In this age of digital acceleration, enterprises are moving past just managing data; they are defining success by how intelligently they utilize it. Data is the lifeblood of a variety of innovations from unique customer experiences to real-time fraud detection across all industries. Yet many companies are simply unable to unleash the power of those innovations by continuing to work with statically fractured ecosystems where dated warehouses, separated lakes, and siloed application stacks inhibit the ability for organizations to glean anything of value from this data. Siloed analytics stifle the value of artificial intelligence (AI), machine learning (ML) and advanced analytics to the point where agile competitors frequently feel the heat of rising customer expectations through falling margins.

The complexity of today's enterprise application ecosystems, spanning web, mobile and Internet of Things (IoT) interfaces continues to grow. This has created a need for new architectures that can procure and deposit data, not just as storage, but in a way that we can immediately use that data for downstream applications, processes, and AI-enabled decision making. This important transition establishes the need for a unified data architecture, specifically, a data lakehouse.  

Lakehouses have always been thought to fill the gaps between traditional data warehouses and data lakes. These lakehouses provide real-time analytics, scalable ML model training, support for unstructured data and flexibility of a true analytics workbench, all in a single offering. Cloud-native technologies such as Snowflake allow organizations to truly redefine their stacks as they decouple storage and compute, take on significant integrations, and build in agile data engineering use cases.

In this blog, we discuss how unified data architectures represent a lot more than just a technical evolution, they are a business necessity. We will discuss how the lakehouse paradigm overcomes historic challenges in data management, and how Snowflake's architecture enables enterprises to operationalize AI and ML for use in mobile apps, enterprise workflows, and mission-critical apps.

The Problem of Fragmentation: Traditional Data Warehouses vs. Data Lakes

Data warehouses have a long history as effective workflows for structured data processing and business intelligence workloads. Yet, as new enterprise applications were created and mobile applications developed, IoT devices would start releasing a massive scale of data in different forms. It was not feasible for a traditional data warehouse's strict structure. At the same time, new data lakes emerged with new features as the media created raw data in both, unstructured or semi-structured formats. They also provided a broader scope for data analytics, in governance, performance, trust, and consistency that were absent with lakes. The simple introduction of AI use cases and Machine Learning further exposed circumstances with trusting a lake, because while agile, they lacked the reliability and performance needed for enterprise-grade data pipelines.

The Lakehouse - Bridging the Gap

The lakehouse represents a combination of both data lakes and data warehouses. It has the data governance, and performance characteristics of a warehouse, but maintains the flexibility and scalability of a lake. However, it also has a lot of additional features:

• Separation of storage and compute for cost and scalability flexibility
• Support for different data types (structured, semi-structured, unstructured)
• Transactional consistency and ACID compliance
• Support for ML and AI workloads

The model eliminates the need to copy data between a warehouse and a lake, assigns operational requirements for, enabling real-time analytics, and delivering new advanced ML models on a single data platform.

Why Unified Architectures Matter for AI & ML

Both AI and ML depend upon disparate datasets with different formats and structures including behavioral logs, customer interaction records, sensor data, etc. Unified architectures can help collect these datasets into a single pipeline of data supporting:

• Real-time ingestion and transformation of data
• Feature engineering at scale
• Training and inferencing of models inside a data warehouse
• Applications across low-latency endpoints for personalization and prediction

When companies deconstruct the silos of data, they can transform actionable insights faster than before, and deploy AI and ML right into their mobile, and enterprise application workflows.

Snowflake's Contribution to the Unified Architecture Movement

Snowflake has taken a leading role in operationalizing unified data architectures. The Snowflake cloud-native platform is built for ease of use, high concurrency, and scalability, while providing native capabilities to do AI/ML at ingestion and inference at scale. Some of the key architectural attributes that Snowflake provides:

• Multi-cluster, shared-data architecture that allow independent scaling of storage and compute
• Native support for semi-structured data types such as JSON, Avro, and Parquet formatted data
• Using external functions to call out to APIs or run NLP and AI models directly from your data warehouse
• Use of Snowpark, with support for Python, Java, and Scala, to write ML and data engineering logic for your data directly inside Snowflake

These capabilities allow data engineers and app developers to build intelligent apps, whether it is an enterprise dashboard or a mobile app visible to customers. The intelligent apps are powered by real-time predictions and leverage natural language processing.

Real-time Personalization & Better NLP

Real-time personalization and Natural Language Processing (NLP) are one of the most valuable use cases for unified data. Unified data allows enterprises to spin up recommendation systems, dynamic pricing engines, and conversational AI with near-real time speeds, while minimizing or eliminating the need for development cycles.

• Real-time personalization and NLP support
• Customer personalization journeys in mobile applications
• Dynamic inventory and promotional engines in retail applications
• Real-time fraud detection in banking, financial services, and insurance (BFSI) applications

NLP workloads also take advantage of lakehouse architectures by providing a way for enterprises to enable text processing, sentiment analysis and summarizing documents in their data warehouse, at scale.

Future of Enterprise App Development

As enterprise app development continues to evolve toward data-native experiences, the architecture supporting the apps and analytical workloads will become more important. Unified data platforms offer faster iteration cycles, less infrastructure complexity, and better alignment between engineering and analytics teams. This allows data-native applications to:

• Have application logic and analytics co-located
• Better approach to data governance and compliance with data built into the architecture.
• Allow product teams to iterate on features like chatbots, real-time alerts or predictive insights, without provisioning separate ML infrastructure

Conclusion

Data lakehouses are an evolution of data architecture. They create an opportunity for enterprises to design and create intelligent, real-time enterprises. Through unified data architecture, organizations can eliminate the bottlenecks created by siloed systems, accelerate their ability to operationalize artificial intelligence and machine learning models, while uncovering value across enterprise and mobile applications.

With solutions like Snowflake, we are already living in the early days of the unified data future. One that is reshaping what enterprises can do with their data.