Why choose DataMastery
Modern companies require flexible, high-performance systems for diverse data applications for SQL analytics, real-time monitoring, data science, and machine learning. These new data trends are causing the traditional data warehouse to break. The Databricks Lakehouse Platform can easily cope with the demands of data growth, fast query expectations from users, non-relational or unstructured data, and cloud-born data which cause issues and slow response rates in traditional data warehouses.
Our solution accelerators provide you with a future-proofed platform built on our custom frameworks to deliver a DevOps-ready data platform. With optimised ingestion, transformation and monitoring built-in, it will address immediate business requirements but allows you to easily extend this platform for future reporting, machine learning and data science needs.
We take a collaborative approach and work with you to ensure we fully understand your requirements and the end product meets your business needs. We will ensure the business agrees with our toolset selection by scheduling in depth overview sessions of the product before the implementation starts to ensure the solution selected meets their needs.
What is a Data Lakehouse?
A data Lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data. Data Lakehouses implement data warehouses’ data structures and management features for data lakes, which are typically more cost-effective for data storage.
A Lakehouse has the following key features:
Transaction support: Support for ACID transactions ensures consistency as multiple parties concurrently read or write data, typically using SQL.
Schema enforcement and governance: Schema enforcement and evolution
BI support: BI tools directly on the source data to minimise costs
Storage is decoupled from compute: Storage and compute use separate clusters
Openness: Open storage formats e.g. Parquet
Support for diverse data types ranging from unstructured to structured data: 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.
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.
From BI to AI
The Lakehouse is a new data management architecture that radically simplifies enterprise data infrastructure and accelerates innovation in an age when machine learning is poised to disrupt every industry. In the past most of the data that went into a company’s products or decision making was structured data from operational systems, whereas today, many products incorporate AI in the form of computer vision and speech models, text mining, and others. Why use a Lakehouse instead of a data lake for AI? A Lakehouse gives you data versioning, governance, security and ACID properties that are needed even for unstructured data.
Current lakehouses reduce cost but their performance can still lag specialized systems (such as data warehouses) that have years of investments and real-world deployments behind them. Users may favor certain tools (BI tools, IDEs, notebooks) over others so Lakehouses will also need to improve their UX and their connectors to popular tools so they can appeal to a variety of personas. These and other issues will be addressed as the technology continues to mature and develop. Over time Lakehouses will close these gaps while retaining the core properties of being simpler, more cost efficient, and more capable of serving diverse data applications.