In today’s data-driven world, businesses deal with more data than ever before — and traditional systems like data warehouses and data lakes struggle to keep up.
Enter the data lakehouse, an approach that combines the best features of data lakes and data warehouses to offer faster data processing and more advanced analysis.
As a unified platform for managing all types of data, lakehouse architecture has become increasingly important, and Apache Iceberg is a crucial part of the system because it helps manage large datasets more effectively. Companies looking to get the most out of their data find that adding real-time analytics and AI into data lakehouses can make a big impact. In this article, we’ll explore what makes data lakehouses so powerful, the role of Apache Iceberg in their success and how real-time analytics are changing the way we manage data today.
Let’s start by understanding the challenges of traditional data storage systems, and how these systems have evolved over time.
The data storage evolution
Data warehouses were designed to address issues like data redundancy and inconsistent formats but were expensive to maintain. Over time, they gave way to data lakes, which can handle both structured and unstructured big data. Yet these too had a trade-off: while offering more flexible big data analysis and eliminating silos, they faced challenges with high storage costs. When the data lakehouse emerged combining the best features of both data warehouses and data lakes, they solved problems including data latency, complex data management and high operational costs as they streamlined big data storage and analytics.
The rise of data lakehouses + Iceberg tables
Because they were designed before modern data, advanced analytics and cloud computing, traditional data architectures often struggle to adapt to the demands of these technologies. Traditional systems simply weren’t built to handle massive volumes of data generated by modern applications and devices, leading to scalability and performance issues.
A common problem is the creation of data silos. When different departments or applications manage their data separately, the resulting fragmentation makes it difficult to gain a holistic view of an organization's data — resulting in duplicated efforts and inefficiencies. Moreover, traditional data architectures were not designed to handle the variety of data types prevalent today, including unstructured and semi-structured data.
Real-time processing capabilities are also limited in these systems, hindering the ability to make timely decisions based on up-to-date information. The rigidity of traditional architectures restricts an organization's agility and innovation potential, while high costs associated with hardware, software and maintenance become barriers as data volumes continue to grow.
The concept of data lakehouses emerged as a solution to address the limitations of traditional data architectures by merging the best aspects of data warehouses and data lakes into a unified and cohesive data management solution.
Data lakehouses have evolved from earlier attempts to manage big data, like data lakes built on Apache Hadoop. These early data lakes enjoyed varying degrees of success, with factors like the complexity of Hadoop causing some to fail. Yet they did pave the way for modern data lake architectures, which have since shifted from on-premises Hadoop to running Apache Spark in the cloud.
The flow of data in a data lakehouse
The preceding diagram shows a simple data flow in the lakehouse integrating diverse data types (audio, video, structured, unstructured) into a unified system. Data flows from ingestion to the storage layer, utilizing platforms like Amazon S3, Hadoop HDFS and Google Cloud Storage. It then proceeds to the processing layer, where platforms like Amazon Redshift, Apache Drill, Apache Spark and Delta Lake manage data processing and querying.
Finally, external BI/AI applications like Amazon QuickSight, Tableau, Jupyter and Power BI access and analyze the processed data, providing visualizations and insights. This setup combines the scalability of data lakes with the reliability and performance of data warehouses.
Core features of a data lakehouse
As we’ve noted, a data lakehouse combines the best aspects of data warehouses and data lakes into a unified data management solution — providing a flexible, scalable architecture that allows organizations to store, process and analyze vast amounts of structured and unstructured data. The core features of a data lakehouse include ACID transactions, schema enforcement and governance, business intelligence and machine learning support and open format and API.
ACID transactions
One of the key features of a data lakehouse is its support for ACID transactions. ACID stands for atomicity, consistency, isolation and durability, which are essential properties that ensure data integrity and reliability. These transactions have traditionally been available only in data warehouses, but data lakehouses now bring this capability to data lakes as well.
ACID transactions in a data lakehouse provide several benefits:
- Atomicity. Each transaction is treated as a single unit, and must execute entirely or not at all, preventing data loss and corruption
- Consistency. Tables change in predictable ways during transactions, ensuring data integrity
- Isolation. Concurrent transactions do not interfere with each other, maintaining data consistency
- Durability. Completed transactions are saved and protected against system failures
By implementing ACID transactions, data lakehouses effectively solve issues related to data consistency and reliability — especially in scenarios involving concurrent reads and writes.
