If you’ve outgrown your current enterprise data management system, you may be looking to upgrade to either a data mart, a data warehouse, a data lake, or a data lakehouse.
They’re all significant investments, but which fits your needs best? And will it meet your future needs?
The answer depends on the type of data you’re dealing with and the scope of that data.
What is a Database?
A database is the most basic form of data management. It houses related data used to capture a specific situation. For example, a point-of-sale database captures and stores data pertaining to retail transactions.
Most databases act as single-purpose repositories of raw transactional data and perform online transactional processing (OLTP).
But single-purpose doesn’t mean there’s only one type of database. There are several different types:
- Structured databases
- Relational databases
- Relational database management systems (RDBMS)
- Unstructured data structures (a.k.a., NoSQL databases)
All new data that enters the database gets processed, organized, managed, and updated. It is then stored in tables.
What is a Data Warehouse?
A data warehouse provides core analytics functionality for your business. Together with an operational data store (ODS), a data warehouse stores data captured by your company’s databases. This might include databases storing point-of-sale activity, online activity, customer information, and HR data.
The purpose of a data warehouse is to take all data from disparate sources, clean it (via the ODS), and store it in a single location. And that makes it an exceptional tool for data analysis.
The business units that use the data for reporting and analysis often define how warehoused data is organized. Commonly, however, data warehouses use SQL to query the data and tables, indexes, keys, views, and data types to organize the data and ensure its integrity.
It’s often best to capture transactional data in databases and reserve your data warehouse for performing data analysis and online analytical processing (OLAP).
What is a Data Mart?
A data mart is a data warehouse with a narrower focus. Data marts maintain and store cleaned data for analysis, but they limit the scope of visibility to one subject matter or business unit.
By focusing on the data for an individual business unit (for example, the marketing department), that business unit can analyze only the relevant data without having to filter out excess data it cannot use.
Data marts also enhance data security. Because business units only see the data they need, there’s no chance they might corrupt or misuse data from another business unit. It also reduces the potential for conflicting reports.
With less data to process, data mart queries run faster, too.
What is a Data Lake?
A data lake is a repository for your business’s unstructured raw data and processed structured data. Data lakes aren’t as focused as a data warehouse or database. They capture anything you think might be valuable in the future—images, videos, PDFs, and any other digital information.
Data lakes are similar to data warehouses in the way they extract and process data from multiple disparate sources. You can use a data lake for analysis and reporting, too.
Where they differ is that data lakes use more sophisticated technology for processing and analysis than data warehouses. They are often paired with machine learning, for example. You can load data into a data lake without an established methodology, and you don’t need an ODS for cleaning the data.
Because of a data lake’s additional complexity, you will need users who are experienced in software development and data science techniques.
A data lake and a data warehouse work well together, giving you more query options. While a data warehouse gives you structured and organized information, adding a data lake’s real-time analytics may give you greater insights drawn from raw data.
If that sounds like what you need, you may want to consider the next technology on the list: the data lakehouse.
What is a Data Lakehouse?
A data lakehouse is an open architecture that—as the name suggests—merges the functionalities of data lakes and data warehouses. They were made possible by the rise of cheap and highly reliable cloud storage solutions.
Data lakehouses apply the kind of data structures and data management features of a data warehouse to raw data stored in cloud-based repositories. They simplify your business’s data infrastructure in a way that can help accelerate innovation. They often reduce costs, as well.
Before the advent of data lakehouses, businesses had to rely on structured data to inform their products or decision-making. Data engineers manually cleaned, ordered, and gave the data a standardized format. This structured data came from a system that processed a business’s day-to-day transactions, like a data warehouse.
Data lakehouses allow you to tap into the flood of unstructured data generated by AI-powered products while maintaining the necessary data versioning, governance, security, and integrity.
While they may sound ideal, data lakehouses do have some downsides. If you already have a data warehouse or similar enterprise data management system, you will likely get lower-quality results from a lakehouse solution—at least, at first. That’s because your current solution has years of investments and real-world deployments behind it.
Data lakehouses are also relatively new and have fewer interfaces to popular software. If your users have favorite BI tools, IDEs, or similar software, they may not be able to connect those tools to your lakehouse solution.
These deficiencies will shake out as data lakehouse technology matures. However, you may need to weigh their impact in the short term.
Tomorrow’s Enterprise Data Management Solutions
According to IDC, the world generated 64 zettabytes of data in 2020—nearly double the 33 zettabytes generated in 2018. They predict that in 2025, we’ll reach 175 zettabytes; according to Forbes, 150 zettabytes (85%) of that data will need to be analyzed.
The unrelenting flood of big data may give rise to new and more powerful enterprise data management solutions.
It also means that enterprise-level businesses will need to make their own predictions. Will your business’s data grow at the same rate? If so, will the big data solution you’re considering be up to the task?
Before investing significant dollars in an enterprise data management solution, be sure you won’t need to replace it again in three years.