What is a Data Warehouse?Data warehouses are centralized repositories used to store data for an entire organization. Data warehouses contain data from many disparate data sources and can often be quite large. Data warehouses are different from other data repositories in that they are relational, meaning they store data in tables with rows and columns. Show
What is a Data Mart?Data Marts are a subset of data warehouses. Ultimately, what distinguishes data marts is that they are specialized. Often described as subject-orientated, data marts are limited in scope to a specific purpose or subject. There are three types of data marts: those dependent on data warehouses, those that are wholly independent, and hybrids of the two. Why Would an Organization Build a Completely Independent Data Mart?Say a sales organization wants to join together Salesforce data and commission information to display sales goals in Tableau. The sales organization isn’t interested in connecting to the larger data warehouse because they want a speedy connection to the data that strikes at the point – how near or far is the sales team away from meeting their quarterly sales goals? Dependent or Hybrid models might come into play when the sales team wants to include data from Marketo and other internal systems, and as the connected applications become larger, the need to gather these inputs into one internal source becomes greater. Dependent and Hybrid Data Marts Comparison At a Glance: Data Mart vs Data Warehouse
Cost & SizeI’ll combine cost and size here because the relationship between the two is so strong. The cost for data storage can be broken down into two elements: storage and compute. Storage, of course, depends on the volume of data to be stored, while compute is determined by the transactions (SELECT, INSERT, UPDATE, DELETE) done to the data. As you might expect, the bigger the data, the bigger the compute. Data marts are, by definition, smaller data repositories than data warehouses and so naturally will cost an organization less to spin up and maintain. PerformanceAfter cost, the other attribute affected by size is performance. Data marts are generally speedier than data warehouses because they are smaller. A thoughtful data warehouse design is necessary to ensure that these larger repositories are performant. Considerations like indexes, query optimization, materialized views, etc., must be implemented for a data warehouse to perform like the much simpler data mart. SecurityOne interesting capability of a data mart is the sectioning off of data from parties that either aren’t interested in it or, more importantly, shouldn’t see it. For example, a data warehouse might include salary or employee retention information that shouldn’t be made available to most employees. This sensitive information can be separated securely by spinning up a data mart that just contains the data appropriate to a group of employees. While a savvy database administrator can apply security rules to a data warehouse to ensure the same outcome, removing the possibility of access completely is a security benefit. Ease of ImplementationIt can take years for data warehouses to be fully implemented. Collecting the necessary data, gathering permissions, and storing it all in a sensible way that allows multiple teams within an organization to utilize the collected data successfully takes time. On the other hand, data marts can be spun up quickly with their much simpler designs and use cases. LongevityData marts can and are often spun up and then deleted after the specific use case they were created for is complete. Data warehouses have a much longer lifetime, potentially lasting years. When to Use a Data Mart vs. a Data WarehouseI’ve already mentioned one use case for using a data mart – a sales organization wanting to view Salesforce and commission data independent from the larger organization’s data repository. Historically, another reason organizations have chosen to add data marts to their data architecture is data mining. Data mining is the process of manually sifting through data to come up with an analysis that could potentially be used for a business decision. However, data mining has been largely replaced with machine learning. Machines, after all, are better than us at finding patterns. However, machine learning still requires cleaned and sampled data to train models, and this is an area where a data mart might come into play again in modern data architecture. Another frequent use case for a data mart is to power a business intelligence solution like Tableau. Users expect visualizations that load and filter quickly. One easy way to make sure this is possible is only to make a small subset of your data available to Tableau: enter the data mart. Whenever an organization wants a centralized repository for their data – which today is most of the time – it’s time to implement a data warehouse. Extra points if your organization wants the repository to be relational. At StreamSets, we help customers load data into popular cloud and on-prem data warehouses or directly into specific data marts. StreamSets smart data pipelines can also help you transform and cleanse your data before it gets to the data warehouse. StreamSets pipelines can also help you sync two data warehouses for hybrid cloud use cases. Are data marts quicker than data warehouses?3) Data Mart vs Data Warehouse: Performance
The sole objective of creating a Data Mart is to allow easy access to relevant data for a specific department or business line. Hence, a Data Mart generally provides better performance for queries simply because it handles much less data than a Data Warehouse.
How data mart is different from data warehouse?Data marts contain repositories of summarized data collected for analysis on a specific section or unit within an organization, for example, the sales department. A data warehouse is a large centralized repository of data that contains information from many sources within an organization.
How data mart is useful in data warehouse?A data mart is a subset of a data warehouse focused on a particular line of business, department, or subject area. Data marts make specific data available to a defined group of users, which allows those users to quickly access critical insights without wasting time searching through an entire data warehouse.
For which task is a data mart more useful or appropriate than a data warehouse?Data marts are faster and easier to use than data warehouses. Data marts typically function as a subset of a data warehouse to focus on one area for analytical purposes, such as a specific department within an organization. Data marts are used to help make business decisions by helping with analysis and reporting.
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