Enabling a unified data storage architecture through data lakes, warehouses, and marts
Enterprises across industries are looking for a scalable, flexible, and adaptable data storage solution that supports a multitude of use cases, delivers real-time insights, and provides a unified view of all enterprise data.
Data stores (also called repositories) form the nervous system of any storage architecture on-premise or on the cloud. These repositories can be in the form of data lakes, data warehouses, or data marts – each of which serves a different purpose. If designed correctly, these repositories can complement each other and empower you with the business intelligence tools, reporting, and dashboards you need to run your business efficiently.
Let’s take a quick look at what differentiates the three different types of storage repositories:
- Data lake – A central repository of data in raw formats, which can be leveraged to support various workloads, applications, and analytics
- Data warehouse – A repository where data is selected, organized, structured, and often transformed for business intelligence purposes before being stored
- Data mart – A subject-oriented database containing summarized data collected for analysis on a specific business line/department of an organization – like Sales
Benefits of a unified data storage architecture
Recent advancements in technology have enabled the creation of integrated data solutions. For instance, Azure Synapse Analytics combines a traditional SQL-like approach with modern Spark-driven architecture to provide an integrated storage environment. Several leading platforms have also come up with a unified data architecture that supports multiple data warehouses, data lakes, data engineering, and data science workloads. This type of architecture provides logical integration of compute, storage, and cloud services layers with virtually unlimited concurrency, using a single underlying platform.
Here’s a look at how different kinds of data repositories can work in tandem to enhance business outcomes:
Increased capacity and performance
Many enterprises consider creating a data lake to replace their data warehouse and mart with its unlimited storage capacity. However, since data lakes store huge volumes of data in a generic structure, we would not advise using them for hosting a reporting or customer-facing application. Instead, you can store a golden copy of consumable data in your data warehouse, as this supports business reporting and can also feed data into marts for further processing. This is just one instance of how data marts can co-exist with data lakes and warehouses to provide a robust solution for catering to different capacity requirements and meeting performance SLAs.
Optimal resource utilization
Consider an enterprise with data coming in from multiple sources – like transactional systems, social networks, sensors, devices, and others. In this scenario, a data lake can act as a single storage repository for all the enterprise data. Quality checks, archival, and history retention can be applied in the data lake, which would free up EDW resources enabling it to be used for true data warehousing and business intelligence practices. Additionally, if you need to set up an enterprise search system on a subset of data, the consumable data can be fetched from the EDW and stored in a zone catering to the search use case.
Enabling a single source of truth
Not all data ingested in an EDW is of immediate use to an enterprise. In the absence of a data lake, data which is not needed immediately often gets lost or converted to another form. Bringing a data lake together with a warehouse and mart helps avoid such losses and creates an environment of limitless opportunity, where your data lake becomes the single source of truth for the business. You can leverage your warehouse for working with filtered, processed information, and gather specific business insights from the highly targeted data models residing in your marts.
The co-existence of data lakes, data warehouses, and data marts results in a loosely coupled storage architecture, where warehouses and marts can be physically separate, yet dependent on the data lake for most data. While the underlying data engines share a consistent governance model, they can be deployed in dynamically scalable configurations. This type of decoupling makes it easier to run various kinds of workloads and manage peaks in volumes. Developers also find it much easier to make changes or add features in a loosely coupled architecture, as changes can be made on individual components without cascading effects across the entire ecosystem.
Data lakes, data warehouses, and data marts play a unique role in storing and analyzing an organization’s data. Given today’s ever-changing business landscape, it is important to know the strengths and weaknesses of each technology to adopt a holistic approach in line with business needs. We also recommend formulating a robust master data management strategy based on your specific consumption patterns and use cases, as well as critical factors like data governance, access control, cost, and risk and compliance management. This will lay the foundation for true data literacy and help you unlock the full potential of your data.
With over a decade of experience in modern data platforms, Impetus Technologies can help you assess the pros and cons of various technologies and combine the best of different worlds to create a comprehensive, powerful data solution that meets your current and future business needs.
*Depending on business needs, there may be additional data flows between the different layers