Organizations increasingly rely on streaming data sources not only to bring data into the enterprise but also to perform streaming analytics that accelerate the process of being able to get value from the data early in its lifecycle. As lakehouse architectures (including offerings from Cloudera and IBM) become the norm for data processing and building AI applications, a robust streaming service becomes a critical building block for modern data architectures.

Unlocking the full potential of your SAP solution requires the complete trust of your data. Without trust, users will second guess any insights, stalling business progress. Research has pinpointed three key pain points that companies encounter with their SAP data: a prevailing sense of data distrust, a lack of maintenance and data cleansing, and a shortage of skilled users. These pain points not only impede progress but also pose significant roadblocks to migrating to S/4HANA, the future of SAP.

Multi-tenancy is a concept that refers to the ability of a software application or system to serve multiple tenants, or customers, on a shared infrastructure. In simpler terms, it is the capability of a single instance of a software application to accommodate multiple users or organizations, each with their own datasets and customization options.

Enterprises are drowning in data. Structured, semi-structured or unstructured data for the modern, data-driven enterprise is everything, everywhere, all at once. But that’s also a challenge for enterprises looking to transform their data into usable information for business success. The sheer volume of data is challenging the ability of enterprises to find trustworthy, reliable data to drive their business decisions. Traditional data catalogs offer only structured data discovery.

AtScale enables Google Cloud users to build a single source of governed analytics to enable self-service business intelligence and data science programs.

How Windsor.ai, a Google Cloud Ready - BigQuery partner, helped Anything is Possible to build an automated pipeline into BigQuery.