Data Integration Platform – Must Have Features In Gen AI Era

Modern applications increasingly rely on unified data access to high-quality, ready-to-use datasets. Gen AI has opened up new frontiers in technology and business applications. However, the effectiveness of these AI models depends on the availability of comprehensive and high-quality data to ground its inferences. As organizations collect vast amounts of data from various channels, the challenge lies in integrating this data to provide a unified view. Without unified data access and governance mechanisms, AI models may generate inaccurate or hallucinated outputs due to data silos, inconsistencies, and incomplete information. Inadequate access controls could also lead to unauthorized data exposure.

This is where data integration platforms could bridge the gap. Data integration platforms facilitate combining data from various sources and provide a usable, accurate, and up-to-date dataset for applications and business processes. These platforms must also incorporate strong governance features to regulate how AI models access and utilize sensitive data.

This guide explores the features you should look for when selecting a data integration platform for your Gen AI applications and other modern use cases.  

Summary of key data integration platform features 

The role of data integration platforms in modern application development

In data-driven enterprises, structured data powers most analytics and applications, from reporting to visualization dashboards. Over the past decade, advancements have enabled free-form data analysis, such as sentiment analysis on customer reviews or keyword extraction from unstructured text. Traditionally, data integration platforms have focused on building pipelines to ingest, clean, enrich, and transform structured and semi-structured data for downstream applications. 

Generative AI is changing the landscape of data-driven business decisions beyond traditional analytics and reporting. LLMs’ ability to parse natural language instructions removes the need for highly technical development or coding typically required for information extraction from data. Many use cases of generative AI technology in enterprises are being explored, and those providing the most value are applications backed by context.

Enterprise integration platform
for AI-ready data

Learn How

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  Accelerate integrations with pre-built, configurable, and customizable connectors
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  Deploy production-grade analytics and generative AI applications on a single platform
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  Monitor data quality with automated lineage to alert on data failures and errors

The architecture of a simple gen AI application (source: Nexla)

Data integration platforms play a critical role in modern generative AI application development.

Provide high-quality data for AI training and improvement

The advantage of large language models is that you can fine-tune them cost-effectively to customize the model to make informed inferences based on enterprise data. With improved in-context memory and larger context windows, these models use organizational data to provide insights. The true value of these applications lies in high-quality, relevant data that enables accurate inferences. AI models also learn and improve continuously from data and interactions, making reliable data a foundation for realizing long-term value.

Single source of truth for all data

Given the direction of AI-driven decision-making, a data integration platform acts as the single source of truth, providing high-quality, continuous data streams for applications across the enterprise. With AI applications, governance, and compliance have also become core considerations. Over time, a unified pool of high-quality, timely datasets that multiple AI applications and AI agents can access becomes useful for accelerated but regulated AI development. 

A data integration platform unites your data so you can work with all your data from within the platform. It provides a unified interface, data products, and governance models to speed up the process of rapid prototyping and experimenting.

Data integration platform features for Gen AI success

A good data integration platform would have hundreds of pre-built off-the-shelf features to clean, enrich, and restructure data for Gen AI development. We explain several features below.

Diverse connectors and parsers

To fully tap into your enterprise data for AI, you need data flows that connect any source to any destination. Your data integration platform should offer pre-built connectors for databases, data warehouses, cloud storage services, file transfer protocols, SaaS applications, and more. 

According to the State of Saas report by Productiv, enterprises rely on 473 different SaaS applications on average. It is not feasible to build custom integrations for each of these applications. You want to be able to establish these connections with a few clicks through pre-built API connectors. Such connectors should come with ready-to-use templates that handle the technical complexities of API authentication, data mapping, and endpoint management.

Beyond internal systems and SaaS platforms, Gen AI applications are increasingly relying on external data providers to enrich their business intelligence. This includes specialized financial market data, weather information, social media trends, etc. Instead of managing multiple API implementations and maintaining separate integration points, your data integration platform should handle the behind-the-scenes technical complexities and make it easy to connect to these providers.

A platform like Nexla offers comprehensive connectivity options, enabling integration with various data sources, including databases, cloud applications, flat files, APIs, and more. It also offers an adaptive integration engine that can accommodate new data sources and destinations as they emerge without requiring the development of new connectors for each instance. What sets Nexla apart is its ability to automatically create a source-agnostic abstraction layer above every connection. This means users experience consistent interaction patterns regardless of the underlying system’s specific nuances. On top of that, Nexla automatically generates API interfaces to query any consumable data product.

