Monday, June 17, 2024

What is a Data Lakehouse? Definition, Benefits and Features

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A data lakehouse is a hybrid of a data warehouse and a data lake, combining the best of both data platform models into a unified data management solution to store and facilitate advanced analytics of both structured and unstructured data. More than a simple storage system, a data lakehouse is a comprehensive data platform that supports all stages of data processing, from ingestion and storage to processing and analytics. This article provides a high level overview of data lakehouses, their key features and benefits, and the architecture behind them.

Data Lakehouses vs. Data Lakes vs. Data Warehouses

A data lakehouse is a new data architecture that combines the best features of data lakes and data warehouses into a single, centralized platform to store and handle data. Designed to address the weaknesses of the two, this comprehensive data platform can perform advanced analytics and generate valuable real-time insights by supporting the entire lifecycle of data processing for continuous streams of real-time and historical data.

Data lakes are vast repositories of raw data in its native format. Primarily designed for the storage of unstructured data—data generated by Internet of Things (IoT) devices, social media posts, and log files, for example—they are well-suited to storing store large volumes of data at a relatively low cost, but lack the capacity to process and analyze that data. Data stored in lakes tends to be disorganized, and because they require the use of external tools and techniques to support processing, they’re not well-suited for business intelligence (BI) applications and can lead to data stagnancy issues—sometimes referred to as “data swamps”—over time.

Data warehouses, on the other hand, are designed for the storage, processing, and analysis of large volumes of data—primarily structured data like information from customer relationship management systems (CRMs) and financial records. They excel at handling structured data, but are generally not as useful for unstructured data formats. They’re also inefficient and expensive for organizations with constantly expanding data volumes.

Data lakehouses bridge the gap by combining the storage capabilities of a data lake with the processing and analytics capabilities of a data warehouse. A data lakehouse can store, process, and analyze both structured and unstructured data in a single platform.

Learn more about data architecture vs. data modeling.

Key Features of a Data Lakehouse

Data lakehouses can facilitate high-speed data queries and other data processing efforts, consolidating data from multiple sources and in multiple formats in a single, flexible solution. Here are some of the key features that set them apart from other storage solutions:

  • Unified data architecture. Data lakehouses provide a unified and centralized platform for the storage, processing, and analysis of both structured and unstructured data.
  • Scalability and flexibility. Due to data lakehouses’ ability to handle vast volumes of data, they’re also capable of exceptional scalability, enabling businesses to increase their data capacity based on demand.
  • Advanced analytics support. Data lakehouses can facilitate advanced analytics, including machine learning and artificial intelligence, on stored data.

Benefits of a Data Lakehouse for Business Operations

Why choose a data lakehouse over a data lake or data warehouse? They can be used across a wide range of industries to help enterprises meet their data processing and business intelligence needs. In the healthcare sector, for example, data lakehouses are used to store and keep track of patient data, enabling healthcare providers to deliver personalized care. In the finance industry, data lakehouses are used to manage and analyze transaction data, helping financial institutions detect fraudulent activities.

Here are few of the key benefits of data lakehouses for enterprise use.

Simplified Data Management

In traditional data warehouses, data needs to be transformed and loaded before analysis, while data lakes are raw and lack schema enforcement. Data lakehouses, on the other hand, enable businesses to ingest and store both types of data in the same location, simplifying the process of needing to manage multiple storage technologies. This enables businesses to focus on data-driven decisions more effectively.

Improved Data Accessibility and Collaboration

Data lakehouses facilitate data accessibility and collaboration across the various departments of an organization thanks to centralizing the repository of the enterprise data. This lets employees access a much wider range of data sets without the need for complex data request procedures or access permissions. This also enables teams to work together more efficiently by letting analysts, data scientists, and business users collaborate on data exploration, analysis, and visualization during the decision-making process.

Scalability and Cost Efficiency

When combined with cloud-based storage and cloud computing, data lakehouses allow businesses to easily scale their data infrastructure based on demand. As the volume of data grows, the architecture can expand to handle the influx of data with minimum disruptions or last-minute hardware investments. Most data lakehouse providers offer pay-as-you-go models for cost efficiency, as businesses only pay for the resources they use. This eliminates the need for expensive, upfront infrastructure costs, making it suitable for businesses of all sizes.

Real-time Analytics and Processing

Using data lakehouses, organizations can perform real-time data analytics and processing, generating immediate insights and responses to changing market conditions and customer purchasing behaviors and trends. This capability is particularly important for industries that rely on up-to-date information, such as retail, finance, and telecommunications. By harnessing real-time data, they can better optimize operations, personalize customer experiences, and gain a competitive edge in the dynamic market landscape.

Data Lakehouse Architecture

Building a data lakehouse structure from scratch can be a complicated task. For many enterprises, paying for the service from a vendor will be a better option. Databricks is one of the better known data lakehouse providers; others include Amazon Web Services (AWS), iomete, Oracle, and Google. There are also hybrid solutions that allow more control over the data lakehouse structure while working alongside a cloud provider for easier implementation.

At a high level, five levels comprise data lakehouses:

  • Ingestion. This layer uses a variety of protocols to connect to disparate external sources, pull in the data, and route it to the storage layer.
  • Storage. This layer keeps all the data (both structured and unstructured) in affordable object storage, where it can be accessed directly by client tools.
  • Metadata. This layer deploys a unified catalog to provide information about all the data in the storage layer, making it possible to implement data management.
  • Application Programming Interface (API). This layer serves as a host layer for the APIs that are used to analyze and process the data.
  • Consumption. This layer is where client applications perform BI, visualization, and other tasks on the data.

While each layer is essential to the architecture, the metadata layer is the one that makes data lakehouses more useful than either data lakes or data warehouses. It allows users to apply data warehouse schemas and auditing directly to the data, facilitating governance and improving data integrity.

Bottom Line: The Future of Data Lakehouses

Data lakehouses are a relatively new architecture, but because they provide a single point of access to an organization’s entire data stores, their future looks promising. As businesses continue to generate vast amounts of data, the need for a unified data platform like a data lakehouse will only increase.

Enterprises already using data lakes will find the shift to a data lakehouse can provide better data processing capabilities while creating cost efficiencies over a data warehouse. Opting for a single platform can also cut down on costs and redundancy issues caused by using multiple data storage solutions. A data lakehouse can also support better BI and analytics and improve data integrity and security.

Advancements in technologies like machine learning and artificial intelligence will only increase the capabilities of data lakehouses, and as they become more intelligent and better able to automate data processing and analysis, they’ll become more useful enterprises hungry for more insights to give them a competitive advantage.

Read next: Data Management: Types and Challenges

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