The demand for AI is growing, with over 80% of enterprises actively investing in AI initiatives to drive innovation, improve decision-making, and unlock new efficiencies. ForAI to be successful it relies heavily on the access to quality data that fuels it. Gartner reports that 85% of all AI models/projects fail because of poor data quality and lack of availability. In this blog, we’ll explore why data lakes are the best destination for AI workloads and how they help organizations drive innovation.

Traditional storage solutions fall short in the age of AI

While traditional data warehouses enable companies to store and access their data in a queryable format, they often cannot support the scale and flexibility necessary for advanced AI projects. With inflexible data structures and hefty storage and compute costs, traditional data storage solutions often especially fall short of meeting business needs when it comes to AI. 

The effectiveness of an AI model depends on data that is not only high in volume but also high in quality and relevance. To drive accurate and reliable insights from AI workloads, companies must modernize their data infrastructures to accommodate the growing scale, complexity, and variety of their data. This makes data lakes an ideal storage option to power AI workloads. With their cost-effective, scalable, and flexible approach to data management, data lakes provide the foundation that organizations need to fully maximize AI's business impacts.

Why modern data lakes drive AI success

Modern data lakes bring the best of both worlds, combining the flexibility and scalability of data lakes with the structure and performance of data warehouses. Open table formats innovations like Apache Iceberg and Delta Lake address historical management challenges while maintaining scalability. As organizations face growing demand for real-time insights, governance, and AI, modern data lakes are evolving into the preferred destination for analytics, bridging the gap between traditional warehouses and earlier data lake architectures.  

Scalable storage for diverse, high quality data 

Data lakes are built to handle large, complex datasets, including structured, semi-structured, and unstructured data. AI models rely on various data sources, such as logs, images, text, and real-time streaming data, all of which can be efficiently stored, managed, and accessed in a data lake. Open table formats, such as Delta Lake and Apache Iceberg further enhance data lake capabilities by improving data reliability, query performance, and interoperability across platforms. Data lakes are essential for AI, providing scalable storage and flexibility for unstructured data, which makes up most organizational data, to enhance model performance and enable deeper insights. 

Reduce costs without compromising performance

One of the key advantages of data lakes is their cost efficiency. Storage costs in data lakes are significantly lower than in traditional data warehouses, allowing organizations to store large amounts of AI-ready data without breaking the bank. Their modular architecture separates storage and compute, enabling businesses to scale each independently, optimize resource utilization, and reduce total cost of ownership (TCO). This flexibility prevents vendor lock-in, allowing teams to select the query engine that best suits their specific needs, whether for scale, latency, or media type without being tied to a single platform. Since AI workloads demand substantial storage and processing power, these cost savings can be transformative, making data lakes a highly cost-effective solution for powering AI.

Secure and compliant storage for AI-driven data

Businesses must protect their data and brand reputation by adhering to strict compliance and security requirements. While data lakes provide a flexible and scalable storage solution, they require robust security features to maintain data discoverability and compliance. Catalogs enable data lineage tracking ensuring transparency, while Fivetran supports granular access controls to ensure that only authorized users have access to sensitive AI training data. 

How Tinuiti transformed AI-driven marketing with a scalable data lake 

Tinuiti, a leading digital marketing agency, transformed its AI-driven marketing operations with the Fivetran Managed Data Lake Service. By shifting from a traditional data warehouse to an Amazon S3 data lake, Tinuiti accelerated client onboarding by 120x and eliminated 80% of manual data maintenance work, allowing engineers to focus on high-value initiatives like AI, advanced analytics, and predictive modeling. Fivetran automated data ingestion from 100+ marketing platforms, creating a scalable foundation for real-time insights. This transition reduced operational overhead, lowered costs, and empowered Tinuiti to deliver more accurate, AI-powered marketing insights.

“Fivetran has completely transformed our data operations. What used to take 2-4 weeks now takes minutes. It’s enabled our teams to focus and deliver on high-value work, like our new data lake that powers AI-driven insights for our clients.”
— Lakshmi Ramesh, VP of Data Services at Tinuiti

Fivetran Managed Data Lake Service modernizes your infrastructure to unlock AI success 

Fivetran Managed Data Lake Service streamlines data lake onboarding by automating the extraction, normalization, and deduplication of data from 700+ diverse sources, ensuring data is clean and reliable as it lands in the lake. Fivetran also handles the conversion of data to Iceberg or Delta Lake open table formats – unlocking interoperability across systems, enabling governance, and avoiding vendor lock-in. Fivetran continuously monitors data in the lake and handles ongoing table maintenance with schema evolution and performant change data capture to ensure the lake is fresh, efficient, and high-performing. 

Organizations leveraging Fivetran Managed Data Lake Service benefit from:

• Seamless integrations with leading data lake storage platforms, including OneLake, Azure Data Lake Storage (ADLS), Amazon S3, and Google Cloud Storage (GCS)
• Automated ingestion and conversion of raw data into structured formats, making it ready for AI workloads
• Lower total cost of ownership, with Fivetran reducing the ingestion costs to data lakes
• Automated data governance and metadata management with native integrations with popular data catalogs, ensuring data is not only well-managed but also compliant with industry standards and regulations

Future-proof AI workloads with modern data lakes

As AI continues to evolve, organizations must ensure their data infrastructure can keep up. Data lakes provide the flexibility, scalability and cost efficiency needed to support ongoing AI innovation. By adopting a data lake strategy, organizations can future-proof their AI workloads, drive long-term innovation and thrive in the era of AI.

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