The way that companies govern data has evolved over the years. Previously, data governance processes focused on rigid procedures and strict controls over data assets. But now, with the data-driven culture, modern enterprises are adopting an agile approach toward data governance that primarily centers around data accessibility and empowering business users to take responsibility for governing and managing data.

Automated data governance is a relatively new concept that is fundamentally altering data governance practices. Traditionally, organizations have relied on manual processes to ensure effective data governance. This approach has given governance a reputation as a restrictive discipline. But, as organizations increasingly adopt automation in their governance processes, this perception is changing.

The mid-sized enterprise is the fastest-growing market opportunity for data storage. But not just any storage system will do. These days, mid-sized enterprises must handle the complexities of unremitting data growth and distributed infrastructure, meet sustainability goals, manage the diverse storage needs of mission-critical applications, and respond to user requirements. Oh, and they need uninterrupted access to their data no matter what.

From social media and Google reviews to sensors and AI assistants, development teams today have access to so much user data that it sometimes feels like a blessing and a curse. This user data, often called big data, consists of structured and unstructured data from various sources, like the ones mentioned above. Traditional testing techniques aren’t built to handle the complexity of these large datasets.

The journey toward achieving a robust data platform that secures all your data in one place can seem like a daunting one. But at Snowflake, we’re committed to making the first step the easiest — with seamless, cost-effective data ingestion to help bring your workloads into the AI Data Cloud with ease. Snowflake is launching native integrations with some of the most popular databases, including PostgreSQL and MySQL.

Snowflake is a modern, robust data platform for cloud data management that provides users with the opportunity for unique scalability, flexibility, and performance. However, keeping costs under control is crucial to maximize the benefits on offer with this platform.

If you're noticing bottlenecks or disadvantageous data silos in your enterprise workflows, enterprise automation could be the answer.

Trying to integrate data without knowing your starting point is like taking a road trip without a map—you’re bound to get lost. To navigate the challenges of data integration, IT leaders must first evaluate their current data setup. This means taking stock of all your data sources, understanding their quality, and identifying integration points. It’s like conducting a thorough inspection before renovating a house; you must know what you’re working with.

Learn why successful AI initiatives require a well-executed data foundation to drive business value.

Learn to a knowledge base with an AI chatbot frontend to your BigQuery or unstructured Cloud Storage data using RAG and BigQuery ML.