With the use of cloud technology increasingly on the rise, there are plenty of forward-thinking opportunities for data organization and analysis. The data lakehouse is a newer technology in data science and data management that can help organizations with both data organization/analysis.
Many organizations are already using lakehouse architecture but some data engineers are still yet to take advantage of its benefits in business management.
Data Lakehouse Vs Data Lake Vs Data Warehouse
A data lake contains all of an organization’s data in its rawest form. This can be accessed immediately or in the future, but in this form, it’s just noise at this point. Data lakes contain unstructured data that isn’t of much use to anyone… At least not yet!
But a traditional data warehouse contains this data in an organized, more structured form. This is business-friendly and easy to analyze in comparison! The problem with this is if the original data lakes are unorganized, it can lead to future issues with the data warehouse. Data warehouses can be useful in machine learning.
This is where data lakehouses come in! They are a newer technology that combines the two. They improve on data warehouses and other (now outdated) technologies to make everyone’s lives simpler as well as more productive.
What Is Data Lakehouse?
Data lakehouse architecture ensures secure, economical artificial intelligence and business intelligence data storage/analysis. It is also good for organizations wanting to move from business intelligence to artificial intelligence. Data lakehouses are also a reliable and cost-effective way to handle raw data.
So How Does a Data Lakehouse Work?
Lakehouse architecture keeps large amounts of data in raw formats, like data lakes for example. But this is hard to analyze and deal with by itself.
Components of a data lakehouse include delta tables and unity catalogs. These then include ACID transactions (Atomicity, Consistency, Isolation, and Durability), Data Versioning, ETL, and Indexing. Then data governance, sharing, and auditing.
A megastore keeps all the metadata that would define the data objects in a data lakehouse. This is all kept inside something called object storage.
Programs like Apache Spark are also used for handling big data workloads. A Delta Lake improves the reliability of this in software like Apache Spark.
Apache Spark uses three tiers of metadata layers and these programs are all part of what’s called the modern data stack.
What Are the Benefits of the Data Lakehouse for Organizations?
There are many benefits of data lakehouses for organizations ranging from it being a more cost-effective option to being able to stream results in real-time. Data lakehouses also stop data reduplication by unifying overall data which is space and time-saving.
Data scientists can use data lakehouse as an alternative to outdated data warehouses and for advanced analytics. So it is a win-win for everyone and a step into the future of data organization.
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