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So what is a Data Lakehouse?
Anyone who has ever managed a large Data Warehouse knows there comes a tipping point when things start to slow down.
Data Lakes present different problems. They lack some of the features we need for financial reporting and driving critical business decisions, such as full data access audits, row level security, dynamic data masking, and other enterprise security features.
The Data Lakehouse bridges these two worlds perfectly with the scalability and low storage costs of cloud data lake storage, combined with the structures and management features you’d find in a data warehouse. Leveraging the Delta Lake open file format also means you aren’t locked into a specific vendor or platform providing an easy route to use the best products for the job.
The tools used to process and assess the data offer complete flexibility. They’re compatible with either the adaptable, schema-on-read querying that comes with engines like Apache Spark or a more structured, governed approach found within relational databases such as SQL Server. Take your pick.
Some of our favourite features
- Support for ACID transactions ensures consistent performance as multiple parties concurrently read or write data, typically using SQL.
- Lakehouses support schema governance and architectures such as Star and Snowflake.
- Use BI tools directly on the source data. This reduces staleness and latency whilst also minimising storage costs as you’ll no longer need copies of the data in both a data lake and a warehouse.
- Separation of your storage and compute mean you can scale as each demand requires.
- An open and standardised storage format using Delta Lake so a variety of tools and engines can access and write the data.
- Store, analyse, and access unstructured data types including images, video, audio, semi-structured data and text.
- Does the job for everything in one platform. From data science to machine learning, and SQL to analytics.
- Support for streaming and batch data processing so no need for separate systems for real-time data applications.
Why are we such big fans of Data Lakehouses?
Rolling the best of data lakes and warehouses into one enables data teams to move faster without having to dip into loads of different systems. Lakehouse users can also work with data using a wide range of languages such as Spark, Python, R, and the ever-present SQL. Integrate a wealth of code libraries for non-BI workloads like data science and machine learning.
Data Lakehouses are also ideal for working with AI. Previously, most data that went into products or decision-making was structured data from operational systems. But now, more and more products incorporate AI in formats such as computer vision and speech models. A Lakehouse gives you data versioning, governance, security, and ACID properties that work well even with unstructured data like this.
We have a number of services designed to assess your current data architecture and implement a Data Lakehouse in the most efficient way for your business. We can meet you where you're at and figure out the best way forward for your specific needs. Find out more below.
Why choose Advancing Analytics?
As the pioneer and opinionated champions of Data Lakehouses and a one-stop shop provider for end-to-end data solutions, we’re the first people you should speak to if you need help in this area.
Our team of experts provides support to technical teams to design, build and implement a high-performing, reliable and efficient data platform that meets the current and future needs of your business.
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