Data repositories are the key foundational components of an organization’s data infrastructure. A data repository is a collection of large databases that collect, store, and manage enterprise data in raw and/or processed formats, and make it available for further analysis.
Options such as data marts, data warehouses, data lakes, and data lakehouses are available for enterprises to store and manage their data. Choosing the best solution depends on the business goals, data types and sources, organizational skillset, and budget. Let us look at the pros and cons of each type and how to choose the right solution for your business.
A data warehouse (DW), also called enterprise data warehouse (EDW), is a central repository that integrates data from multiple disparate sources across the enterprise into a common schema and format. DWs became popular in the 1990s addressing a key challenge of the time, namely, siloed data distributed across multiple departments resulting in delayed and impaired business decision-making.
Data warehouses focus on delivering report generation and business intelligence and rely predominantly on structured source data. The source data is converted to an optimal structure using the Extract-Transform-Load (ETL) process. This three-stage process involves extracting the data from the sources, perform the required cleaning and transformation operations for easy insight generation and report creation, and load the integrated data into a DW.
The data marts extract subsets of data from a DW catering to distinct needs of operational departments such as the marketing, sales, finance, or human resources. Data marts, working with DWs, help improve the speed of insights and reduce the computational costs.
Here is a sample architecture of a data warehouse.
In the late 2000s, data generation and collection began to grow exponentially with the extensive use of internet technologies and the rise in data collection through increased adoption of the Internet of Things (IoTs) and the Industrial Internet of Things (IIoTs). Access to increased computational power, a significant drop in storage costs, and maturing of cloud platforms have made the collection and management of data much more economical. However, most of the accumulated data is unstructured, and the data warehouses are ill-equipped to extract meaningful business value because of their reliance on curated and structured data. Moreover, the Artificial Intelligence (AI) and Machine Intelligence (ML) technologies in the 2010s relied on the raw data to create intelligent models, limiting the use of data warehouses.
The challenges of the new data era forced a rethinking of the data architecture, and the data lake paradigm was born. Unlike the data warehouse, a data lake is an unstructured, distributed repository that stores data in its native format and offers excellent scaling to petabytes. Instead of an ETL process, data flows using an ELT pipeline with the source data extracted and loaded into a data lake before any transformations. Data lakes typically use a schema-on-read approach, meaning that the data gets structured and transformed when it is accessed, rather than when it is loaded into the repository.
A data lake distinguishes itself from a data warehouse in the following ways:
- Variety: A data lake supports structured, semi-structured, and unstructured data. A data lake is thus the ideal solution for the storage of documents, text files, audio and video files, images, and formats such as JSON and XML.
- Cost: A data lake is a cost-effective solution for large volumes of data that are orders of magnitude higher than what can be handled by a data warehouse.
- Adaptability: Data lakes are more adaptable than data warehouses. Since the business goals and formats need not be defined while designing a data lake, businesses can collect and store raw data for a business use case that can be defined and leveraged later.
- Data Science: Since data lakes store data in raw formats, they enable data scientists to explore the data and develop valuable proof-of-concept studies.
A sample architecture diagram of a data lake is shown here.
While the data lake addresses many of the limitations of a data warehouse, it suffers from a few drawbacks. Data lakes are good at storing data in multiple formats, but the security requirements and quality controls are not as robust as many enterprises need. This can turn a data lake turning into a data swamp with no consistent structure, making it increasingly hard to find and use data for analysis. Enforcing data governance can be time-consuming and costly if not done early in the design process.
A new, hybrid architecture of data lakehouse is gaining popularity in the past few years in enterprises by combining the strengths of data management and governance of a data warehouse with the flexibility and scalability of data lakes.
- Data lakehouses store structured, semi-structured, and unstructured data sources like a data lake while providing tools for schema definition, data transformation, and data management.
- Data lakehouses support schema-on-read and schema-on-write approaches so that data can be transformed and structured before or after it is stored based on user needs.
- Data lakehouses are well suited to support the ingestion of streaming data from IoTs, enabling real-time feedback and analytics.
- Data lakehouses cater to the needs of multiple personas in the enterprise – data scientists, analysts, and business users, empowering them with smart analytics, business insights, and self-service reporting.
A sample architecture diagram of a data lakehouse is as follows.
Choosing the right repository depends on the specific needs of an organization, including the type and volume of data, security requirements, and the need for scalability and flexibility. Data warehouses are optimized for structured data, while data lakes are well-suited for unstructured data. Data lakehouses, on the other hand, provide a highly scalable, flexible, and comprehensive solution handling all types of data.