The Lakehouse Platform combines the best elements of data lakes and data warehouses





The Lakehouse Platform delivers data management and performance typically found in data warehouses with the low-cost, flexible object stores offered by data lakes. 

This unified platform simplifies your data architecture by eliminating the data silos that traditionally separate analytics, data science, and machine learning. It’s built on open source and open standards to maximize flexibility. And, its native collaborative capabilities accelerate your ability to work across teams and innovate faster. 


Key needs we adress

1. Dealing with shattered, fragmentized data all over the place?

Simplify your data architecture by unifying your data, analytics, and AI workloads on one common platform 

2. Do you struggle with maximizing the potential of your data team?

Databricks Lakehouse Platform unifies data teams to collaborate across the entire data and AI workflow. All your data teams — from data engineers to analysts to data scientists — can now collaborate across all your workloads, accelerating your journey to become truly data-driven 

3. Do you have challenges with your Data Lake? 

  • Hard to append data – Adding newly arrived data leads to incorrect reads 
  • Modification of existing data is difficult– GDPR/CCPA requires making fine-grained changes to the existing data lake 
  • Jobs failing mid-way – Half of the data appears in the data lake, the rest is missing 
  • Real-time operations – Mixing streaming and batch leads to inconsistency 
  • Costly to keep historical versions of the data – Regulated environments require reproducibility, auditing, governance 
  • Difficult to handle large metadata– For large data lakes the metadata itself becomes difficult to manage 
  • “Too many files” problems– Data lakes are not great at handling millions of small files 
  • Hard to get great performance– Partitioning the data for performance is error-prone and difficult to change 

How is a Lakehouse better than a Warehouse? 

Lakehouses are enabled by a new system design: implementing similar data structures and data management features to those in a data warehouse directly on top of low-cost cloud storage in open formats. 

They are what you would get if you had to redesign data warehouses in the modern world:  

  • Cost-effective 
  • Highly reliable data storage   

Support your data 

  • Support for diverse data types (unstructured to structured): The lakehouse can be used to store, refine, analyze, and access data types needed for many new data applications, including images, video, audio, semi-structured data, and text. 
  • Support for diverse workloads: including data science, machine learning, and SQL and analytics. Multiple tools might be needed to support all these workloads but they all rely on the same data repository. 
  • Transaction support: In an enterprise lakehouse many data pipelines will often be reading and writing data concurrently. Support for ACID transactions ensures consistency as multiple parties concurrently read or write data, typically using SQL. 

Mold your own, personalized Data World 

  • Openness: The storage formats they use are open and standardized, such as Parquet, and they provide an API so a variety of tools and engines, including machine learning and Python/R libraries, can efficiently access the data directly 
  • Schema enforcement and governance: The Lakehouse should have a way to support schema enforcement and evolution, supporting DW schema architectures such as star/snowflake-schemas. The system should be able to reason about data integrity, and it should have robust governance and auditing mechanisms. 
  • Storage is decoupled from compute: In practice, this means storage and compute use separate clusters, thus these systems are able to scale to many more concurrent users and larger data sizes. Some modern data warehouses also have this property. 

Make Business Intelligence smart (again) 

  • BI support: Lakehouses enable using BI tools directly on the source data. This reduces staleness and improves recency, reduces latency, and lowers the cost of having to operationalize two copies of the data in both a data lake and a warehouse. 
  • End-to-end streaming: Real-time reports are the norm in many enterprises. Support for streaming eliminates the need for separate systems dedicated to serving real-time data applications. 

Set your course to
Databricks Lakehouse Platform 





Don’t hesitate to schedule a meeting if you are interested in:

  • Demo
  • Consultations
  • Proof of Concept

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Contact us

Robert Woźniak

Founder / Data&AI Strategic Advisor