In today’s enterprise world, big data drives decision-making, customer engagement, and innovation. As the data landscape evolves, so do the technologies that power it. The traditional extract, transform, load (ETL) approach is being challenged by more modern, cloud-native solutions that promise greater efficiency, scalability, and cost savings. In this context, technologies like Apache Spark and Apache Iceberg are gaining prominence, especially as companies explore the Lakehouse model as an alternative to conventional data warehouses.
So, is the future of big data management in ‘icehouses’ – leveraging Spark and Iceberg to redefine how we manage and process massive datasets?
From ETL to ELT: A Paradigm Shift
The classic ETL model involves extracting data from operational systems, transforming it using middle-tier software like Informatica, and then loading it into a destination, typically a database such as Oracle. This approach has been effective for years, but it also introduces complexities. The middle-tier transformation layer requires dedicated infrastructure, and because transformations occur before data loading, flexibility is limited.
Enter extract, load, transform (ELT). In this model, data is loaded directly into platforms like Snowflake or Databricks before transformation. This shift provides several advantages:
- Cost Efficiency: Loading data into Snowflake is free (ingress costs are negligible), while egress (pulling data out) remains costly. This creates a cost-effective way to store and manage large datasets.
- Simplified Data Processing: Instead of relying on separate ETL tools, platforms like Spark handle large-scale data processing natively. This reduces dependencies on third-party transformation layers and ensures better integration with cloud-native ecosystems.
- Scalability: ELT on cloud platforms like Snowflake allows for greater flexibility and scalability, as businesses no longer need to pre-transform data, making real-time analytics more accessible.
Iceberg: The Evolution of Data Lakes
As big data use cases evolved, businesses transitioned from structured data to semi-structured and unstructured formats. The challenge was how to efficiently manage continuously updating massive datasets, especially when traditional file systems were not designed for dynamic workloads.
Databricks introduced Delta Lake – a layer that sits on top of object-based storage, enabling updates without directly modifying the file. This concept evolved into what we now call the Lakehouse model, a combination of data warehouse reliability and data lake scalability.
Meanwhile, Apache Iceberg emerged as a new solution, offering the flexibility of external tables with the robustness of Snowflake’s architecture. Iceberg effectively acts as a metadata layer, simplifying data updates, supporting time travel, and reducing storage inefficiencies.
Why Iceberg is the Future
Iceberg addresses many limitations of traditional data lakes:
- Handling Large, Continuous Data: Iceberg shines in scenarios where data is continuously updated, such as financial transactions or Internet of Things (IoT) data streams.
- Optimized Data Processing: By merging smaller files and improving metadata management, Iceberg significantly reduces the cost of processing massive datasets.
- Flexibility: Iceberg enables Snowflake and other platforms to create external tables, providing seamless integration between cloud-native storage and advanced data processing capabilities.
In this sense, Iceberg represents the ‘best of both worlds,’ blending the scalability of data lakes with the query performance and management features of data warehouses.
The Cost Dynamic: Storage vs. Processing
One of the key considerations in any big data architecture is cost management. While storage is becoming increasingly cheaper, data processing remains expensive. With the rise of Iceberg and Spark, companies can process data more efficiently without constantly moving it between systems. Iceberg allows for effective file management – such as breaking large files into smaller chunks – reducing storage overhead without compromise performance.
Additionally, Iceberg enables time travel, allowing organizations to access previous states of the data for auditing, rollback, or historical analysis, further enhancing the value of their data platforms.
Real-world Use Case: Migrating to the Cloud
To illustrate the power of this technology stack, consider a recent project we executed for a large gaming and entertainment company. We supported a strategic migration of their on-premise big data systems to Azure for enhanced scalability and cost efficiency. We utilized Azure Data Lake and SQL for storage, combined with Apache Spark for real-time processing and analytics. As a result, the client was able to leverage Power Business Intelligence (BI) dashboards for granular insights, significantly reducing costs on processing while effortlessly scaling as their data grew.
The success of this migration showcased the practical benefits of transitioning to a modern ELT model, with Spark handling the processing and cloud-native solutions like Azure and Iceberg managing storage and metadata.
Conclusion: The Icehouse of Tomorrow
As enterprises move toward a future where data drives every aspect of business, technologies like Apache Spark and Iceberg will play a pivotal role. These solutions provide a scalable, efficient, and flexible approach to big data, allowing organizations to achieve the best of both worlds: powerful, real-time data processing and cost-effective cloud storage.
The transition to ‘icehouses’ – the convergence of Lakehouse models with Iceberg’s capabilities – represents a significant step in the evolution of data architectures. The question for CXOs is not whether to adopt these technologies but how quickly they can be implemented to gain a competitive edge.
As you plan your data strategy, consider whether the future of your big data might just be housed in an ‘icehouse.’
