Farmers today face a host of challenges, from volatile weather, weeds, pests, and diminishing resources. For more than a decade, John Deere has been helping farmers tackle these issues by collecting and using data on weather, soil conditions, and equipment maintenance. But managing and analysing this vast information required a robust solution.
Enter Microsoft Azure. By creating a data lake on Azure, John Deere is able to store all data in its raw form, ready for processing and analysis. Advanced analytics and machine learning transform this data into actionable insights, enabling farmers to make precise, data-driven decisions. This innovative approach helps farmers optimise irrigation, predict equipment needs, and enhance overall farm productivity and sustainability, ensuring a sustainable future.
By leveraging Microsoft Azure, John Deere has shown that with the right tools, even the age-old practice of farming can be revolutionised through data.
The Vital Importance of a Strong Data Foundation
A strong data foundation is vital to your business. This isn’t news to you. But with the increasing prevalence of AI, the need for a strong and well-managed data foundation that can handle all types of data – structured, unstructured, metadata, sensor data, and so on – is more important than ever.
Why?
Because the amount of data in the world only goes up, never down. The global datasphere is expected to grow from about 45 zettabytes in 2019 to 150-175 zettabytes by 2025. Managing this massive influx of data requires a robust infrastructure capable of scaling efficiently.
Because real-time data is more important than ever for making quick, informed, data-driven decisions. When you need to respond quickly to market changes, customer needs, and operational challenges, you don’t have time to be sifting through data. You just need a quick and reliable answer.
Because today’s data comes from a variety of sources: internal systems, customer interactions, social media, third-party services, and more. Integrating these diverse data streams into a cohesive and actionable dataset is essential for gaining comprehensive insights. A well-designed data foundation supports the seamless integration of various data types, ensuring that all relevant information is available for analysis.
As Peter Sondergaard, Senior Vice President and Global Head of Research at Gartner, Inc., famously said: “Information is the oil of the 21st century, and analytics is the combustion engine.” Data is your enterprise’s most valuable resource — but just like oil, data is useless unless it’s processed, managed, and used in the right way.
The Evolution of Data Platforms: From the Relational Database to the Lakehouse
- Early days of computing – Relational Databases (RDBMS)
Relational databases, or RDBMS, provided a structured way for companies to store and analyse customer data using SQL. They were reliable and straightforward, perfectly matching the smallscale data storage needs of the time.
- Late 1990s – Data Warehouses
Data warehouses emerged as structured environments optimised for analysing well-defined, highly structured data. They required data to be cleaned, transformed, and organised before storage, facilitating fast and efficient querying.
- Early 2000s – Data Lakes
With the rise of big data, Apache Hadoop, and data lakes, organisations began storing vast amounts of raw data in its original format. Data lakes allowed for the ingestion of various data types, providing flexibility for future use.
- Early 2020s to present – Data Lakehouses
Data lakehouses combine the best aspects of data warehouses and data lakes, offering the structured data management capabilities of data warehouses with the flexibility and scalability of data lakes.
The Data Lakehouse
The Lakehouse architecture enables you to:
- Store raw data in a scalable, cost-effective way.
- Apply schema-on-read to ensure data quality and structure when needed.
- Use powerful query engines for fast analytics on large datasets.
- Integrate seamlessly with machine learning and AI workflows, leveraging the best of both data lakes and data warehouses.
According to McKinsey, “Utilising this merger of Data Lakes and Warehouses provides unique benefits and can prove cost effective, particularly with large data sets and heavy analytical compute patterns. Furthermore, if a client places emphasis on Machine Learning, DLHs are particularly good for use cases that require and prioritise raw data and compute.”
