Fivetran launches Managed Data Lake Service with Google Cloud

Fivetran has unveiled its new Managed Data Lake Service for Google's Cloud Storage, aiming to support almost 4,000 joint customers in improving data lake functionality while maintaining high governance standards.

The newly announced service allows enterprises to centralise both structured and unstructured data from over 700 connectors directly into Google's Cloud Storage. This integration organises data into a query-ready open table format, enhancing scalability and cost-efficiency for AI, analytics, and modern storage needs.

George Fraser, CEO of Fivetran, highlighted the importance of this capability, noting, "AI requires vast amounts of high-quality data, but getting that data into the right format at scale is a major challenge. Our Managed Data Lake Service for Cloud Storage automates the entire process — moving, organising, and optimising data in open table formats — so businesses can focus on leveraging insights and driving innovation rather than managing infrastructure."

The service permits enterprises to harness Fivetran's ecosystem of over 700 connectors to centralise large volumes of data into Google's Cloud Storage. This creates a robust foundation for developing custom large language models and other AI applications designed to be context-aware, effectively taking businesses from the experimental phase of AI deployment to scalable enterprise solutions.

It offers several essential features, including reduced data ingest compute costs, swift data movement facilitated by high-performance pipelines, and automatic conversion of data into open table formats like Apache Iceberg and Delta Lake. This conversion enhances interoperability, ensuring a seamless user experience.

Furthermore, the service integrates natively with Google Cloud's BigQuery metastore, providing cataloguing capabilities to enhance governance and compliance. This integration allows users of Google Cloud's BigQuery platform to query the stored data with the standard ease and speed, akin to native BigQuery tables.

Yasmeen Ahmad, Managing Director of Data Analytics at Google Cloud, remarked on the increasing complexity of enterprise datasets, saying, "Enterprises are working with larger and more complex datasets than ever before, and they need a seamless way to centralise and prepare that data for AI and analytics. Fivetran's Managed Data Lake Service for Cloud Storage helps businesses get their data into open table formats efficiently, so they can take full advantage of Google's Cloud's leading AI and data analytics capabilities without the operational overhead."

Currently, Fivetran is onboarding Cloud Storage customers and anticipates that this integration will lead to greater efficiency and cost reduction for organisations deploying AI and data-centric applications.

As a part of its strategic alliance with Google Cloud, where over 100,000 unique connectors are already operational, Fivetran extends its support to Cloud Storage through the Managed Data Lake Service. This enables their substantial customer base to optimise data lake flexibility and scalability while guaranteeing adherence to enterprise compliance and governance standards.

Bhaskar Kalita, executive head of financial services for Quantiphi, a partner of Fivetran, attested to the benefits of the integration, "At Quantiphi, we've seen first-hand how Fivetran's seamless data integration capabilities enable us to deliver faster, more reliable AI and analytics solutions for our clients. Our partnership with Fivetran allows us to leverage their industry-leading data movement technology, ensuring that we can quickly and efficiently centralise and transform complex datasets into actionable insights. This collaboration gives our clients the confidence to innovate and scale their AI initiatives, knowing they have a solid, secure data foundation that supports their most critical business decisions."

Fivetran also emphasises its commitment to up-to-date, secure data management through its fully managed change data capture, table maintenance, and native BigQuery Metadata catalogue integration, ensuring data readiness for production-level AI applications.