Data lakehouses are a relatively new concept in data architecture and are fast becoming the go-to solution for data teams that want a more flexible, cost-effective, and efficient way of managing their structured, semi-structured, and unstructured data.
If you’re looking for a simple explanation of what a data lakehouse is, it’s best to think of it as a data architecture that combines the strengths of data warehouses and data lakes and helps to overcome their limitations.
Before we delve into all the reasons why your business should be considering using a data lakehouse, it’s worth having a recap of data warehouses and data lakes, and why a hybrid solution is needed.
The Limitations of Data Lakes and Data Warehouses
Before the advent of data lakehouses, businesses typically had to make the choice between opting for the structure and governance of a data warehouse or the scalability and flexibility of a data lake.
Both solutions have their advantages, but also their limitations.
Data warehouses are great at consolidating data from a wide range of different sources, to provide businesses with a single source of truth.
They have historically been a preferred solution for businesses that want to make data-driven decisions from structured data, and are typically used for business intelligence reporting and analysis.
One of the major benefits of data warehouses is their strength in data governance. When businesses use a data warehouse, they can have a lot of control over the structure, standardization, and quality of data. But this strength leads to one of the main limitations of data warehouses.
Because data warehouses can only contain structured or semi-structured data, data teams need to make sure that data is in the correct format before it is uploaded. This can often be a time-consuming task.
Another drawback of data warehouses is that they can often be costly to scale and maintain as the amount of business data grows.
A data lake can be an effective solution for organizations that are looking to store vast amounts of data in its raw, native format after data collection.
Data lakes are typically more cost-effective than data warehouses, providing businesses with the opportunity to store large amounts of data affordably.
A data lake is also more flexible than a data warehouse; allowing businesses to store all their structured and semi-structured data, and unstructured data like text files, images, and videos.
There is a downside that comes with this flexibility, however. As data lakes allow the storage of raw data, it can be challenging to ensure data governance and be confident in the quality, accuracy, and compliance of data.
Data lakes also often require skilled data professionals who are confident in different querying languages to extract meaningful insights from the data.
The benefits of using a data lakehouse
We’ve established that both data warehouses and data lakes have their strengths and limitations.
Businesses have historically had to choose between the two based on their specific needs, or in certain cases consider investing in both.
But with the rise of data lakehouse architecture, trade-offs and duplication of costs is no longer necessary.
Data lakehouses offer a hybrid solution that combines the scalability and flexibility of data lakes with the structure and governance of data warehouses. This means businesses can store and analyze all types of data, while also ensuring that the data is clean, consistent, and secure.
A data lakehouse also allows anyone within an organization to query vast amounts of data without the need for a data scientist.
This is great news for CMOs, Data Managers, and marketing teams who are overwhelmed with data and need a robust solution to help them use their data to its full potential.
Choosing a data lakehouse can bring a wide range of benefits to an organization.
If you’re in a position where you’re considering a data warehouse vs data lake vs data lakehouse, take a look at the list of benefits of the data lakehouse architecture before you make your decision.
In today’s data-driven world, businesses need to manage vast amounts of data to optimize performance and remain competitive.
But as your volume of data grows, so can the costs.
With a data lakehouse, your business can store a large amount of data in structured, semi-structured, and unstructured formats without having to worry about soaring costs.
For example, Fashionette used Adverity to connect to a vast amount of business data from more than 40 different data sources. The result of the integration was that the business was finally able to understand its holistic KPIs and use this insight to improve its marketing ROI.
The dream for many data teams is a unified view of business data that exists in a single location.
The reality for a lot of businesses is either a data warehouse that can’t store unstructured data, or having both a data warehouse and a data lake for different types of data - which obviously runs counter to the dream of having all business data in one place!
If you’re considering a data warehouse vs data lake vs data lakehouse, and you’re only looking for a single solution, it has to be a data lakehouse.
A data lakehouse has the flexibility to support all types of structured, semi-structured and unstructured data like a data lake. It also has the ability to enforce a schema to all data, to allow analysis from anyone within the business like a data warehouse. It really is the best of both worlds.
Advanced analytics uses techniques like machine learning and predictive modeling to help businesses make forward-thinking strategic decisions.
Advanced analytics typically requires access to vast amounts of raw data that hasn’t been processed or manipulated in order to deliver accurate results.
The ability to support all types of data means that a data lakehouse is a great solution for any business that is interested in accessing advanced analytics to predict trends and future performance.
Data governance refers to the processes and standards a business puts in place for data gathering and managing and controlling data. Effective data governance is really important for ensuring data quality and consistency, and compliance with regulations.
One of the drawbacks for many businesses that use a data lake is that it supports unstructured data without a predefined schema. This can make data lakes a challenge from a data quality and data governance perspective, and can also make it difficult for people across the business to easily access and analyze data.
When you compare data lakes vs data lakehouses, although they both support unstructured data, with a data lakehouse you’re able to employ advanced schema enforcement.
Schema enforcement helps data teams ensure that all data in the lakehouse is stored in a structured format, which not only enables more robust data governance but also makes data easier to manage, track, query, and analyze.
Another important feature of data lakehouses is their meta-data layer.
The meta-data layer in a data lakehouse helps data teams to catalog every piece of data in storage, making it really easy for everyone across the business to organize and analyze data.
The meta-data layer also helps data teams manage data quality control and easily index, cache, and version business data.
Integrate your data with Adverity
Data lakehouses are quickly becoming a go-to solution for businesses looking to overcome the limitations of traditional data storage and management approaches.
By combining the best features of data lakes and data warehouses, a data lakehouse provides businesses with the benefits of scalability, flexibility, real-time analytics, advanced analytics capabilities, robust data governance, and a metadata layer that sets it apart from other data architectures.
However, to fully take advantage of the benefits of a data lakehouse, effective data integration is crucial.
Adverity has one of the world’s largest libraries of more than 600 pre-built data connectors to help integrate all your structured, semi-structured, and unstructured data. You’re also able to build your own custom data collection methods to integrate data via webhook, SFTP, and API.
We’ve recently helped Mindshare free their data teams from manual data integration processes, boosting their efficiency of marketing data collection, integration, and analysis for their 100+ clients.
By working with Adverity to quickly and easily connect all your business data sources to your data lakehouse, your business can benefit from a streamlined data management process that saves valuable time and resources, and the ability to make faster data-driven decisions.