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As businesses begin the movement to be more data-driven, it can be difficult to assess an organization’s exact data infrastructure need. With terms such as data lakes, data vaults, and data warehouses, IT business leaders may be asking themselves: “What exactly does our organization need to extract the most value?”

Each of the concepts boils down to managing your data in an effective way for today’s level of decision-making driven by analytics. Let’s break down various options, how they relate to each other, and what they are used for.

Data Warehouses

A data warehouse, or an enterprise data warehouse as it is sometimes known, is a curated repository of data. It provides business users with access to the right information in a usable format, and can include both current and historical information. As data enters the data warehouse environment, it is cleansed, transformed, categorized, and tagged – making it easier to manage, use, and monitor from a compliance perspective, which is where automation comes in.

The volume and velocity of data experienced by businesses today means that manually ingesting this data, processing it, and making sure it’s stored and accessible in a way that meets compliance requirements within a data warehouse is unfeasible in the modern world. However, with businesses constantly looking to data as the source of both reports and forecasts, a data warehouse is invaluable.

It’s important that data lakes do not subsume the role of a more structured data infrastructure just because of the perceived effort of ingestion. Automation can help speed the ingestion and processing to fast-track time to value with data-driven decision-making in a data warehouse.

Data Lakes

Data lakes are huge collections of data, ranging from raw data that has not been organized or processed, through to varying levels of curated data sets. One of their benefits from an analytics purpose is that varying types of consumers can access appropriate data for their needs. This makes it perfect for some of the newer use cases such as Data Science, AI, and Machine Learning, which are viewed by many companies as the future of analytics work.

It is a great way to store masses of raw data on scalable storage solutions without attempting traditional ETL or ELT (extract, transform, load), which can be expensive at this volume. However, for more traditional analytics, this type of data environment can be unwieldy and confusing – which is why organizations turn to other solutions to manage essential data in more structured environments. 

In terms of positioning within a data infrastructure, data lakes are, if you like, up-stream of other data infrastructure, and can be used as a staging area for a more structured approach such as a data warehouse, as well as providing for data exploration and Data Science.

Data Marts

A data mart is a specific subset of a data warehouse, often used for curated data on one specific subject area, which needs to be easily accessible in a short amount of time. Due to its specificity, it is often quicker and cheaper to build than a full data warehouse. However, a data mart is unable to curate and manage data from across the business to inform business decisions.

Data Vaults

Data vault modeling is an approach to data warehousing that looks to address some of the challenges posed by transforming data as part of the data warehousing process. One of the great advantages of a data vault is that it makes no assessment as to what data is “valuable” and what isn’t, whereas once data is processed and cleansed into a warehouse environment, this decision has typically been made. Data vaults have the flexibility to manage this, and to address changing sources of data, leading the data vault approach to be credited with providing a “single version of the facts” rather than a “single version of the truth.”

For enterprises with large, growing, and disparate datasets, a data vault approach to data warehousing can help tame the beast of big data into a manageable, business-centric solution, but can take time to set up. Data vault automation is a critical component to ensuring organizations can deliver and maintain data vaults that adhere to the stringent requirements of the Data Vault 2.0 methodology and will be able to do so in a practical, cost-effective, and timely manner. 

Each data approach plays its own part in ingesting, managing, and delivering data across an organization. Being able to understand how they all fit together is a valuable tool for IT managers and business leaders when trying to comprehend how to make the most out of big data. Using technologies such as automation can help speed up the establishment and management of these practices and can help businesses fully utilize their data infrastructure.