Generative AI has the potential to make data accessible to every user within your organisation. It represents a paradigm shift in how data is harnessed and used. The excitement surrounding Generative AI must be tempered with a crucial realisation; you can’t simply apply a language model to your data and expect magical results. To unlock its true potential, your data infrastructure must be integrated, well-organised, easily accessible, and, most importantly, of high quality. Without these foundations, even the most advanced machine learning models, including Large Language Models (LLMs), will fall short of delivering the value you seek – rubbish in, rubbish out.
Generative AI isn’t here to replace human expertise; it’s here to amplify and accelerate it. As discussions about the future of Generative AI unfold, two essential themes have emerged: responsibility and actual capability. While Generative AI holds immense promise, there are areas where its tools should be used cautiously or may not be appropriate at all. In many cases, the generated output should be reviewed and employed within its proper context. It’s crucial to exercise care when presenting AI-generated content without verifying its references and accuracy.
The journey toward full automation in data and analytics is a long one. Current and future applications of Generative AI are designed to enhance productivity and, most significantly, make data more accessible to a broader audience. With this vision in mind, Databricks is committed to opening up its platform to a wider user base. Databricks recognises Generative AI as an extraordinary opportunity to ‘democratise’ data, and its AI Assistant stands as a testament to this mission. Already, this AI Assistant can generate code seamlessly through natural language, serving as a powerful productivity tool. The recent announcement of LakehouseIQ further signifies Databricks’ dedication to this cause, allowing users without extensive technical backgrounds to explore data effortlessly, all without the need for SQL or Python — just the simplicity of natural language.
For now, here are two key applications that highlight the advantages of utilising Databricks’ AI Assistant in code development, opening your organisation’s data to all.
1 – SQL Querying for End Users
For those who may not be SQL experts or are new to data analysis, Databricks’ AI assistant offers an efficient means of querying and extracting insights from your data. It empowers users to create complex queries without requiring expertise in coding languages. Users must still know what they’re seeking from the data. The AI Assistant provides a jump-start for this analysis, requiring users to frame the questions it should answer without the need to write intricate code.
A typical scenario could be:
A billing or finance analyst, maybe not that familiar with SQL, is looking to cross-check total meter data consumption to overall billing numbers. With access to a detailed meter data set, what is the right code to produce monthly summaries?
2 – Optimised Engineering / Advanced Analysis
Databricks’ AI assistant truly shines when it comes to empowering technical teams to build complex data processing logic and analyses. It efficiently translates natural language transformation or logic concepts into the necessary code for data processing in your notebooks. However, similar to the SQL application, users must possess a foundational understanding of the data and its context.
It’s a step-by-step process that does require clarity on the desired outcomes and the steps required to achieve them. For those well-versed in technical aspects, this tool offers a lightning-fast route to enhancing daily productivity by giving you a starting point to your code – including comments!
An example could be:
An analyst or data scientist looking to understand the trend in raw weather data and plot this temperature analysis by day, in order to playback these trends to others visually.
If you are interested in understanding how Databrick’s AI Assistant can assist your analytical capabilities, please feel free to reach out to Ignite’s data experts.