Data Lakehouse vs. Data Hub

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Abstract

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    </div><p id="48f8">This means that the data is not pre-processed or structured in any way, making it much more flexible and easier to work with than in a traditional Data Warehouse. From the Data Lake the data can then be cleaned, transformed and aggregated and used in the Data Warehouse but also in other use cases like Machine Learning, Data Science or BI systems.</p><h2 id="add9">The Data Hub</h2><p id="80f3">A Data Hub is a data exchange with friction-less data flow at its core. It can be described as a solution consisting of different technologies: Data Warehouse, Engineering, Data Science, etc. It’s rather a technology, but an approach to more effectively determine where, when, and for whom data needs to be mediated, shared, and then linked and/or persisted. Endpoints, which can be applications, processes, people, or algorithms, interact with the hub, potentially in real time, to provide data to or receive data from the hub [2].</p><div id="8b15" class="link-block">
      <a href="https://towardsdatascience.com/what-is-a-data-hub-41d2ac34c270">
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            <h2>What is a Data Hub?</h2>
            <div><h3>Why Architectures such as Data Hubs are on the Rise</h3></div>
            <div><p>towardsdatascience.com</p></div>
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    </div><h2 id="8e0a">When to choose what?</h2><p id="4cd4">The Data Lakehouse is ideal for companies that need to store and process large amounts of unstructured data quickly or want to updrade their traditional Data Warehouse. The Data Lakehouse, like the Data Warehouse or the Data Lake, is to be regarded as a data repository that only supplies systems that are operated in the area of data analys

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is, but does not generally pass on data to apps, websites and other tools. A data hub on the other hand is best suited for organizations that need to structure and process data so it can be used by a variety of data consumers. A data hub is also a good choice for organizations that need to ensure the accuracy and consistency of their data, as pre-processing and structuring the data makes it easier to maintain data quality.</p><div id="2924" class="link-block"> <a href="https://readmedium.com/data-lake-vs-data-hub-9e259a1ca551"> <div> <div> <h2>Data Lake vs Data Hub</h2> <div><h3>What are the Differences and how they actually work together</h3></div> <div><p>medium.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/1*vyTUsafIJOlu--qNKtGPaQ.jpeg)"></div> </div> </div> </a> </div><p id="482f">In this way, many systems, data platforms and even the previous ESB and ETL processes can be integrated together in a data hub that supplies the individual systems with each other. To summarize again, the Data Lakehouse is a data analysis platform and, so to speak, the further development of the Data Warehouse, while the Data Hub is used, for example, exactly in front of such a platform and supplies the system with data from other systems, but it bundles these processes so that they can be monitored more clearly, more securely and better.</p><h2 id="6360">Sources and Further Readings</h2><p id="ba67">[1] AWS, <a href="https://aws.amazon.com/big-data/datalakes-and-analytics/data-lake-house/?nc1=h_ls">What is a Lake House approach?</a> (2021)</p><p id="9af3">[2] Eckerson, <a href="https://www.eckerson.com/articles/data-hubs-what-s-next-in-data-architecture">Data Hubs — What’s Next in Data Architecture?</a> (2019)</p></article></body>

What is what and which Use Cases do the two Solution Approaches have?

In addition to the Data Lake and the Data Warehouse, which have already become very established in companies, there are also two somewhat lesser-known concepts that have emerged in recent years to meet this challenge: The Data Lakehouse and the Data Hub. Both concepts offer advantages and disadvantages, and choosing the right concept depends on the needs of the business.

The Data Lakehouse

The Data Lakehouse combines Data Lake and Data Warehouse, but it is not just about setting up a Data Lake with a Data Warehouse, but rather integrating a Data Lake, a Data Warehouse, and purpose-built storage to enable unified governance and ease of data movement [1]. In a Data Lakehouse, data is stored in a central location, just like in a classical Data Warehouse, but the data is stored in its raw form up in front in the Data Lake.

This means that the data is not pre-processed or structured in any way, making it much more flexible and easier to work with than in a traditional Data Warehouse. From the Data Lake the data can then be cleaned, transformed and aggregated and used in the Data Warehouse but also in other use cases like Machine Learning, Data Science or BI systems.

The Data Hub

A Data Hub is a data exchange with friction-less data flow at its core. It can be described as a solution consisting of different technologies: Data Warehouse, Engineering, Data Science, etc. It’s rather a technology, but an approach to more effectively determine where, when, and for whom data needs to be mediated, shared, and then linked and/or persisted. Endpoints, which can be applications, processes, people, or algorithms, interact with the hub, potentially in real time, to provide data to or receive data from the hub [2].

When to choose what?

The Data Lakehouse is ideal for companies that need to store and process large amounts of unstructured data quickly or want to updrade their traditional Data Warehouse. The Data Lakehouse, like the Data Warehouse or the Data Lake, is to be regarded as a data repository that only supplies systems that are operated in the area of data analysis, but does not generally pass on data to apps, websites and other tools. A data hub on the other hand is best suited for organizations that need to structure and process data so it can be used by a variety of data consumers. A data hub is also a good choice for organizations that need to ensure the accuracy and consistency of their data, as pre-processing and structuring the data makes it easier to maintain data quality.

In this way, many systems, data platforms and even the previous ESB and ETL processes can be integrated together in a data hub that supplies the individual systems with each other. To summarize again, the Data Lakehouse is a data analysis platform and, so to speak, the further development of the Data Warehouse, while the Data Hub is used, for example, exactly in front of such a platform and supplies the system with data from other systems, but it bundles these processes so that they can be monitored more clearly, more securely and better.

Data Lakehouse vs. Data Hub

What is what and which Use Cases do the two Solution Approaches have?

The Data Lakehouse

What is a Data Lakehouse?

New Paradigm or just a Buzzword?

The Data Hub

What is a Data Hub?

Why Architectures such as Data Hubs are on the Rise

When to choose what?

Data Lake vs Data Hub

What are the Differences and how they actually work together

Sources and Further Readings