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Data Integration with Azure: How to make your business AI-ready

Before you can start using AI, you're going to need to collect your business data, transform and clean it, and store it properly. Here's how to do that using Azure.

May 17, 2024 • 4 Minute Read

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  • Cloud
  • Data
  • Business
  • AI & Machine Learning

Have you ever wondered what the magic of AI is? If you answered data, you’re spot on!

If you’ve ever heard the saying “Garbage in, Garbage out”, that couldn’t be more true in today's AI-powered world. In order to provide meaningful and accurate answers to your queries, an AI requires accurate data. If it’s not accurate — say, the data you give it is irrelevant or out of date — the answers the AI provides is also going to suffer.

Now, you may or may not realize, but your company is probably sitting on a tonne of valuable data. You might train your own models using your data, or you might ground a Large Language Model (LLM) with your data to ensure the quality, and accuracy of the responses. The process of using your own data with a LLM to generate responses even has a name — Retrieval Augmented Generation, or RAG for short. 

But before you can go about any of these wonderful things, you need to clean up that data.

In a lot of businesses, your data is scattered across various systems and data storage repositories in varied formats. This makes it difficult to ground or even fine tune your LLM. To fix this, you need a way to bring your data together, transform and clean it up, and store it so it’s ready to be used. 

That’s where data integration comes in. In this article, we’ll explain how to go about this using Azure solutions.

What is data integration?

Data integration is the process of bringing together all of your data from various sources and preparing the data so it can be easily consumed, for example in training or grounding an AI model. 

How can I go about data integration?

There are a number of ways you can go about data integration. Some common approaches are using ETL or ELT processes, using Microsoft Fabric, or using cloud-based solutions such as Azure Data Factory or Azure Synapse Analytics Pipelines.

1. Process it upfront using ETL

Using Extract Transform Load (ETL) is considered the “old fashioned” way of going about data integration. With this method, you process the data — combining it from multiple sources —  prior to loading it into something like a relational data warehouse. 

2. Make it a future problem using ELT 

Extract Load Transform (ELT) is a newer approach. Unlike ETL, you upload the data first into something like a data lake, and worry about the transformation process later.

3. Keep it decentralized and just use Microsoft Fabric

Microsoft Fabric is an all-in-one analytics solution that unites your data and services. With Fabric, you can use shortcuts to access your data anywhere, without having to move the data into analytical storage like a data warehouse or data lake.

4. Use cloud-based solutions to handle it your way

On Azure, you can use solutions like Azure Data Factory, Azure Synapse Analytics Pipelines or use data factory from within Microsoft Fabric to:

  • Collect or receive your data from its source

  • Optionally transform the data and then load it into either a data lake, or data warehouse

  • Combine the two and load it into a data lakehouse with One Lake in Microsoft Fabric.

Next steps: Analyzing your data

So, what do you do once you’ve got your data integrated? Can you jump straight to passing it to an AI model? The answer is no, hold your horses — you’ve got to analyze and prepare it first! Often your data is not suitable to be used by the AI model, and needs a bit more love and attention first.

How do I go about data analysis?

Start by exploring your data using any of the supported languages within notebooks. During your initial exploration of the data you might notice inconsistencies in the data. For example, you might find:

  • Data that is incorrectly formatted

  • Clearly invalid data that you want to filter out

  • Duplicate data that needs to be removed

  • Columns that aren’t required

  • New columns you want to create to make the data more meaningful

Remember, we want to provide the most accurate and concise information as possible to our model to get the best results!

On Azure, you can use notebooks in Azure Synapse Analytics, Azure Databricks and Microsoft Fabric to prepare your data.

Using your data

For Retrieval Augmented Generation (RAG) to work well, you need a way to provide your data to a LLM is a cost effective way. You can make it easier for the model to search your data by creating an index. 

On Azure, you can use Azure AI Search to index your data before using it to ground a LLM.

Want to learn more about data integration and analysis?

We’ve discussed how you can get your data ready for AI at a high level in this article, and hopefully this gives you a great place to start from. 

If you want to dive deeper into these data integration and analysis solutions, and how these might fit into your toolkit as an Azure Solutions Architect, check out my latest course: Microsoft Certified: Azure Solutions Architect Expert (AZ-305): Database, Integration, and Analysis Storage Solutions.

And, as always, keep being awesome!

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Wayne Hoggett

Wayne H.

Wayne Hoggett is a Senior Author, Cloud at Pluralsight with 20 years’ experience in Microsoft infrastructure including Windows Server, System Center, Exchange, SQL Server and, Azure. He is a Microsoft Certified Trainer that has earned over 20 certifications from Microsoft, Terraform, Citrix and ITIL throughout his career.

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