Data Lakehouses Explained
Charles Mahler /
Dec 01, 2023
The big data landscape is always changing to solve existing problems and continues to push the boundaries of performance and scale. Data lakehouses are a new architectural pattern that is rapidly gaining popularity by solving a variety of problems seen with previous solutions like data warehouses and data lakes. In this article, you will learn the following:
- What a data lakehouse is
- The key features of a data lakehouse
- The benefits of data lakehouses
- An overview of data lakehouse architectural components
What is a data lakehouse?
A data lakehouse is a data storage architecture that combines the scalability and diverse data storage capabilities of a data lake with the performance and structure of a data warehouse.
Data lakehouses allow organizations to store structured, semi-structured, and unstructured data in its raw form while also providing tools for things like data governance, security, and query optimization in a single platform. Data lakehouses provide the best of both worlds without needing to maintain separate systems.
Key features of data lakehouses
Data lakehouses solve several problems that cannot be addressed by data lakes or data warehouses alone. Let’s take a look at some of the most valuable features of data lakehouses.
Nearly unlimited scale
In theory, by building on distributed object storage solutions, the available hardware is the only factor limiting a data lakehouse when it comes to storage. From a practical perspective, this means you can store as much data as you want and not have to worry about any technical issues beyond the cost of storage.
Separation of compute and storage
Data lakehouses typically separate storage and compute processes, allowing each to scale independently. This means you can store as much data as needed without worrying about compute resources, and scale up compute power as needed without paying for additional storage.
ACID transaction support
The ability to execute ACID transactions is one of the key features that separates data lakehouses from data lakes. When multiple users are reading and writing data at the same time, having guarantees around consistency and durability makes life easier for your developers and analysts.
Data governance and management
Data lakehouses provide a number of features for managing your data, including snapshots and time travel to see the history of stored objects and the ability to roll back changes. For data governance and security, fine-grained access control and auditing are available.
Benefits of using a data lakehouse
Real-time analytics capabilities
Data lakehouses can support real-time analytics use cases that traditional data lakes and data warehouses struggle with. This is possible due to several factors. Data lakehouses intelligently move data from slower object storage to RAM-based queries, which allows for faster query responses. Next, they support advanced query engines like Spark and Presto, which perform distributed and vectorized processing to optimize queries. Finally, data lakehouses support streaming data ingestion, which mitigates analyzing stale data.
Another benefit of data lakehouses is that they can reduce costs for organizations by streamlining and optimizing their data management practices. One way a data lakehouse saves money is by eliminating the need for multiple copies of data across different systems.
In traditional setups, data often resides in silos, duplicated in both data lakes for raw storage and data warehouses for structured analysis. This duplication increases storage costs and complicates data governance and consistency. By consolidating data into a single, unified architecture, data lakehouses reduce the need for redundant data copies.
Data lakehouses also reduce costs by reducing bandwidth usage. Because less data is flowing between different systems—and by having optimized query performance—this reduces the amount of processing power needed for queries.
Simplified architecture and unified data management
Another benefit of data lakehouses is how they can simplify a business’s data infrastructure by creating a unified architecture. Instead of maintaining several different systems connected by complicated data processing pipelines, a single platform where raw and processed data can coexist simplifies unnecessary complexity.
Data lakehouse architecture overview
In this section, you will learn about the components that make up the architecture of a data lakehouse in layers. Later, we will look at some of the tools available for building out these architecture components.
The first architecture layer is how data is collected from different sources and transferred to storage in the data lakehouse. This involves processes such as data transformation and validation before storage. Most data lakehouses support streaming data ingestion to enable real-time analytics.
The storage layer is where different types of data being ingested are stored using persistent file formats like Parquet or ORC.
This layer is what separates a data lakehouse from data lakes and data warehouses. The metadata layer is a unified record of information for every object stored in the data lakehouse. This metadata enables ACID transactions, indexing and caching for faster queries, data governance, auditing, and defined schemas.