Contributors
In today’s enterprise world, big data drives decision-making, customer engagement, and innovation. As the data landscape evolves, so do the technologies that power it. The traditional extract, transform, load (ETL) approach is being challenged by more modern, cloud-native solutions that promise greater efficiency, scalability, and cost savings. In this context, technologies like Apache Spark and Apache Iceberg are gaining prominence, especially as companies explore the Lakehouse model as an alternative to conventional data warehouses.
So, is the future of big data management in ‘icehouses’ – leveraging Spark and Iceberg to redefine how we manage and process massive datasets?
From ETL to ELT: A Paradigm Shift
The classic ETL model involves extracting data from operational systems, transforming it using middle-tier software like Informatica, and then loading it into a destination, typically a database such as Oracle. This approach has been effective for years, but it also introduces complexities. The middle-tier transformation layer requires dedicated infrastructure, and because transformations occur before data loading, flexibility is limited.
Enter extract, load, transform (ELT). In this model, data is loaded directly into platforms like Snowflake or Databricks before transformation. This shift provides several advantages:
- Cost Efficiency: Loading data into Snowflake is free (ingress costs are negligible), while egress (pulling data out) remains costly. This creates a cost-effective way to store and manage large datasets.
- Simplified Data Processing: Instead of relying on separate ETL tools, platforms like Spark handle large-scale data processing natively. This reduces dependencies on third-party transformation layers and ensures better integration with cloud-native ecosystems.
- Scalability: ELT on cloud platforms like Snowflake allows for greater flexibility and scalability, as businesses no longer need to pre-transform data, making real-time analytics more accessible.
Iceberg: The Evolution of Data Lakes
As big data use cases evolved, businesses transitioned from structured data to semi-structured and unstructured formats. The challenge was how to efficiently manage continuously updating massive datasets, especially when traditional file systems were not designed for dynamic workloads.
Databricks introduced Delta Lake – a layer that sits on top of object-based storage, enabling updates without directly modifying the file. This concept evolved into what we now call the Lakehouse model, a combination of data warehouse reliability and data lake scalability.
Meanwhile, Apache Iceberg emerged as a new solution, offering the flexibility of external tables with the robustness of Snowflake’s architecture. Iceberg effectively acts as a metadata layer, simplifying data updates, supporting time travel, and reducing storage inefficiencies.
Why Iceberg is the Future
Iceberg addresses many limitations of traditional data lakes:
- Handling Large, Continuous Data: Iceberg shines in scenarios where data is continuously updated, such as financial transactions or Internet of Things (IoT) data streams.
- Optimized Data Processing: By merging smaller files and improving metadata management, Iceberg significantly reduces the cost of processing massive datasets.
- Flexibility: Iceberg enables Snowflake and other platforms to create external tables, providing seamless integration between cloud-native storage and advanced data processing capabilities.
In this sense, Iceberg represents the ‘best of both worlds,’ blending the scalability of data lakes with the query performance and management features of data warehouses.
The Cost Dynamic: Storage vs. Processing
One of the key considerations in any big data architecture is cost management. While storage is becoming increasingly cheaper, data processing remains expensive. With the rise of Iceberg and Spark, companies can process data more efficiently without constantly moving it between systems. Iceberg allows for effective file management – such as breaking large files into smaller chunks – reducing storage overhead without compromise performance.
Additionally, Iceberg enables time travel, allowing organizations to access previous states of the data for auditing, rollback, or historical analysis, further enhancing the value of their data platforms.
Real-world Use Case: Migrating to the Cloud
To illustrate the power of this technology stack, consider a recent project we executed for a large gaming and entertainment company. We supported a strategic migration of their on-premise big data systems to Azure for enhanced scalability and cost efficiency. We utilized Azure Data Lake and SQL for storage, combined with Apache Spark for real-time processing and analytics. As a result, the client was able to leverage Power Business Intelligence (BI) dashboards for granular insights, significantly reducing costs on processing while effortlessly scaling as their data grew.
The success of this migration showcased the practical benefits of transitioning to a modern ELT model, with Spark handling the processing and cloud-native solutions like Azure and Iceberg managing storage and metadata.
Conclusion: The Icehouse of Tomorrow
As enterprises move toward a future where data drives every aspect of business, technologies like Apache Spark and Iceberg will play a pivotal role. These solutions provide a scalable, efficient, and flexible approach to big data, allowing organizations to achieve the best of both worlds: powerful, real-time data processing and cost-effective cloud storage.
The transition to ‘icehouses’ – the convergence of Lakehouse models with Iceberg’s capabilities – represents a significant step in the evolution of data architectures. The question for CXOs is not whether to adopt these technologies but how quickly they can be implemented to gain a competitive edge.
As you plan your data strategy, consider whether the future of your big data might just be housed in an ‘icehouse.’
Contributors