Data Lakehouse
A Single Solution For All Applications
Data-driven ecosystems are evolving rapidly and can serve a variety of applications. The data collection into the database is the first step of the ladder that leads to diverse applications such as Business Intelligence, Data Analytics, Data Science, and Machine Learning. However, these applications require particular data structuring and data processing architecture. Data Warehouses and Data lakes are two solutions that have emerged in the last decade to make the most out of structured/unstructured data. However, it is about time for a new solution to mitigate any limitations of these two solutions.
What Is A Data Warehouse?
Data Warehouses help organizations with the purpose of Business Intelligence and Reporting. A data warehouse exists on top of several databases to manage the data as a layer that performs data analytics. In the case of a data warehouse, a schema upon importing data from databases. As output, data warehouses provide data visualization of the data through analytics.
As such, organizations benefit from data warehouses by storing data from operational systems and performing analytics on that data. Data warehouses are widely implemented by organizations becoming an essential part of data solutions.
What Is A Data Lake?
A data lake is a data architecture where one can store both structures and unstructured data. In a data lake, one can store raw data without any schema. Therefore, data lakes are efficient for various data sources and don’t require ETL or transformations on them. They can store any type of data format, including images, videos, texas, and files. Data lakes can store Machine Learning model artifacts, real-time data, and analytics outputs, making them suitable to store data for numerous applications. In this case, the schema is defined on reading as data gets processed on export with this architecture.
Need For A New Architecture: Data Lakehouses
There are several areas where data warehouses and data lakes are not efficient as an architecture. Also, Data warehouses don’t support modern data use-cases as they don’t support video, audio, or text. Data warehouses support limited Machine Learning and data science capabilities. Moreover, data warehouses are closed systems that store data in proprietary formats.
In contrast, data lakes can support Machine Learning and data science, but they lack data warehousing and business intelligence capabilities. Moreover, data lakes are complex to set up and offer poor performance. Using data warehouses and data lakes in parallel leads to duplication and a lot of administrative burdens.
Therefore, the solution was to have a data architecture that can support the best of both; data warehouses and data lakes. That’s where the need for a new data architecture that can support these applications alone arises. Data lakehouses can be the key to solve any existing limitations of these two architectures while providing more governance, flexibility, and higher performance.
What Is A Data Lakehouse?
A data lakehouse is a data architecture that combines the best of both; data warehouses and data lakes. Any data lakehouse can store all sorts of data, including structured, semi-structured, or unstructured data. On that data, A data lakehouse can support & offer advanced capabilities such as machine learning, business intelligence, streaming analytics, and data science. As a result, companies can make the most out of their data and insights by implementing a data lakehouse.
Data lakehouses consist of several layers to fulfill the responsibility of both; data warehouses and data lakes. Data lakehouses can store any form of data similar to data lakes. Following the data storing, data lakehouses perform various steps, including transactions, data quality, schema enforcement, indexes, and data versioning to convert the data for the query engine. These layered processes ensure that even unstructured data from data lakes can be processed for business intelligence and analytics applications.
In summary, a data lakehouse can be the single-point solution where a variety of data can be useful for several application needs. It is even more helpful for architectures where a data warehouse or a data lake is not capable solely.
Features Of Data Lakehouses
A data lakehouse offers a handful of features, including the best capabilities of data warehouses and data lakes. Even though Data Lakehouses lose modularity and decoupling, they result in a set of advantages related to a single platform administration, reduced data redundancy, simpler schema management, and the support for diverse applications. Here are some of the standout features of data lakehouses:
Transaction support: Data lakehouses offer ACID transactions support which allows multiple parties to read and write data concurrently. It is an essential feature for any enterprise data management needs.
Mechanisms for schema enforcements & governance: Data lakehouses support schema enforcement & evolution for schema architectures such as star-schemas or snowflake-schemas following data warehouses. Also, data lakehouses support auditing mechanisms and robust governance.
Decoupled storage and compute resources: Data lakehouses provide separate storage and compute resources which allows them scalability for concurrent users and massive data.
Business Intelligence capability: With data lakehouses, one can run business intelligence tools on the source data while reducing latency. It lowers the cost by eliminating the need to deal with two copies for a data lake and a data warehouse.
Multiple workloads: Being diverse, data Lakehouses can manage multiple workloads along with needed support tools for machine learning, data science, SQL, and analytics.
Real-time data applications: Data Lakehouses support real-time streaming support that is essential for many enterprise applications. This provision eliminates the need to eliminate any separate system for streaming support.
Conclusion
A data lakehouse perfectly fits system needs when there are different complex applications to be fulfilled altogether. Of course, upgrading to a new data solution results in additional cost and time. Also, it might take some time and deployments for data lakehouse features and functions to mature before their vast adoption. However, there has been a significant growth of complex applications where the data is used for multiple applications where data warehouses or data lakes are not efficient alone. Therefore, a data lakehouse would be a significant consideration in the near future, acknowledging the massive growth scope and return on investments.