Spending on worldwide public cloud services is expected to reach US$219 billion by 2027, according to new data from IDC. This surge is fuelling innovation in emerging technologies such as generative AI (GenAI). With organisations generating petabytes of data, traditional storage systems are struggling under the pressure of scalability, cost and performance limitations.

To stay competitive, businesses must adopt modern data architectures that can handle the exponential data growth, with agility and cost-effectiveness. Frameworks like the lakehouse architecture offer a solution by merging the performance of data warehouses with the scalability of data lakes, providing a future-proof approach to growing data demands. Embracing these modern practices is essential for driving data excellence.

FreshBytes: Modernising Data Storage with Lakehouses

Continuing with the journey of FreshBytes Retail Group—a fictional retailer specialising in organic food products; mirroring real-world experiences of HorizonX. FreshBytes serves as an example of how businesses can adopt a lakehouse architecture to overcome common data challenges, facilitating scalable, cost-efficient solutions for future growth.

The Challenge: Overloaded Data Storage

Like many rapidly growing companies, FreshBytes faced several challenges with traditional storage solutions:

• Explosive data growth: The company saw a dramatic increase in data generation, overwhelming its current storage capabilities. In just two years, FreshBytes was generating over 10 petabytes of data annually—ten times more than before.

• Scalability limitations: Existing data warehouse struggled to process queries on datasets exceeding 1 petabyte, leading to timeouts and system crashes.

• Cost concerns: Rising data volumes is driving storage costs higher, with FreshBytes expecting expenses to exceed AUD 5 million annually if no changes are made.

• Data accessibility: As data grew, teams struggled to access and analyse the information they needed quickly. Average query times had increased from seconds to minutes, and in some cases, hours.

To meet these challenges, FreshBytes would require a scalable solution that could handle their current data volumes and accommodate future growth without breaking the bank.

The Solution: Lakehouse Architecture for Scalable Data Management

By adopting a lakehouse architecture, FreshBytes will combine the flexibility of data lakes with the structured performance of data warehouses.  

Key components include:

• Cloud-Based Storage Foundation: FreshBytes will use Google Cloud Storage (GCS) as the foundation of its lakehouse architecture, providing the scalability needed to store large volumes of raw data.

• Data Processing and Transformation: To address large-scale data processing, FreshBytes will deploy Apache Spark clusters within Kubernetes. This setup will allow for Extract, Transform, Load (ETL) operations directly on the data lake, streamlining processes and reducing costs through autoscaling.

• Iceberg Table Format¹: FreshBytes will utilise the Iceberg table format which allows for efficient querying and management of large datasets by handling complex data structures and enabling features like time travel² and schema evolution³.

• Metadata Management: FreshBytes will implement a native metadata layer, allowing them to manage schema evolution, ensure ACID⁴ transactions and implement time travel capabilities

Benefits of the Lakehouse Architecture

Through the implementation of the lakehouse architecture, FreshBytes will overcome the limitations of traditional data storage systems, leading to:

• Improved scalability: The new system will handle 10 - 100 Petabytes, with effective storage policies to handle costs, and the ability to scale further as needed.

• Enhanced cost-effectiveness: Storage costs will be reduced by up to 60% reduction in cost compared to traditional data warehouse solutions when handling similar data volumes.

• Faster time-to-insight: Average query times (time to derive a data driven decision i.e analytical query) will reduce from minutes to seconds, with complex queries completing in under 5 minutes compared to hours previously.

• Greater flexibility: The system will support diverse data types, including structured, semi-structured and unstructured data, enabling new use cases in machine learning and AI.

• Improved data discovery: This integration is expected to enhance data discovery across the organisation, leading to a 40% reduction in time spent searching for relevant datasets

• Reduced data reorganisation time: FreshBytes anticipates that Iceberg's partition evolution feature will decrease their data reorganisation time by 60% compared to their previous solution.

• Minimised data inconsistencies: The company expects to reduce data inconsistencies by 85% within the first month of implementation.

For companies facing similar challenges, FreshBytes' scenario offers a glimpse of the potential benefits and considerations of implementing a lakehouse architecture. As an organisation grows, scalable and flexible solutions like these become critical for businesses aiming to succeed in the digital age.

In our next post, we will explore the challenges FreshBytes encountered while striving to gain valuable data insights. We will discuss essential strategies for managing data insights and highlight the tangible benefits of lakehouse architecture, demonstrating how it can transform raw data into actionable insights that improve customer experience and boost business performance.

Is your organisation facing similar data storage challenges? Contact HorizonX to discover how we can help you unlock the full potential of your data and drive your business forward.

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Footnote:

1. Iceberg Table Format:The Iceberg Table Format is a high-performance, open table format designed for managing large-scale analytic datasets. It offers advanced features such as efficient data querying, schema evolution, and optimised partition management. By leveraging the Iceberg table format, FreshBytes optimises data organisation and retrieval processes, enabling faster and more flexible data queries. This enhancement is essential for handling growing data volumes and complex analytical requirements, ensuring that FreshBytes can efficiently access and analyse their data to support informed business decisions.
2. Schema Evolution: Schema evolution enables FreshBytes to adapt its data schema over time without disrupting existing data operations. This flexibility is crucial as business requirements change, allowing the company to add new fields, modify existing ones, or alter data structures seamlessly. ‍
3. ACID: stands for Atomicity, Consistency, Isolation, and Durability, which are fundamental properties that guarantee reliable and secure processing of database transactions. By ensuring ACID transactions, FreshBytes enhances the reliability and integrity of its data operations. This is crucial for maintaining accurate and consistent data, which is essential for generating meaningful and trustworthy business insights. ‍
4. Time Travel Capabilities: Time travel allows FreshBytes to query and analyse data as it existed at any specific point in the past. This feature is invaluable for auditing, debugging, and historical data analysis, enabling the company to track changes, revert to previous states if necessary, and gain insights from historical data without impacting current operations.

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