Exploring data lakehouses: The next frontier in data management

Key features of a data lakehouse

• Unified storage: Integrates data lake storage with data warehouse processing capabilities.

• Scalability: Handles large volumes of structured and unstructured data.

• Cost-efficiency: Reduces costs by minimizing data duplication and simplifying data management.

• Flexibility: Supports various data formats and processing engines.

Best practices for building a data lakehouse

When you’re building a data lakehouse, there are several best practices to keep in mind. These steps will ensure you’re getting the most value from your architecture while avoiding potential pitfalls.

1. Leverage open standards: Open formats like Apache Iceberg are crucial for ensuring compatibility across different tools and ecosystems. At Cloudera, we’ve integrated Iceberg into our platform to help users manage large-scale data efficiently. By doing so, you can avoid vendor lock-in and enable seamless querying, data storage, and management across clouds​.

2. Prioritize governance and security: Governance is key when handling large datasets, especially in industries like healthcare or finance. Implementing robust governance policies early on ensures that data access is controlled, auditable, and secure. Cloudera Shared Data Experience (SDX) ensures that all data is compliant with your governance policies across different clouds and environments.

3. Optimize for performance: Data lakehouses should be optimized for both batch and streaming workloads. For example, Cloudera’s platform integrates Apache Hive and Apache Impala, enabling fast, SQL-based analytics at scale while ensuring that both structured and unstructured data can be processed in real-time.

4. Use automation: Automating routine tasks such as data ingestion, partitioning, and indexing will help maintain performance while reducing operational overhead. Solutions like Cloudera’s SDX include automated security updates and data tagging, enabling teams to focus on analysis rather than infrastructure management.

5. Embrace a multi-cloud strategy: Building your data lakehouse to operate across multiple clouds (or hybrid environments) ensures you have the flexibility to run workloads wherever they make the most sense. With Cloudera’s open data lakehouse, organizations can deploy in public clouds, private clouds, or a hybrid setup, depending on their needs.

Data lakehouse architecture: A deep dive

To understand why data lakehouses are so powerful, let’s look deeper into their architecture. A typical data lakehouse includes the following components:

1. Data lake: At the core of any lakehouse is the data lake, where raw data is stored in its native format—structured, semi-structured, or unstructured. Unlike traditional databases, the data lake is not optimized for querying but instead focuses on cost-effective, scalable storage.
2. Data processing engines: For transforming and analyzing data, a lakehouse incorporates powerful data processing engines like Apache Spark, Apache Flink, and Apache Hive. These engines handle the heavy lifting when it comes to transforming raw data into something meaningful.
3. Data warehouse layer: Once the data has been processed, it can be moved to the data warehouse layer for fast querying and analysis. In this stage, structured data is organized and indexed for efficient SQL-based queries, ensuring fast reporting and analytics.
4. Governance and security: A robust data lakehouse needs to include tools for governance and security, ensuring data is compliant with internal and external regulations. Cloudera’s SDX ensures that data is not only secure but that lineage, auditing, and access policies are enforced across the platform.

Data lakehouse benefits: Why you should care

If you’re wondering how a data lakehouse can benefit your business, here’s a quick rundown of the most impactful advantages:

• Lower costs: By storing data in its raw format and only structuring it when necessary, a lakehouse reduces the need for expensive ETL (extract, transform, load) processes. This results in lower storage costs and less data duplication.

• Faster insights: Since data doesn’t have to be moved between platforms, the time-to-insight is drastically reduced. This is particularly important in industries that rely on real-time data for decision making.

• Unified data management: A data lakehouse allows businesses to manage structured and unstructured data in a single platform, streamlining operations and improving overall data quality.

• Enhanced data governance: With built-in security and compliance features, you can ensure that your data is protected and that your business complies with data regulations.

Data lakehouse vs. data lake vs. data warehouse

So, why go through the trouble of setting up a data lakehouse when data lakes and warehouses already exist? Let’s break down the differences:

• Data lake: Offers massive scalability and flexibility for storing raw, unstructured data. However, it lacks the querying power and governance features of a data warehouse.

• Data warehouse: Optimized for structured data and fast SQL queries, data warehouses excel in business intelligence and analytics. But they struggle with unstructured data and scale.

• Data lakehouse: Combines the scalability and flexibility of a data lake with the performance and governance of a data warehouse, offering the best of both worlds.

For businesses that rely heavily on real-time analytics, machine learning, and AI, a data lakehouse offers a much-needed blend of performance, scalability, and flexibility.

Data lakehouse use cases

To see the true potential of a data lakehouse, let's explore some real-world use cases and scenarios.

• Financial services: Banks need to process transaction data, compliance reports, and customer information. With a lakehouse, they can perform fraud detection in real-time while ensuring compliance with regulatory standards.
• Healthcare: Hospitals can use lakehouses to integrate unstructured data like patient records, medical images, and structured data like lab results to provide a 360-degree view of patient health.
• Retail: Retail companies often deal with massive amounts of structured (sales data) and unstructured data (sentiment data such as customer feedback and social media). A data lakehouse allows them to process this data in real-time, offering faster insights into consumer trends and preferences.

The role of data fabric in data lakehouses

While the lakehouse provides a strong foundation for data management, combining it with a data fabric can enhance its capabilities even further. A data fabric ensures that data is accessible, governed, and secure regardless of its environment—public cloud, on-premises, or hybrid setups. For teams like data science, data engineering, and business analysts, this integration provides a more seamless way to access and process data in real-time.

Within the Cloudera platform, SDX serves as a data fabric that overlays the data lakehouse, ensuring a consistent data governance and security model across all environments. This combination enables businesses to build more resilient, scalable data solutions.

How does Cloudera leverage data lakehouses?

At Cloudera, we’ve fully embraced the data lakehouse as the cornerstone of our modern data platform. We built our platform with the flexibility and performance of a lakehouse in mind. By integrating Apache Iceberg and SDX, our platform allows you to run analytics, machine learning, and data science workloads from one platform, without needing to move or replicate data across different systems.

One standout feature of our lakehouse approach is its focus on open standards. By avoiding proprietary formats and tools, we ensure that you can deploy your data lakehouse in any environment—whether it’s AWS, Azure, Google Cloud Platform, or on premises.