Recently, there has been a lot of buzz around the term lakehouse. Is it a database? A data warehouse? A data lake? In short, a data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the performance and reliability of data warehouses, enabling business intelligence (BI) and machine learning (ML) using a unified governance and security model. One primary goal of the lakehouse platform is to empower enterprises to make better decisions.
One of the main challenges enterprises face is finding a common language for decision making. In the past, enterprises created their own 'stack' to make decisions. These stacks were often siloed, architecturally complex and disconnected from data teams – Data Analysts, Data Engineers and Data Scientists. On top of that, each language has limited insights it can derive out of that stack – whether that's predictive, historical, or current in nature.
As a consequence of this limitation, users have a lack of trust in their data assets: tables, views, reports, dashboards, KPIs. They do not know what a particular field or term represents because there's little metadata or lineage associated with it. For example, the column "date" can have a different meaning depending on the dataset or the user. This breaks down the ability to derive insights and make sound decisions. After all, a data asset's value is derived from its ability to be understood and trusted across the enterprise.
The following chart breaks down traditional teams and their languages of choice, showcasing how convoluted a disparate approach can quickly become, especially at scale.
Team | Language (s) | Use Case | Question Answered |
---|---|---|---|
Data Analytics | SQL | Data Analytics and BI | What has happened? |
Data Engineering | SQL, Python, Scala | ETL, Scalable data transformations | What has happened? |
Streaming | Python, Java, Scala | Real time | What is happening in the near real-time? |
Data Science | Python, SQL, R | Predictive Analytics and AI/ML | What will happen? How do we respond? Automatically make the best decision? |
Adding to the complexity, end users are limited in their languages, as different data platforms are limited to specific languages. For example, data warehouses are bounded to SQL, and data science platforms are bounded to Python and R.
Finding a common language in an enterprise setting is often difficult because you need to allow many users with different data backgrounds and skills to collaborate on one single platform. So what does a common language look like?
Here are some critical characteristics:
The Databricks Lakehouse Platform is built on open source and open standards, which means you have freedom on how you evolve your data strategy. There are no walled gardens or restrictions to current and future choices allowing diverse personas in data, analytics, and AI/ML to work in a single location. With a consistent experience across all clouds, there is no need to duplicate efforts for security, data management, or operational models.
Establishing a common language with a lakehouse starts with three core components:
With the above paradigm shift, the enterprise now has a solid foundation built. This allows any users to solve their use cases and foster a culture of analytics and collaboration in their line of business. Different departments can continue to solve their toughest problems with a unified framework and ultimately a common language.
Now, this concept may sound novel, but this is exactly what happens when the lakehouse platform is built. It is a paradigm shift that fuels cultural change along with speed and sophistication. We have free trials and solution accelerators across industries to help your enterprise begin the journey on the road to a stronger data culture using a common language across your organization.