Data lakehouse: The innovative solution to data management
You may be familiar with, or even use, a data warehouse or a data lake. Or both. Certainly, these two data storage solutions have been popular and effective in recent years.
In case you’re not familiar with these two options, a data warehouse is a storage architecture where structured data is archived with a specific purpose in mind. And a data lake is a store for vast volumes of unstructured data in their raw format.
Now, there is a new buzzword floating around the data management space; the data lakehouse. But what exactly is it? What does it do? And how can it benefit your business?
Why do I need a data lakehouse?
In today’s climate, businesses increasingly need a real-time view of their data, but also for that data to be ready to use. Whether that is for business insights, or to apply initiatives like personalisation or audience selection.
Where data warehouses provide vast architecture allowing for specified BI and reporting, they lack the scalability and speed of a data lake. Often relying on batch updates rather than real-time data. On the flip side, whilst data lakes allow for speed and cost-effective solutions for accessing data, this raw data is not optimised for use.
This is where the data lakehouse comes to save the day!
The data lakehouse brings the best elements of the traditional data warehouse and data lakes into one solution. It enables logical organisation of data (like in a data warehouse) so that it is ready to be put to use, but with the scalability and speed of a data lake.
A lakehouse allows businesses to access their trusted, synchronised data quickly. But, in a way that supports Business Intelligence, machine learning and Artificial Intelligence (AI).
How does a data lakehouse work?
Built to house both structured and unstructured data, a lakehouse allows you to apply structure and schema (as found in a data warehouse) to the unstructured data you’d typically find in a data lake. It achieves this by separating the compute from the storage and introducing a metadata and governance layer.
This enables you to choose where to get data from to support a specific use case. If you need data with master data management (MDM) rules applied, it will take a view after governance, but if you need access to raw data, it can be connected to this.
Smarter analytics for a smarter world
Organisations continue to take the logical next step in their data journey from BI to AI. All the signs suggest machine learning and AI will become fundamental for every industry in the coming years.
A data lakehouse’s smarter analytical capabilities enables organisations to extract business insights easily. This paves the way for personalisation to support the customer experience, predictive modelling to support purchasing, and new technologies such as conversational AI to support customer services.
How to implement a data lakehouse
Although data lakehouses are the next logical step in the mass management of data, they are still in their infancy. Early examples such as Delta Lake offer strong architects, processing, integration, and style, but they still lack in governance.
In order to implement this governance layer required in a data lakehouse, specialist technologies (such as data cataloguing or MDM) are required. Both of these technologies typically require specialist data skills to implement.
We’ve also recently seen Google Cloud enter the market with BigLake. And you can bet AWS is on the verge of bundling their recommended ‘lakehouse architecture’ into a product.
If you want to learn more about data lakehouses, get in touch to talk to our data experts.
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