Exploding data growth has led to a search for a robust, scalable, high performance data solution that can accommodate growing data demand. There are many solutions available, but the data warehouse and data lake are two of the most popular.
While a data warehouse collects and stores processed data for business intelligence and data analytics, the data lake offers a cheaper alternative and more flexibility for handling unstructured and structured data for multiple use cases like machine learning, IoT, and streaming analytics. A third solution exists, however. It’s called a lakehouse, and it combines the economical cost of storage and the flexibility of the data lake with the data management and fast-performing ACID (atomicity, consistency, isolation, durability) transactions of warehouses. Examples of popular lakehouse architecture include Databricks Lakehouse, AWS Lake House, and Azure Data Lakehouse.
With several solutions available, companies must choose the one that fulfills their storage, analytics, and business needs. In this piece, we’ll explore the Databricks lakehouse.
Databricks Lakehouse Fundamentals
The data lake and warehouse answered business data needs for some time, so what brought the need for a data lakehouse? Why couldn’t businesses use both together? One primary reason was that using the data lake and warehouse together translates to more cost and time spent in data management. On the other hand, combining the data lake and warehouse into a single architecture solution means that data lakehouses get the best of both worlds at a lower cost and better data management.
What Is a Data Lakehouse?
A data lakehouse is a new and open data management architecture solution that combines the best features of the data lake and warehouse, giving rise to a flexible, cost-effective solution that can perform ACID transactions and cater to the varied nature of data today.
How Is a Data Lakehouse Different From a Data Lake or Warehouse?
Because the data lakehouse builds on the best features of the data warehouse and data lake, it provides the following advantage over using one or the other:
Fast performance and data integrity with ACID-compliant transactions: Although data lakes offer flexibility and can handle various data formats (unstructured, structured, and semi-structured), data lake transactions aren’t ACID-compliant, making UPDATES and DELETES complex operations. Data lakehouses take on the data warehouse’s ACID-compliance nature, easing data operations and ensuring data integrity.
Cost-effective storage: Unlike data warehouses that can be expensive to set up and maintain, the data lakehouse presents a lower storage price while being able to scale massively to handle growing data workloads.
Suitable for various workloads: The data lakehouse is the best solution for organizations looking to utilize the structure of a data warehouse solution for data analytics and business intelligence while also building on the streaming and machine learning analytics capabilities of data lakes.
Simplified data management and governance: The unified architecture of a data lakehouse means easier management and governance than the multi-tiered architecture of maintaining both a data lake and warehouse, which increases data management workload.
Reliable data management: Although data lakehouses offer the storage of raw data like data lakes, they also enable ETL/ELT processes to help transform this data to improve its quality in the analysis which improves data management. Traditional data lakes lack the critical management features that help simplify ETL/ELT processes in data warehouses, like transactions, roll-backs, and zero cloning.
Reduced data redundancy: Because the data lakehouse acts as a unified storage solution for organizational data, it reduces the occurrence of data redundancy, which helps maintain data integrity and reduces storage and maintenance costs.
More manageable architecture: Most of today’s data architecture involves operating and managing data lakes and warehouses. This data architecture involves ETL-ing data into data lakes, after which data is ELT-ed into warehouses. This transport between multiple systems requires consistent management, which is difficult and costly. Additionally, every extra ETL step increases the risks of failures or introducing bugs that may reduce data quality. The unified lakehouse architecture reduces data movement, creates less opportunity for threats or bugs, and is less challenging to maintain.
What Is the Databricks Lakehouse Platform Built On?
The Databricks lakehouse is a robust data solution that allows organizations to combine their data warehousing and data lake needs to provide reliable, flexible, and high-performance data operations while ensuring proper data governance and management. Databricks Lakehouse architecture is built on Delta Lake, an open-source framework.
Here are some resulting features of the Databricks Lakehouse:
Flexibility due to its open-source building stack: The Databricks Lakehouse architecture uses Delta Lake, an open-source storage framework that allows engineers and developers to build data lakehouse architectures with compute engines like Spark, Flint, and Hive and APIs for Java, Python, Ruby, and more, to build its lakehouses. Building on an open-source tool like Delta Lake makes the Databricks Lakehouse flexible and platform agnostic, seamlessly integrating with multiple query engines, whether in the cloud or on-premises.
Easier access and collaboration: Databricks lakehouses allow organizations to access, collaborate, and build their modern data solutions by leveraging open and unrestricted access to open-source tools and a broad Databricks partner network.
Is Delta Lake a Lakehouse?
While Delta lake forms the foundation for building the Databricks lakehouse, it is not a lakehouse.
Instead, one can view Delta Lake as a temporary open-source repository/storage layer that confers the reliability and ACID transactions of data warehouses on data lakes. A Delta Lake confers the following features on a data lake:
Support for ACID transactions: Delta lakes ensure Big Data workloads are ACID-compliant by using a serialized transaction log to capture all changes made to data, ensuring integrity, reliability, and for use in audit trails.
Data versioning: The Delta lake transaction log makes it easy to reproduce and revert any changes made to data via analysis or experiments. This versioning plays an essential role in conducting ML experiments.
Schema enforcement: Delta lakes help create and enforce a defined structure and schema for data, ensuring that all data types are consistent and complete, ensuring quality standards.
Enable Data Manipulation Language (DML) operations: The Delta lake support DML operations like update, delete, and merge for use in change-data-capture and streaming upsert operations.
The Databricks Lakehouse Platform and StreamSets
StreamSets supports Delta Lake as both an origin and a destination. With StreamSets you can get started building pipelines to and from your lakehouse immediately using a low-code, visual interface. You don’t have to wait to gear up your team on specific languages or proprietary platforms to start making a difference to your business. Execute immediately on your organization’s Delta Lake strategy. Then, leverage StreamSets as a control panel to actively detect and respond when data drift occurs to keep your data flowing. Build your pipelines fast and then keep them running with StreamSets.
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