Schema enforcement and governance
To maintain data quality and consistency, data lakehouses incorporate powerful schema enforcement and governance features.
- Schema enforcement prevents the accidental upload of low-quality or inconsistent data, ensuring that new data follows the schema of the target table
- Schema evolution allows for automatic addition of new columns, enabling flexibility as data requirements change
- Data governance features include fine-grained access control, auditing capabilities and data quality constraints and monitoring
These features help organizations maintain data integrity, reduce data quality issues and implement effective data governance practices.
BI and ML support
Data lakehouses offer native support for both business intelligence (BI) and machine learning (ML) workloads. This integration allows organizations to use BI tools directly on the data in the lakehouse, eliminating the need for data duplication and ensuring data freshness. They also support advanced analytics — including machine learning and artificial intelligence — on the same platform as traditional BI applications. Finally, they leverage various ML libraries, likeTensorFlow and Spark MLlib, to read open file formats like Parquet and query the metadata layer directly.
This unified approach enables organizations to perform a wide range of analytics tasks, from traditional reporting to advanced data science, on a single platform.
Open format and API
Data lakehouses typically use open file formats and APIs, offering advantages like flexibility, interoperability and cost-efficiency. For example, open formats like Apache Parquet allow for easy integration with various tools and technologies. This means that all data can exist in one location instead of being spread across multiple systems, regardless of file type. As a result, organizations can change metadata layer solutions and consumption layer tools to fit their evolving needs,, without being locked into proprietary formats or tools.
Iceberg tables: A data format revolution
Iceberg tables emerged to address significant challenges in managing large datasets within data lakes, including data management complexity, performance issues and data governance. Traditional data lakes struggled with ensuring data consistency, efficient querying and supporting ACID transactions. Developed by the Apache Software Foundation, Iceberg is an open table format designed to enhance data lake capabilities. Key features of Iceberg include:
- Schema and partition evolution
- Efficient metadata management
- Support for ACID transactions
- Time travel for querying historical data
These features enable better data integrity, faster querying and easier data management.
Iceberg has gained widespread adoption and is now a standard in leading data platforms like Snowflake and Databricks. Snowflake and Databricks have integrated Iceberg into their ecosystems to provide enhanced data lake capabilities, and to ensure compatibility and interoperability across different big data processing engines. Standardizing on Iceberg is helping streamline big data operations, ensuring reliability, efficiency and better data governance in complex data environments — making Iceberg a crucial component in modern data architecture.
The need for a speed layer and analytics in AI
This brings us to the era of AI-driven decision-making, where the need for real-time data processing and analytics has never been more critical. To gain immediate insights and make timely decisions, organizations are using speed layers in their data architectures to enable rapid data ingestion and processing. With the ability to handle high-velocity data streams, these layers ensure that information is current and actionable.
The integration of advanced analytics with speed layers facilitates the deployment of AI models and machine learning algorithms, enhancing predictive accuracy and operational efficiency. As organizations strive to stay competitive and keep up with the demands of modern applications, the ability to process and analyze data in real time becomes a key driver of success.
SingleStore addresses these needs. By introducing bi-directional support for Apache Iceberg, SingleStore provides a seamless data management solution that integrates speed layer capabilities with powerful analytics. This integration allows for real-time querying and analysis of Iceberg data without the need for data duplication, significantly reducing storage overhead.
Because Iceberg supports multiple catalog backends, including Hive Metastore, AWS Glue, Hadoop and database systems via Java Database Connectivity (JDBC), users can select the most suitable backend for their specific data infrastructure needs.
Another recent development that is helping users build faster, more efficient real-time AI applications is SingleStore’s integration with Snowflake. Snowflake users can now utilize SingleStore’s data platform in real time on top of Snowflake data, without shifting their entire database.
Meanwhile, SingleStore's advancements in vector search, full-text search and scalable autoscaling are designed to support how you build and scale intelligent applications. With SingleStore Helios®, users can deploy in their own VPC, combining manageability with control. That makesSingleStore an indispensable tool in harnessing the full potential of AI and real-time analytics in modern data architectures.
Learn more about how you can unlock your Iceberg lakehouse and power intelligent applications here.
