Data Warehouse, Data Lake, Data Hub or Data Platform?

The growth in the amount of data, data sources and analysis options means there are also more and more ways to store and process data. All kinds of terms get thrown around, such as Data Lake, Data Hub, Data Warehouse and Data Platform. Research by Gartner showed that demand for Data Hubs rose by 20% between 2018 and 2019. Interestingly, Gartner noted that more than 25% of customers thought a Data Hub was a Data Lake solution[1]. Gartner's research illustrates how much confusion there is about what the various terms mean. In practice, we also notice there is a lot of confusion: how do these terms differ from one another? This blog sheds more light on what these terms mean.

Data Warehouse

A Data Warehouse (DWH) is an integrated store of information whose purpose is to feed business decisions and analyses. A data warehouse lays the foundation for Business Intelligence (BI) and Analytics. The data in a data warehouse often comes from various sources inside or outside the organization. Because a data warehouse brings together huge amounts of data from different data sources (HRM, DMS, ERP), it enables valuable insights. A data warehouse saves time for companies that collect data at scale and ensures consistent definitions of business information. If you want an integrated view of your business operations, a data warehouse is essential.

Data Lake

A data lake is a storage location that holds large amounts of raw data in its original structure. A Data Lake can contain both structured and unstructured data. The data structure of individual files, and how they should be accessed, is not known until the data is used. It's important not to see a data lake as a replacement storage system, but as a place where analysis and research can be carried out with unprecedented freedom, thanks to the relatively low cost of storage and the ease of scaling. Data lakes generally provide a solid foundation for reporting, visualizations, advanced analytics and machine learning.

Data Hub

A data hub doesn't store data itself, but manages the flow of data between source systems, target systems and users. With a data hub, you specify exactly what needs to happen to the data. For example, you can link certain sensor information to an automated ordering system. It makes as much use as possible of the power of the source systems to guarantee optimal performance. A data hub often takes the form of a hub-and-spoke architecture, in which systems can distribute data via the Data Hub, rather than via point-to-point integration, where every system is connected to every other system it needs to share data with.

A data hub also gives organizations the insight they need to interpret data correctly. After all, if you understand what you're looking at, it becomes easier to guarantee that data is correct, or to adjust it where necessary. You can literally see, down to column and row level, how datasets are built up. What's more, you always comply with laws and regulations, because you know exactly who has access to which data and where data is stored. Unlike in a Data Warehouse, the data in a data hub is not necessarily integrated and can contain different levels of detail side by side. Compared with a data lake, a data hub can provide data in different formats. Whereas data warehouses and data lakes are endpoints for data, a data hub is a junction that data flows through.

Data Platform

A data platform, also known as a data management platform, is an integrated solution that combines the functionality of a data lake, data warehouse and data hub with elements of a Business Intelligence (BI) platform. Without a data platform, a separate tool or set of tools is usually used for each aspect. This creates a complex landscape in which many tools have to be managed to get data flowing from source to end user. A data platform centralizes these solutions in a single tool, delivering a product that is much more manageable.

Differences

Conclusion

The enormous growth in data sources and data volume, and the differing data needs of different users, create major challenges for BI/IT departments and others who work with data for analytics, artificial intelligence (AI) and BI. Organizations use all kinds of different tools to process and manage data. It doesn't have to be that way. That's why E-mergo chose to partner with TimeXtender. The TimeXtender platform provides a cohesive data structure for on-premises technology and the cloud. It lets you connect to various data sources and catalog, model, move and document data for analytics and AI purposes.

TimeXtender aims to change the traditional way of BI development by automating repetitive work. Building a traditional data platform involves a lot of repetitive, time-consuming work. With TimeXtender, you can move to an integrated data platform that delivers data insights 5 to 10 times faster thanks to automation. This means you can save as much as 80% on maintenance and develop 70% faster.

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