Data consumption layer
The final layer makes data available for end users to consume. This can involve integrations with data visualization and analysis tools for non-technical users via API. More advanced users, like machine learning engineers, can directly access the underlying Parquet files.
Data lakehouse challenges
Data lakehouses aren’t perfect and do come with some potential challenges. Here are some of the most common problems you may run into:
- Implementation complexity - Creating a data lakehouse requires technical expertise that an organization may not have internally. Integrating the data lakehouse with your existing systems can be another implementation problem.
- Data governance and security - While data lakehouses provide better governance than data lakes, setting up and maintaining proper governance, compliance, and data quality processes can be daunting, especially with the sheer volume of data.
- Vendor lock-in - While most data lakes focus on open source storage formats, there can still be a risk of being locked into a platform if you choose a cloud-based service that provides proprietary features and integrations.
- Variable query performance - Data lakehouses can vary in performance for different types of queries. Handling high numbers of concurrent queries can also be a problem, particularly if a handful of complex queries slow down other simple queries.
Data lakehouse tools
There are several options for getting started with data lakehouses. This includes opting for a pre-built service or building your own using open source tools. In this section, we’ll look at the main categories of tools and some popular tools within those categories.
- Data ingestion - Data ingestion tools need to be able to handle a large volume of incoming data efficiently and make it easy to integrate with different data sources. Examples are Telegraf and Apache Kafka.
- Data storage - Available tools for building a data lakehouse include object storage tools like AWS S3, MinIO, and Google Cloud Storage.
- Data processing - Data processing tools commonly used with data lakehouses include Apache Spark and Apache Flink. These tools allow you to transform and manipulate data as needed for your workload.
- Data management - Data management tools extend the underlying storage layer to provide the capabilities you expect from a data lakehouse. Examples include Delta Lake and Apache Hudi.
Future data lakehouse trends
The data lakehouse architecture is still relatively new, and as a result, is rapidly evolving. Here are some future trends in terms of features and functionality that you should look for in data lakehouses moving forward:
- Adoption for MLOps workloads - Data lakehouses are a good fit for data science teams to adopt MLOps best practices with minimal overhead. Data lakehouses support much of the functionality needed for MLOps without requiring a dedicated solution, which makes life easier for machine learning specialists.
- Automated performance optimization - Rather than manually tuning things like indexes and storage patterns to improve query performance, many data lakehouses will automatically optimize queries and tune performance based on workload patterns.
- Improved semantic layer - A challenge of data lakehouses is making the vast amount of data accessible and understandable to non-technical users. Improvements in the semantic layer of data lakehouses, combined with tools like LLMs, will allow users to query and analyze their data using natural language.
Data lakehouse FAQs
How does a data lakehouse work?
A data lakehouse works by first ingesting data from a variety of sources. This includes traditional databases, real-time stream data from IoT devices, application logs, and more. The lakehouse ingests this data, whether structured, semi-structured, or unstructured, through various mechanisms, such as batch uploads or streaming pipelines, depending on the source and nature of the data.
Once inside the lakehouse, the raw data is stored in its native format, typically in a distributed file system or object storage like AWS S3 or MinIO. Metadata management tools catalog the data and maintain its lineage, enabling efficient discovery and access. Data lakehouses often employ schema-on-read approaches, where data is only structured and transformed at the time of query or analysis.
How is a data lakehouse different from a data lake?
The key difference between data lakes and data lakehouses is the support for structured data and performance optimization seen in data warehouses. Data lakehouses still store the raw and unformatted data but add a metadata layer and governance model on top to ensure better data quality, which allows for better performance as a result.
Data lakehouse vs data warehouse
The primary difference between data lakehouses and data warehouses is data lakehouse’s ability to store unstructured and semi-structured data. Data stored in a data warehouse typically requires an ETL process to process the data before storage to ensure data is formatted correctly.