Lakehouse: The Best Data Warehouse

The exponential increase in data variety, veracity, and volume brings the challenge of effectively storing, sorting, manipulating, and driving decisions using this data. Data warehouses, data lakes, and data lakehouses are the different data management architectures, each with their share of benefits and challenges. Find out how Lakehouse is the best data management solution for your needs and how Blue Orange can help.

What is a data warehouse?

A data warehouse is a consolidated repository that stores data that has been processed, cleaned, and organized. Data warehouses work best with structured data and are used primarily for querying, data analysis, reporting, and making decisions. Snowflake, Amazon Redshift, and Google BigQuery are the big players in the data warehouse approach.

Data warehouses benefits

• Varied sources: Data warehouses serve as a central repository for data from numerous sources, and this consolidation helps make data usable.
• Subject-oriented: The subject-oriented nature of data warehouses makes it easier to focus on a particular business case such as finance or human resources.
• Performance: Warehouses are optimized for high-speed querying, typically using time-variant indexes and materialized views.
• Non-volatility: The non-volatile nature of data warehouses makes it easier to derive information from historical data.
• Time-variance: Data warehouses analyze changes over time.

Data warehouse challenges

• Coupled storage and compute: Traditional data warehouse storage and compute operations are coupled, meaning that both need to be scaled up or down, even when the requirements of both differ.
• Loss of data: Since data warehouses require data to adhere to a specific format, other metadata can be lost in the process.
• Limited flexibility and scalability: The extensive nature of data warehouses, such as extracting data from various sources, cleaning it, integrating it into one consolidated source, and processing it before storage, limits its flexibility and scalability, especially when it comes to managing huge amounts of data on an ad-hoc basis.
• Upfront investment: As data warehouses require complex setup and maintenance, they require a significant upfront financial and manual investment and even more costs when scaling up. 

What is a data lake?

A data lake differs from data warehouses as it can store and process data in its native format without needing to adhere to a specific format. Data lakes can ingest any data:

• structured (databases and Excel sheets), or
• semi-structured (XML, webpages), or 
• unstructured (images, audio, text).

They can integrate data from any source (such as on-premises, on-cloud, or edge computing sources), at any speed, and analyze the data using SQL, R, or Python.

Data lake benefits

• Varied data storage: Data lakes can store relational and non-relational data from various sources without adhering to a specific schema. This makes data management flexible and scalable.
• Economical: Data lakes separate storage and computing, allowing both to scale independently, thus reducing costs. Since they can store huge amounts of data of any structure the costs associated with adherence to a specific format are also reduced.
• Scalability: The separation of storage and computing, and the handling of huge amounts of any type of data makes it more scalable than data warehouses.
• Platform independence: Data lakes are platform independent, making it possible to analyze data from large, varied data sets.

Data lake challenges

While data lakes are far more scalable, economical, and flexible than data warehouses, they still pose some data management challenges.

• Quality control: As data lakes store data from varied sources, quality control issues related to data corruption, and improper partitioning are common. This poses a threat to data integrity, slows performance, and can introduce security risks.
• Issues with updates and deletion: Since data lakes hold tremendous amounts of unstructured data, it can be difficult to reliably update or delete the data consistently.
• Data processing issues: Large amounts of data, data trapped in silos, inherent lack of metadata management, and integration of data from varied sources also lead to slower data processing issues in data lakes. 
• Governance issues: Data lakes cannot maintain historical data at a reasonable cost, and since deletion and updates of data become a challenge, data lakes also have data governance issues,

Why is Data Lakehouse the best data warehouse?

The Data Lakehouse Architecture is a modern data management paradigm combining the strengths of data lakes and data warehouses to offer a unified platform for storing, processing, and analyzing structured and unstructured data. At its core, a Lakehouse merges the flexibility, scalability, and low-cost storage of a data lake with the data management, governance, and performance optimization features typically found in a data warehouse.

In a Lakehouse, data is stored in open formats, allowing easy integration with various analytics and machine learning tools. This structure enables businesses to perform advanced analytics, machine learning, and business intelligence (BI) workloads all from the same platform, eliminating the need for multiple disparate systems.

Data Lakehouse benefits over data lakes and data warehouse

Schema enforcement and governance support

Similar to data lakes, lakehouses can store vast amounts of structured, semi-structured, or unstructured data types. However,  Lakehouses support the enforcement of schemas on read and write operations, ensuring that data remains clean, consistent, and ready for analysis without sacrificing the flexibility of raw data storage. This helps solve the governance and quality issues often seen in data lakes.