Modern data integration platforms like Nexla are transforming what was once a complex, code-intensive process into a streamlined, template-driven approach that business users can manage with minimal technical intervention.

End-to-end data processing for AI

LLMs perform well in generating code to query or automate analytical tasks. Contextual data quality and associated metadata determine the accuracy of these generated codes. Unstructured data processing for gen AI requires accessing and processing millions of documents, images, and videos stored across SharePoint, FTP, S3, Dropbox, and other systems. 

Semi-structured data requires data integration platforms that manage hybrid data pipelines that extract both structured and unstructured data and efficiently combine them for inference.

A data integration platform should support easy processing of all structured, unstructured, and semi-structured data formats for AI, transforming such data into a format that AI models can efficiently process and understand. For example, Nexla handles document parsing, converts them into vector embeddings, and makes these embeddings independently searchable—managing everything from loading to chunking and storage. You can also track and remove outdated indexed materials so your data remains current and relevant. 

In this context, data as a product approach is gaining popularity. It’s the idea of treating data sets as products with a lifecycle designed and maintained to prioritize quality, usability, and user satisfaction. Modern data integration platforms like Nexla adhere to this principle. 

Nexla automatically detects file formats upon ingestion and organizes data into “Nexsets”—data products independent of file type. They allow teams to distribute data in formats different from the originals, providing flexibility across systems and applications.

Support scalable RAG workflows

One of the most popular Gen AI application development processes in enterprises involves combining retrieval-based methods with LLMs—referred to as retrieval augmented generation(RAG). The foundations of any RAG-based application are access to high-quality searchable data and metadata tagging. RAG workflows require scalable ingestion pipelines that orchestrate multiple algorithms and LLMs to deliver quality output with security and governance. 

High level RAG architecture(source: Nexla)

A modern data integration platform must have native support for building RAG applications with modules for re-ranking, retrieval, and evaluation. For example, Nexla provides a robust solution for building RAG workflows. Their RAG-based chatbot engine is ready to use and connects to enterprise data across documents, databases, and APIs. It comes with built-in user-level governance. 

Nexla’s RAG chatbot

Speed and flexibility in data flow setup

Speed and flexibility in setting up data integrations is an important consideration. Customizing data flows for complex use cases can become complex and code-heavy. A good data integration platform reduces the time and effort required by offering pre-built functionality and low-code/no-code interfaces to make this process quick.

Consider the following use cases:

• Real-time inventory updates across services when a product is sold.
• Migrating a production database to a new cloud provider.
• Processing daily web application logs for reporting.
• Automatically routing customer support tickets to the right teams.

Some use cases need data transformations, while others require quick real-time processing. Each scenario comes with considerations like:

• Fast replication of data across systems.
• Efficient migration with minimal downtime.
• Rapid processing of large data volumes with low latency.
• Event-triggered, conditional workflows.

These aren’t one-off problems – they’re everyday challenges that businesses face repeatedly. That’s why a modern data integration platform needs to:

• Set up data flows quickly, minimizing setup time.
• Optimize architecture and resource usage.
• Offer customizations and built-in functions to handle each use case.

Top data integration platforms provide automation and scheduling features and ensure timely data delivery. This eliminates the need for manual orchestrations and reduces the risk of errors.

For example, Nexla supports four types of workflows, balancing standardization with flexibility:

FlexFlows

FlexFlows lets users focus only on how their data should be captured, transformed and delivered – while Nexla does everything else under the hood. An easy and flexible way to create any data flow, from simple A to B to really complex multi-step pipelines.

DB-CDC workflows

DB-CDC (Database–Change Data Capture) flows replicate tables across databases and cloud warehouses using CDC. They run on a Kafka engine and are suited for data migration and maintenance. Nexla lets you choose which tables to include, customize how data maps between systems, and configure table prefixes, lineage tracking, and column mapping. These are ideal for keeping your data in sync across different locations.

Replication workflows

Replication flows move unmodified files between storage systems at high speed. They can also clone tables between cloud data warehouses. Latency is minimized by processing all data flow nodes in memory and transferring new data as soon as it’s available. These workflows support structured and unstructured files and can route to multiple destinations. 

