Data lakehouse FAQs & resources

A data lakehouse is a relatively new concept in the world of data management that combines the best of two existing approaches: the data lake and the data warehouse.

Data lake: A quick explanation

A data lake is a centralized repository that allows for the storage of vast amounts of structured, semi-structured, and unstructured data at scale. Unlike a traditional database, a data lake can handle data of varying types and formats, making it an ideal choice for storing raw data that may be processed and analyzed later.

Data warehouse: A quick explanation

A data warehouse, on the other hand, is a structured repository that is optimized for querying and analysis. Data in a data warehouse is organized in a way that allows for efficient querying and analysis, making it ideal for business intelligence applications.

A data lakehouse combines the benefits of both data lakes and data warehouses by allowing for the storage of both raw and processed data in a single, unified system. In a data lakehouse, data is first stored in its raw format in the data lake portion of the system, where it can be easily accessed and processed. Once processed, the data is then moved to the data warehouse portion of the system, where it can be easily queried and analyzed.

The key advantage of a data lakehouse is that it allows for a more streamlined data management process, reducing the need for data movement and duplication. This can lead to improved data quality, faster time-to-insight, and lower costs. Data lakehouses can synergize with a unified data fabric to create a powerful modern data architecture for organizations.

Let’s take a look look at some of the specific benefits a data lakehouse can deliver:

• Unified data management: A data lakehouse allows for the storage of both raw and processed data in a single, unified system. This can simplify data management by reducing the need for data movement and duplication.
• Flexible data storage: With a data lakehouse, data can be stored in its raw format in the data lake portion of the system, and then moved to the data warehouse portion of the system once it has been processed. This provides flexibility in terms of data storage, allowing organizations to store and process data in a way that makes the most sense for their needs. Data lakehouses can be configured and maintained by an organization in their own cloud, or subscribed to as an all-in-one data lakehouse service. 
• Improved data quality: By allowing for the storage of raw data, a data lakehouse can help improve data quality. Raw data can be processed and transformed in a controlled manner, reducing the risk of errors or data inconsistencies.
• Faster time-to-insight: With a data lakehouse, data can be accessed and processed more quickly, leading to faster time-to-insight. This can be especially valuable for organizations that need to make data-driven decisions in real-time.
• Lower costs: By reducing the need for data movement and duplication, a data lakehouse can help lower costs associated with data storage and management. Additionally, because a data lakehouse can handle both structured and unstructured data, organizations may be able to use more cost-effective storage solutions for certain types of data.

The core components of a data lakehouse are designed to work together to provide a unified, flexible, and efficient approach to data management and analytics. 

1. Data lake: As we explained above, the data lake is a centralized repository where raw data is stored in its native format. It can handle structured, semi-structured, and unstructured data, and can scale to handle large volumes of data.
2. Data processing engines: These are the tools and technologies used to process and transform raw data in the data lake. Examples include Apache Spark and Apache Flink.
3. Data warehouse: As we explained above, the data warehouse is a structured repository optimized for querying and analysis. Processed data from the data lake is moved to the data warehouse, where it is organized in a way that allows for efficient querying and analysis.
4. Data management and governance tools: These tools are used to manage the data lakehouse environment, including data ingestion, processing, and movement. They also ensure that data is compliant with regulatory requirements and organizational policies.
5. Data visualization and analytics tools: These tools enable users to explore and analyze data stored in the data warehouse. Examples include Tableau, Power BI, and Looker.
6. Security and access control: Security is a critical component of any data management system, and a data lakehouse is no exception. Data lakehouse security should monitor and control access to the ecosystem so that sensitive data remains protected.

Any industry that generates and processes large amounts of data can benefit from the power of a data lakehouse. Let’s take a look at a few examples:

1. Finance services: The financial services industry generates vast amounts of data on a daily basis, including customer transactions, market data, and regulatory filings. A data lakehouse can help financial institutions efficiently manage and analyze this data, allowing them to make better decisions, reduce risk, and improve compliance.
2. Healthcare: The healthcare industry generates a tremendous amount of data, including electronic health records, medical imaging, and patient-generated data. A data lakehouse can help healthcare organizations more effectively manage and analyze this data, leading to improved patient outcomes, more efficient operations, and lower costs.
3. Retail: Retailers generate a wealth of data on their customers, including purchase history, website behavior, and social media activity. A data lakehouse can help retailers gain deeper insights into customer behavior, optimize their marketing efforts, and improve their supply chain management.
4. Manufacturing: The manufacturing industry generates data from a wide variety of sources, including sensors on production equipment, supply chain data, and customer feedback. A data lakehouse can help manufacturers gain real-time insights into their operations, identify areas for improvement, and optimize their supply chain management.
5. Transportation: The transportation industry generates vast amounts of data on a daily basis, including traffic data, logistics data, and vehicle data. A data lakehouse can help transportation companies improve route planning, reduce fuel consumption, and optimize maintenance schedules.

We have already talked about faster time-to-insight and reduced cost above, but let’s take a look at some example outcomes that a business can use to know that they are using a data lakehouse successfully:

• Improved decision making: By providing access to a wide range of data in a unified, flexible, and efficient manner, a data lakehouse can enable organizations to make more informed decisions. For example, a retailer might use a data lakehouse to analyze customer behavior and optimize their marketing efforts, leading to increased sales and customer satisfaction.
• Improved operational efficiency: By providing real-time insights into operations, a data lakehouse can help organizations to identify inefficiencies and optimize their processes. For example, a manufacturer might use a data lakehouse to monitor production equipment and identify potential maintenance issues before they cause downtime or product defects.
• Better customer experience: By analyzing customer data in a unified manner, a data lakehouse can help organizations understand their customers' needs and preferences, leading to a better customer experience. For example, a healthcare provider might use a data lakehouse to analyze patient data and personalize treatment plans, leading to improved outcomes and patient satisfaction.