Decoupled storage and compute

Lakehouses decouple storage and compute operations by using separate clusters for processing and storage. This allows you to have more concurrent users and scale separately.

Diverse data type support

Lakehouses let you store, refine, access, and analyze structured, semi-structured, and unstructured data types. This can include data from IoT devices, system logs, images, audio, and text.

Open and standardized formats

Storage formats like Delta, Iceberg, and Hudi, used by Lakehouse are standardized and open. This allows you to bring multiple tools for your user case to operate on your data. This allows lakehouses to be platform-independent, flexible, and efficient.

Cost efficiency

Data warehouses are highly efficient for structured data but have substantial upfront costs. Data lakes are extremely affordable as they can store large amounts of data. Data lakehouse combines the cost-effectiveness of data lakes and the flexibility of data warehouses, making it a suitable option for a huge range of data usage scenarios.

Diverse workload support

A key strength of Lakehouse architecture is its ability to support a wide range of workloads, from traditional SQL-based queries to advanced machine learning and real-time data processing. This makes it the ideal architecture for businesses looking to leverage AI and predictive analytics.

Databricks: The Best-in-Class Lakehouse Architecture

When it comes to Lakehouse implementations, Databricks leads the industry with its comprehensive and robust Lakehouse Platform. Built on top of Apache Spark and the Delta Lake storage layer, Databricks offers a unified approach to data management and analytics. Below are the standout features that make Databricks an exemplary Lakehouse Architecture:

Delta Lake

At the heart of Databricks’ lakehouse architecture is Delta Lake, an open-source storage layer that brings ACID (Atomicity, Consistency, Isolation, Durability) transactions to data lakes. This solves data quality issues by enforcing schemas, enabling version control, and ensuring data is reliable and consistent across different workloads.

ACID Transactions and Data Reliability

With Delta Lake, Databricks supports ACID transactions, allowing consistent reads and writes, even during concurrent operations. This ensures that data remains reliable, even in high-throughput environments with multiple users.

Built-In Data Governance and Security

Databricks provides robust security and data governance features, such as fine-grained access control and auditing, ensuring that enterprises meet compliance requirements while safeguarding sensitive data.

Unified Workloads Across BI, AI, and Streaming

Databricks seamlessly supports diverse workloads on the same platform. Users can run everything from traditional BI dashboards to advanced AI/ML algorithms and real-time data processing. This eliminates the need to move data between systems, accelerating time-to-insight.

Scalable Compute with Apache Spark

Databricks is built on Apache Spark, offering unparalleled scalability for large-scale data processing. Spark’s distributed computing capabilities allow businesses to efficiently process terabytes or even petabytes of data.

Serverless Architecture

Databricks also offers serverless infrastructure, removing the need to manage clusters manually. This allows businesses to focus on data processing and analytics while Databricks handles resource provisioning and scaling automatically.

Integration with Machine Learning

Databricks integrates seamlessly with popular machine learning frameworks like TensorFlow, PyTorch, and MLflow, enabling data scientists to train and deploy models directly within the lakehouse environment.

Data Sharing and Collaboration

With Databricks, teams can easily share data across departments or even with external partners. The platform supports real-time collaboration through notebooks, which integrate code, comments, and visualizations, ensuring transparency and efficient decision-making.

Conclusion

Data warehouses are efficient and quick and suitable for subject-oriented use cases. However, they are expensive to set up, cannot store large amounts of data, and are not scalable. Data lakes are cost-efficient and store large amounts of data but suffer from quality control, processing speed, and governance issues. 

Data Lakehouses solve the problems of warehouses and lakes and combine the benefits of both by offering standardized formats, enforcing schema, supporting concurrent read and write, and decoupling storage and processing. If you need a multi-faceted approach, want to work with AI, and require reporting, predictive analysis, and machine learning in your data management solution, then a Lakehouse is the best approach. Databricks is the clear industry leader in implementing Data Lakehouses, followed by Snowflake.

Data Lakehouses only offer the best of both worlds if implemented correctly. As you navigate your data landscape to use lakehouses, consider pairing up with data experts at Blue Orange Digital. By using our expertise, you can rest assured that our sure-shot strategies and tailored approach can speed up success in data for you. Contact us now to get started with a free consultation.