Businesses today rely heavily on data to inform decisions, predict trends, and optimize operations. However, more data volume and complexity has led to growing pressure to find scalable, cost-effective solutions for data storage while staying within IT budgets. Companies want to handle both structured and unstructured data efficiently, while supporting advanced data analysis and machine learning use cases.
Many teams may be navigating the decision between a traditional data warehouse, a data lake, and the Databricks Data Lakehouse. This blog will explore the differences between data warehouses and a Databricks Lakehouse. In it, we’ll compare data lakes to these two approaches, and discuss how to search your log and telemetry data regardless of its format.
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A data warehouse is a centralized system designed to store data that is structured, typically used for business intelligence (BI) and reporting. Traditional data warehouses rely on a schema-on-write model, where data must conform to a defined structure before being stored. This ensures data quality and consistency— making it easier for businesses to generate accurate reports and analyze historical trends.
One of the major advantages of data warehouses is their ability to manage large amounts of historical data efficiently. As a central repository, they store years of business information, enabling companies to perform data analysis that helps guide decision-making. Data warehouses are optimized for handling structured, predefined queries, making them ideal for traditional BI use cases.
However, the strict structuring of data warehouses can also be one of its biggest limitations. Data warehouses struggle to handle raw data that is semi-structured or unstructured, which has become more common in modern data environments. Additionally, the infrastructure required to maintain a data warehouse, especially as the amount of data grows, can lead to mounting costs.
On the other hand, data lakes are designed to store data in its raw form, whether structured, semi-structured, or unstructured. This means businesses can store any type of data without needing to structure it upfront, offering more flexibility than a traditional warehouse. Data lakes follow a schema-on-read model, allowing data structure to be applied only when data is accessed or processed, which enables organizations to work with a wider variety of data.
One of the core advantages of a data lake is the ability to decouple data storage from compute resources, meaning businesses can scale these independently. Data lakes can support real-time data streams and distributed computation, making them well-suited for big data processing and advanced analytics such as artificial intelligence and machine learning.
However, without the right data management tools, data lakes can turn into a "data swamp," where the lack of organization leads to difficulty in finding and using the most valuable data. Proper data integration and governance practices are crucial to keeping a data lake functional and valuable.
A data lakehouse takes the best features of both data warehouses and data lakes, creating a hybrid architecture that can manage both structured and unstructured data. By storing raw and processed data in a unified environment, data lakehouses provide organizations with the flexibility of a data lake combined with the structured data management capabilities of a warehouse.
One of the central features of a data lakehouse is its support for ACID transactions (Atomicity, Consistency, Isolation, Durability), which ensures the integrity and reliability of stored data. Data lakehouses provide the best of both schema-on-write and schema-on-read approaches and offer the ability to handle both structured and raw data for advanced real-time analytics. The introduction of SQL capabilities makes it accessible to users who are familiar with querying relational databases.
For example, Delta Lake provides a powerful storage layer used within the Databricks Data Lakehouse. Delta lake provides ACID transaction support, ensuring that businesses can maintain data quality and consistency while processing large amounts of data. It also allows for real-time data processing, which is crucial for businesses aiming to perform advanced analytics and machine learning tasks on up-to-date information.
While data lakehouses offer versatility and cost-efficiency, they are still evolving. Organizations need the right tools to effectively manage large-scale, long-term data without the risk of losing valuable insights over time.
Built on the foundation of Apache Spark, the Databricks Data Lakehouse platform empowers customers to efficiently and cost-effectively process, store, manage, and analyze large volumes of enterprise data. With Delta Lake as its foundation, the Databricks Lakehouse allows for real-time analytics, enhanced data consistency, and a unified approach to data storage.
Databricks integrates directly with cloud object storage (e.g. Amazon S3, Google Cloud Storage, or Azure Blob Storage), allowing customers to establish an open data lake for storing structured, unstructured, or semi-structured enterprise data.
Data that enters the customer’s cloud object storage is converted to Delta Tables and stored in Delta Lakes. From there, Databricks customers can manage and catalog the data, configure ETL pipelines to transform or process the data at scale, build data warehouses and data marts inside Delta Lakes, and execute SQL/relational queries on their data.
Additionally, Databricks features Unity Catalog, which brings centralized governance, data integration, and access control across the platform, providing a holistic approach to managing data securely. On top of BI use cases, the platform is also built for data engineering and machine learning, enabling streamlined data pipelines and real-time data processing. These capabilities are essential for any organization looking to leverage their data for advanced analytics.
Databricks is well-known for its powerful capabilities in data engineering, processing, and data science, enabling enterprise teams to efficiently ingest, process, and store large volumes of data. This data can then be operationalized for AI/ML applications or used to build data warehouses for SQL querying. A key advantage is that all these processes can be carried out within the customer’s cloud object storage, eliminating the need to move data to a proprietary storage platform, which would otherwise lead to high data egress fees, increased storage costs, and difficult trade-offs around data retention at scale.
However, when using Databricks for log and event analytics, customers often need to export data to specialized third-party tools for advanced capabilities. This external data transfer can lead to significant egress and storage costs, particularly for organizations handling large volumes of log data. In an effort to manage costs, enterprises may limit the data retention period within these third-party tools, but this approach compromises the effectiveness of long-term use cases, such as analyzing application performance trends or investigating the root causes of security incidents.
ChaosSearch addresses this challenge by simplifying log and event analytics within the Databricks Lakehouse. With ChaosSearch, businesses can analyze security logs, monitor cloud infrastructure, and gain insights into user behavior without transferring data out of the cloud, significantly reducing costs. By keeping data within cloud object storage and allowing for long-term analysis, ChaosSearch enhances the data lakehouse platform’s observability capabilities in a cost-effective and efficient manner.
The choice between data warehouses, data lakes, and data lakehouses depends on the organization's needs, the type of data they work with, and how they plan to use it. BI teams tend to prefer data warehouses because of their ability to manage structured, well-organized data for reporting purposes. Data science and engineering teams often need the flexibility to work with raw data in various forms, making data lakes more suitable for their workflows.
For organizations that require both structured data for reporting and unstructured data for advanced analytics, a data lakehouse is an ideal hybrid solution. By unifying both approaches, data lakehouses provide versatility and scalability, offering the ability to store large amounts of data while also processing it in real time.
From a cost perspective, data warehouses are generally more expensive due to their reliance on structured data and heavy infrastructure. In contrast, data lakes offer more affordable storage but require robust governance to avoid data management challenges. A data lakehouse bridges this gap, combining the cost-efficiency of a data lake with the data quality and control features of a warehouse. Integrating tools like ChaosSearch with the Databricks Data Lakehouse can help teams who need to analyze product data, conduct security threat hunting, or improve overall observability.
Explore more about how Databricks and ChaosSearch can help unlock new insights from your data.