years, this concept has evolved into AI chatbots, highly sophisticated tools that can read our queries and perform tasks ranging from customer service to automated alerts.
Episode 11: The future of data lakes: Open table formats, metadata and AI | AWS
Paul Meighan, Director of Product Management at AWS, shares how enterprises are increasingly looking for ways to integrate more data sources in their environment — especially with data lakes. From turning S3 buckets into databases to establishing better metadata layers, Meighan explores the rapid evolution of data lakes alongside data warehouses. He also explains the pivotal role AI, ML and GenAI workloads and applications will play in large metadata environments, driving innovative analytics and business insights.
AI Adoption in SMBs: Key Trends, Benefits, and Challenges from 100+ Companies
AI Adoption in SMBs: Key Trends, Benefits, and Challenges from 100+ Companies With larger competitors already using AI to streamline operations and gain a competitive edge, SMBs can’t afford to fall behind. But for many, adopting AI is easier said than done. Limited budgets, lack of in-house expertise, and the fear of wasting time and resources on the wrong tools often leave business owners stuck in decision paralysis.