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A data platform can transform the way your business operates, providing you with the insights you need to make better strategic decisions. By leveraging the power of data, you can optimize your operations, improve customer experiences, and increase revenue. It helps you centralize all of your data and gain a comprehensive view of your business. This means you can quickly identify trends, patterns, and opportunities that may have been hidden in your data.
For example, a data platform can help you understand your customers better. By analyzing data on their purchase history, demographics, and preferences, you can create personalized experiences that increase customer loyalty and retention. You can also identify new customer segments and develop targeted marketing campaigns to reach them.
A data platform can also help you optimize your operations. By analyzing data on inventory, sales, and revenue, you can identify areas where you can reduce costs, increase efficiency, and maximize profitability. This can include everything from optimizing your supply chain to improving your pricing strategy.
New systems are beginning to emerge that address the limitations of data lakes. A lakehouse is a new, open architecture that combines the best elements of data lakes and data warehouses. 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, now that cheap and highly reliable storage (in the form of object stores) are available.
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.
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.
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. By using a SQL Serverless Pool people can query the data lake via tools that they are used to like SQL Server Management Studio.
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. It is also much cheaper to store data in the data lake then it is storing it in a relational database. Backing up data redundantly can be automated without having to write complex code or procedures.
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.
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.
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.
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.
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