Why the modern data stack is critical for leveraging AI

Databricks’ 2023 State of Data + AI report highlights the importance of the modern data stack in leveraging AI
June 5, 2023

At this point, it’s difficult to avoid discussions about AI. The hype around Large Language Models (LLMs) is real and growing. In Databricks’ 2023 State of Data + AI report, they found that the number of companies using SaaS LLM APIs (used to access services like ChatGPT) has grown 1310% between the end of November 2022 and the beginning of May 2023. 

This is unsurprising if you’ve worked anywhere near tech in the past year. The opportunity to communicate with computers through natural language is revolutionary, and has led to companies looking for innovative ways to interweave artificial intelligence (AI) into their products and services. 

But, as organizations continue to invest in ways to leverage AI, data serves as the most valuable asset — and the biggest risk. The ability to move, transform and leverage large volumes of data is table stakes if you’re looking to train LLMs or other models. 

That makes the modern data stack more necessary than ever before. Without effective tools and practices, the movement and transformation of data can block you from successfully implementing this innovative technology. 

With that in mind, as you read through Databricks’ 2023 State of Data + AI report, here are some questions to consider before you embark on your AI journey. 

How are you moving data? 

That’s one of the first questions we’d ask any team looking to start leveraging AI. Depending on the maturity of your data practice, it’s one of the first factors that can trip you up on your quest to utilize AI. 

While you might have the expertise and talent on-hand to build and maintain pipelines, is that an effective use of your resources? Building and maintaining pipelines is an unnecessary bottleneck for mature data organizations. 

That’s especially true when you consider what effects a failure can have on your AI aspirations. Inaccurate or unreliable data can destroy the output of predictive modeling, underscoring the need for reliability and ease in data movement. 

Clearly we’re not alone in that thought, as Databricks found that the data integration market is one of the fastest-growing with 117% YoY growth. 

Spending finite time and resources on something you can automate is inefficient, and directly opposed to the efficiency gains you’re looking to benefit from with the use of AI. 

Utilizing an ELT platform like Fivetran, you can automate the movement of data from any source — freeing your data engineers to focus on higher-value work that contributes towards your AI goals. 

How are you transforming data? 

If you’re looking to train LLMs – or any model – you’ll need large amounts of data, but how that data is transformed is also critical. 

Machine learning, at its simplest level, is teaching a machine to recognize patterns and make predictions based on what it was taught. Raw data is typically not structured or formatted in a way that makes that process easy. 

That means you’ll need to transform large volumes of data to match one specific norm to serve that purpose — including revising, cleansing, deduplicating, and more depending on the current condition of your raw data. 

It then makes sense that Databricks found dbt™, the leading SQL-based transformation tool, one of the fastest growing data tools on the market, growing by 206% YoY. The second fastest growing tool? Fivetran at 181%, as our ELT platform is integrated tightly with dbt while providing additional features that make data transformation faster and more powerful.

Solutions like dbt and an ETL platform like Fivetran make the process of data transformation feasible at the scale and volume required to train models regardless of the output of your data sources. By automating the movement and transformation of data, you’re removing a risk and bottleneck that both can create. 

How are you storing your data? 

The data required for training, especially to create multi-modal outputs such as generative images or video, may be a mixture of structured and unstructured data. Depending on your current architecture, it might prove difficult to centralize that data in one location. 

While a data lake might have the flexibility required for all data types, it might lack the rigidity in structure to prove efficient for data modeling. Equally, while a data warehouse might have the orderly structure required to store ready to use data, it might also be difficult to utilize for the vast array of raw structure and unstructured data you’d need for training. 

Enter: the data lakehouse. A data lakehouse is a modern data solution that combines the best features of a data warehouse and a data lake. It combines the flexibility of a data lake (which allows you to store unstructured data) and the management methods of a data warehouse to unify the data you’ll utilize to train models. 

In fact, usage of Databricks’ Lakehouse is exactly how they sourced the information in their recent 2023 State of Data + AI report and it’s increasingly being used for data warehousing, including serverless data warehousing with Databricks SQL, which grew 144% YoY. 

What will you leverage AI to accomplish?

This last question is the one we can’t help you answer. While we can ensure you have the modern data stack required to start your AI journey, how you utilize that stack to achieve innovative results is fully in your hands. 

We’re already seeing it evolve the ways we develop our very own product, as we leverage AI in new ways to produce improved results for our customers, and do so faster. 

While the answer to this question will change depending on each organization and their intent, one thing is certain: the future is going to look very different than today. Some of the uses we’ll see for LLMs will undoubtedly alter every industry. 

This recent report from Databricks has made clear that many organizations are investing in AI, but it also highlights how organizations understand the integral role data plays in their success to do so. 

With that, I’ll leave you with this final statement, written by Google Bard: 

“Data is the new oil, and AI is the new engine. By harnessing the power of data, AI can help us to solve some of the world's most pressing problems.” 

