Intelligent Data Engineering for Enterprise AI with Databricks and Informatica

Generative AI holds tremendous promise for how organizations unlock value from their data. However, it also comes with a litany of challenges around ensuring accurate and relevant outcomes rooted in true intelligent data management. In fact, in a recent MIT Technology survey of 600 CIOs, 72% of execs said that data challenges are the biggest factor jeopardizing AI success. As a result, we constantly talk to customers for whom AI projects are top of mind – but are also struggling to realize business value in production.

Databricks and Informatica are reshaping the data management landscape to deliver intelligent solutions for enterprise AI applications. By combining Informatica’s low-code/no-code data management expertise to discover, catalog, and govern data from diverse source systems with Databricks’ AI-optimized intelligent data warehousing capabilities, organizations can:

• Accelerate the development of intelligent data pipelines
• Ensure data quality and governance
• Deploy scalable GenAI applications
• Enable all end-users including the line-of-business (LOB) to gain actionable insights from their data.

Accelerated pipeline development, in particular, highlights a core value driver for data teams today. Only through democratizing data access and supercharging the productivity of data professionals can organizations become truly data-driven. In this blog, we’re going to explore how Databricks and Informatica can empower your data professionals to tap into the limitless potential of your enterprise data. In fact, we’re so excited about this topic that we’ve dedicated an upcoming webinar to it – more details at the bottom of this post.

For now, let’s double click into the partnership.

Challenges in building high-quality, trusted AI systems

Every organization has a surplus of data they’d like to unlock value from but an overwhelming scarcity of resources that can extract that value. Large language models (LLMs), in particular, have demonstrated remarkable capabilities in generating human-like text and providing insightful answers. However, their effectiveness is often limited by the scope of their training data, which may not always be up-to-date or factually accurate. This poses significant challenges for enterprises aiming to deploy generative AI or traditional AI applications in production environments, where accuracy and reliability are paramount.

At Databricks, we believe that the key to unlocking the full potential of GenAI lies in grounding these models with reliable, enterprise-specific data. By integrating LLMs with proprietary data, companies can harness the power of AI to generate valuable insights tailored to their unique business contexts. This approach not only enhances the accuracy of AI outputs but also mitigates risks associated with hallucinations and misinformation.

Combining LLMs with enterprise data can revolutionize various business use cases, including:

• Customer Support Bots: Providing accurate and context-aware responses to customer inquiries based on current company solutions.
• Internal Q&A Bots: Assisting employees with quick access to up to date organizational knowledge.
• Text Generation: Crafting personalized emails, marketing content, and reports based on corporate brand guidelines and context.
• Business Insights: Uncovering actionable insights from large datasets based on company-specific jargon and metadata.

Databricks and Informatica: AI-powered Data Management

Recognized as the 2024 Databricks Data Integration Partner of the Year, Informatica provides cloud-native data integration on the Databricks Data Intelligence Platform. The partnership empowers enterprises to tap into the full potential of their data across disparate enterprise systems while taking advantage of advanced AI systems in Databricks to improve the efficiency and performance of data engineering workloads.

We combine Informatica’s Intelligent Data Management Cloud (IDMC) with Databricks SQL, the intelligent warehouse built on the lakehouse, to dramatically simplify all aspects of data management so data engineers can build reliable data pipelines for enterprise AI.

1. Consolidate enterprise data into the lakehouse: Identify data from a variety of internal and external data sources (e.g. Salesforce, Oracle database, Netsuite, MySQL, etc.) to integrate into the Databricks SQL. Customers build zero-cost data pipelines with visual mappings in Informatica that are automatically translated to SQL for Databricks SQL pushdown. Informatica has over 300 pre-built connectors to bring data from on-premises, cloud, modern and legacy systems into Databricks SQL to make it easily accessible for downstream applications like RAG. To bring efficiencies, Databricks SQL uses AI systems to analyze workloads and improve performance automatically enabling data engineers to build pipelines faster without any knobs.
2. Build a trusted data foundation – Informatica Cloud Data Governance and Catalog integrates tightly with Unity Catalog, the unified governance framework for managing data across various domains, including business intelligence, data engineering, and machine learning. For data and AI assets in Databricks Data Intelligence Platform, Unity Catalog offers access controls (securing data access based on user roles), data lineage (tracking the flow of data through various processes), discovery and monitoring (facilitating the identification and tracking of data assets) and metadata management (organizing and tagging data for easy retrieval and compliance). Informatica then brings this rich metadata from Unity Catalog into its enterprise catalog to keep track of data across both Databricks and on-premises with a trusted, high-fidelity view of data entities via its Master Data Management (MDM) offering within IDMC.

Check out this talk to learn more about how KPMG transformed its on-premise data estate to a future-proof, cloud-based enterprise data capability with Databricks and Informatica.

3. Transform and curate data for AI applications: Informatica’s metadata intelligence prioritizes and selects only trusted data to be used for AI systems such as RAG. IDMC’s advanced integration supports seamless data ingestion from various sources, improving RAG model outcomes with enhanced data quality and contextualization. Learn more about Informatica’s blueprint for Databricks DBRX here.

Register for the Free November 2024 Virtual Event

In the midst of recent GenAI hype, it’s been sometimes difficult to separate real value from the noise. AI value is impossible without a trusted data foundation, and a trusted data foundation is impossible without a modernized approach to data engineering. In Intelligent Data Engineering: Beyond the AI Hype, we’ll explore how to modernize your approach to data engineering through real data intelligence.

Register today to reserve your spot, and join us in November to hear speakers like Databricks Distinguished Engineer Michael Armbrust and more discuss:

• Leveraging conversational AI to empower every data practitioner to author better code, and diagnose and fix issues faster
• Unifying ingestion, transformation and orchestration in a single streamlined solution
• Simplifying the building and operation of production ingestion pipelines with native, scalable connectors to a variety of data sources

Learn more and register here