Google Cloud increased the clarity of its database and data analytics platforms, rolling out new features targeted at helping enterprises glean more value from the mounds of data most are just sitting on.

The first is the Google Cloud Dataplex platform, which acts as a data fabric that provides an integrated analytics layer to help with managing, securing, and analyzing data at scale. It uses Google’s artificial intelligence (AI) and machine learning (ML) tools to help organizations manage data consistency.

The next is its Analytics Hub, which allows companies to create, curate, and manage analytics exchanges in a secure manner and in real time. It provides a way to combine datasets with Google’s vast trove of commercial and public data, and uses its already established BigQuery as a base for sharing capabilities.

The final new piece is its Datastream, which is described as a serverless change data capture and replication service. It allows users to replicate data streams in real time between Oracle and MySQL databases into Google Cloud services like BigQuery, Cloud SQL, Storage, and Cloud Spanner so that an organization can power analytics, database replication, and event-driven architectures.

Gerrit Kazmaier, VP and GM for databases and data analytics at Google Cloud’s Looker division, explained in a press briefing that the new products feed into the cloud giant’s desire to construct a full data lifecycle. He noted that for Google this involves “four key pillars.”

The first is openness, or the idea that “data needs to be freely accessible and data can reside in many places," Kazmaier explained. “This means that we are fully embracing the cloud and we are fully embracing the ecosystem all around us so you can process your data very precise and you can pick the best available tool and service to work with.”

This also applies to how customers use Google Cloud’s data services. Kazmaier said the services can plug into data coming from different cloud providers or applications from third-party vendors.

The second is the use of intelligence to self-optimize systems to reduce manual oversight of those systems. This taps into the AI and ML aspects used in the new products.

The third pillar is trust, which “means that you trust the integrity of the data, the availability of your data, and that you know that your data is being secured by only the highest standards,” Kazmaier said. “It’s important that our customers know that they are safe and protected at any point in time, at the highest standards.”

The final pillar is the ability to handle data at scale, which Kazmaier said “cannot be overstated.”

“Data is a vast, infinite resource,” he said. “This means that your capacity to store, process, and analyze [that data] ultimately defines how much value we can derive from it.”

“It's very important that we understand that when we think of data as an ability that we need to think about these interconnected areas and skills because data actually represents a value flow, and we are all about connecting the state of flow together and in the value flow it actually represents,” Kazmaier added, when discussing Google’s overall view of the market.

The focus and updates are important for Google as it attempts to cement its position in the space. It made a big push in mid-2019 when it acquired Looker for $2.6 billion, but remains just outside the market leadership position according to a recent Gartner Magic Quadrant ranking of Analytics and Business Intelligence platforms.