Spark ETL workflows

For large-scale data processing, Spark ETL flows modify data stored in cloud databases or Databricks and move it to another location. Powered by Apache Spark, these flows handle big data processing, focusing on reducing latency in data movement. They are ideal for consistent data transformation requirements, such as processing logs. Users can leverage pre-built transforms or Spark SQL to modify datasets before sending them to the target location. 

With these capabilities, Nexla simplifies data workflows. You can modify these workflows anytime, share the processed data within your organization, and create data products that other teams can use immediately.

Advanced data transformation capabilities

Data pre-processing and transformation are intensive steps in building data pipelines. Automating common data processing and transformation steps or providing pre-built transformation functions that can be easily customized accelerates this process. 

A good data integration platform simplifies data transformation with no-code or low-code interfaces and features that make applying, testing, and validating rules easy. Low-code/no-code features standardize and systematize the process with intuitive design, reducing the load on data engineers. You also want copilot capabilities that provide recommendations for automating parts of the work—like workflow scheduling, modification tracking, and file inclusion/exclusion.

Nexla, for instance, offers extensive pre-built functions covering mathematical operations, IP transformations, conditional logic, and specific tasks like PII removal. They can be quickly applied and customized so data engineers can transform data with just a few clicks. Such features simplify sensitive data encryption tasks, like hashing personal or health information. Nexla also allows users to share and reuse transformation functions across teams. Through Nexset panels, you can visualize and troubleshoot the applied data transformations. While no-code/low-code features handle many common scenarios, complex data transformations often require more flexibility and control. Recognizing this, Nexla supports custom transformations through Python, SQL, and JavaScript functions. These functions can be written once and reused infinitely across different data flows.

AI applications require a comprehensive approach to data management and data quality assurance through validations, filtering, and metadata tagging. Beyond these operations, your data integration platform must also support advanced requirements like vectorizing data for ML/AI applications and handling domain-specific data, such as medical or financial transactions. Nexla provides pre-built transformations like Text2Vectors for processing unstructured data. You can follow this tutorial to get started. 

Nexla’s Text2Vectors pre-built transformation

Nexla also supports cross-format data compatibility, with automatic file format and schema detection for quick transformations and validations.

Data accessibility and advanced security

A good data integration platform must make data accessible and break silos while making it easy to establish effective data controls and governance structures. A no-code or low-code interface significantly simplifies workflow setups, visualization, and troubleshooting. Such interfaces enable users across the organization to access and manipulate data products easily. By balancing data accessibility with advanced security measures, organizations can maximize the value of their data assets.

Centralized data governance is essential for establishing effective data controls. Organizations gain granular and overarching views of all processes by accessing and managing all data sources from a single control point. 

A robust data integration platform simplifies establishing controls and governance structures, often automating or natively supporting enterprise needs related to security and compliance. The ability to simplify data transformations, visualize workflows, implement centralized governance, and integrate features like SSO, schema templates, and activity monitoring allows for a more efficient and secure data environment. By making it easy to set up and manage security features, organizations can focus on creating value from their data rather than being bogged down by data preparation and engineering tasks. 

We have covered advanced security, monitoring, and compliance requirements in our article on data integration tools. 

Recommendations on choosing a data integration platform

To summarize, we recommend you consider the following checklist when choosing a modern data integration platform.

Comprehensive support for the Gen AI data lifecycle

• Supports all stages—from data discovery to flow design, management, governance, and collaboration.
• Connects any data source to any destination.
• Treats data as a product to encourage reuse across various applications.
• Facilitates user management and enforces data governance policies.

Flexible data flows

• It supports the quick setup of various workflow types, such as replication, CDC, and ETL. 
• Handles large data volumes with low latency, supports real-time processing, and is easily customized to your business needs

Enterprise-wide data accessibility

• Makes AI-ready data accessible and discoverable organization-wide.
• Promotes collaboration among data teams, such as shared transformation libraries and version control.

• Provides no code/low code interfaces to lower the learning curve and speed up implementation.
• Provides good documentation, tutorials, and support to assist users in setting up workflows and integrations quickly.

Advanced transformation and AI support

• Offers a library of pre-built transformations to simplify data transformation tasks.
• Supports gen AI application development with features like embedding and vector storage.

Last thoughts

Data integration platforms and data engineering expertise are integral to a good production-grade Gen AI application. A good platform reduces the workload of a skilled data engineer by standardizing workflows and centralizing controls. Selecting the right platform involves understanding your business needs and evaluating key features like unified data integration, advanced transformation capabilities, and centralized governance.