[CTA_MODULE]

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Data insights
Data insights

Why the modern data stack is critical for leveraging AI

Why the modern data stack is critical for leveraging AI

June 5, 2023
June 5, 2023
Why the modern data stack is critical for leveraging AI
Databricks’ 2023 State of Data + AI report highlights the importance of the modern data stack in leveraging AI

At this point, it’s difficult to avoid discussions about AI. The hype around Large Language Models (LLMs) is real and growing. In Databricks’ 2023 State of Data + AI report, they found that the number of companies using SaaS LLM APIs (used to access services like ChatGPT) has grown 1310% between the end of November 2022 and the beginning of May 2023. 

This is unsurprising if you’ve worked anywhere near tech in the past year. The opportunity to communicate with computers through natural language is revolutionary, and has led to companies looking for innovative ways to interweave artificial intelligence (AI) into their products and services. 

But, as organizations continue to invest in ways to leverage AI, data serves as the most valuable asset — and the biggest risk. The ability to move, transform and leverage large volumes of data is table stakes if you’re looking to train LLMs or other models. 

That makes the modern data stack more necessary than ever before. Without effective tools and practices, the movement and transformation of data can block you from successfully implementing this innovative technology. 

With that in mind, as you read through Databricks’ 2023 State of Data + AI report, here are some questions to consider before you embark on your AI journey. 

How are you moving data? 

That’s one of the first questions we’d ask any team looking to start leveraging AI. Depending on the maturity of your data practice, it’s one of the first factors that can trip you up on your quest to utilize AI. 

While you might have the expertise and talent on-hand to build and maintain pipelines, is that an effective use of your resources? Building and maintaining pipelines is an unnecessary bottleneck for mature data organizations. 

That’s especially true when you consider what effects a failure can have on your AI aspirations. Inaccurate or unreliable data can destroy the output of predictive modeling, underscoring the need for reliability and ease in data movement. 

Clearly we’re not alone in that thought, as Databricks found that the data integration market is one of the fastest-growing with 117% YoY growth. 

Spending finite time and resources on something you can automate is inefficient, and directly opposed to the efficiency gains you’re looking to benefit from with the use of AI. 

Utilizing an ELT platform like Fivetran, you can automate the movement of data from any source — freeing your data engineers to focus on higher-value work that contributes towards your AI goals. 

How are you transforming data? 

If you’re looking to train LLMs – or any model – you’ll need large amounts of data, but how that data is transformed is also critical. 

Machine learning, at its simplest level, is teaching a machine to recognize patterns and make predictions based on what it was taught. Raw data is typically not structured or formatted in a way that makes that process easy. 

That means you’ll need to transform large volumes of data to match one specific norm to serve that purpose — including revising, cleansing, deduplicating, and more depending on the current condition of your raw data. 

It then makes sense that Databricks found dbt™, the leading SQL-based transformation tool, one of the fastest growing data tools on the market, growing by 206% YoY. The second fastest growing tool? Fivetran at 181%, as our ELT platform is integrated tightly with dbt while providing additional features that make data transformation faster and more powerful.

Solutions like dbt and an ETL platform like Fivetran make the process of data transformation feasible at the scale and volume required to train models regardless of the output of your data sources. By automating the movement and transformation of data, you’re removing a risk and bottleneck that both can create. 

How are you storing your data? 

The data required for training, especially to create multi-modal outputs such as generative images or video, may be a mixture of structured and unstructured data. Depending on your current architecture, it might prove difficult to centralize that data in one location. 

While a data lake might have the flexibility required for all data types, it might lack the rigidity in structure to prove efficient for data modeling. Equally, while a data warehouse might have the orderly structure required to store ready to use data, it might also be difficult to utilize for the vast array of raw structure and unstructured data you’d need for training. 

Enter: the data lakehouse. A data lakehouse is a modern data solution that combines the best features of a data warehouse and a data lake. It combines the flexibility of a data lake (which allows you to store unstructured data) and the management methods of a data warehouse to unify the data you’ll utilize to train models. 

In fact, usage of Databricks’ Lakehouse is exactly how they sourced the information in their recent 2023 State of Data + AI report and it’s increasingly being used for data warehousing, including serverless data warehousing with Databricks SQL, which grew 144% YoY. 

What will you leverage AI to accomplish?

This last question is the one we can’t help you answer. While we can ensure you have the modern data stack required to start your AI journey, how you utilize that stack to achieve innovative results is fully in your hands. 

We’re already seeing it evolve the ways we develop our very own product, as we leverage AI in new ways to produce improved results for our customers, and do so faster. 

While the answer to this question will change depending on each organization and their intent, one thing is certain: the future is going to look very different than today. Some of the uses we’ll see for LLMs will undoubtedly alter every industry. 

This recent report from Databricks has made clear that many organizations are investing in AI, but it also highlights how organizations understand the integral role data plays in their success to do so. 

With that, I’ll leave you with this final statement, written by Google Bard: 

“Data is the new oil, and AI is the new engine. By harnessing the power of data, AI can help us to solve some of the world's most pressing problems.” 

[CTA_MODULE]

Interested in seeing what else Databricks found in their 2023 State of Data + AI report?
Download the report now

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Commencer gratuitement

Rejoignez les milliers d’entreprises qui utilisent Fivetran pour centraliser et transformer leur data.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.