Businesses need efficient architecture to store and structure big data for business intelligence (BI) and machine learning (ML).
Presently, more than 44 zettabytes of data exist in the digital space, with 70% of the world’s data being user-generated. Every day, digital users produce nearly 2.5 quintillion bytes of data. Here, data warehouses and data lakes present their pivotal utility in helping businesses manage big data. These solutions can store data from disparate sources for data analysis, processing, and insights reporting, which is the core of business growth and operations.
Data lakes are more advanced than data warehouses in terms of serving as storage repositories for large amounts of raw data. They are highly scalable and can hold huge data volumes in their natural format. However, growing a business with high-quality insights requires advanced functionalities such as transaction support and data quality enforcement—a more advanced data solution that can check all the boxes.
The answer? A data lakehouse. A combination of a data warehouse and a data lake, a data lakehouse brings together the best of both worlds in terms of the data practices of an organization.
Let us understand the fundamentals of a data lakehouse and its features in detail.
What is a data lakehouse?
A data lakehouse stores unstructured data like a data lake and has management capabilities similar to a data warehouse. It is the only data architecture that stores all data— unstructured, semi-structured, and structured—in the data lake while providing the data quality and governance standards of a data warehouse. The data and tools are implemented together to form a larger system.
A data lakehouse makes big data more accessible and consumable. Its open data management architecture is flexible, scalable, and cost-efficient. Businesses can store and analyze large amounts of data to enable Business Insights and Machine Learning with a purposeful impact, making more data-driven business decisions.
A data lakehouse works by:
- Applying the data warehouse model on the data lake
- Adding metadata layers and a new high-performing SQL query engine on data lakes
- Streamlining data governance and analytical processes
- Building aggregations and metrics on the data model to be used by BI tools
Businesses can build a data lakehouse by aggregating data from all channels and unifying them for better access and organization.
Key features of a data lakehouse
Lakehouse provides the ability to specify your schema and enforce it. This helps ensure that the data types are correct and that required columns are present, preventing bad data from causing data corruption.
The data is stored in Apache Parquet format enabling Delta Lake to leverage the efficient compression and encoding schemes that are native to Parquet.
A data lakehouse supports real-time streaming and ingestion from data sources, enabling smart, real-time reporting.
It is ideal for diverse workloads such as data science, SQL analytics, and machine learning for business analytics.
Unlike data lakes, a data lakehouse has transaction support (ACID properties) for the concurrent reading and writing of data.
It supports schema with data governance and audit mechanisms seamlessly via the Unity Catalog.
Big data is continuously changing. Lakehouse enables you to make changes to a table schema that can be applied automatically without the need for cumbersome DDL.
Flexible, Cost-effective Compute & Storage
It separates compute functions from storage resources to make storage more flexible, cost-effective, and scalable.
Why use a data lakehouse?
As mentioned earlier, businesses generate about 2.5 quintillion bytes of data comprising different raw data types. Organizations need massive repositories to store their unstructured data, and data lakehouse turns out to be an ideal option. Businesses can leverage the capabilities of a data lakehouse to derive intelligence from unstructured data, which is critical for business operations and forecasts. In addition, a data lakehouse simplifies the structuring and organization of data in the warehouse.
Instead of maintaining multiple systems for data management, organizations can create a data lakehouse to bring their data analytics frameworks under one roof at a much lesser price than warehouses.
Leveraging data lakehouses can have several advantages:
- Simplified schema, with reduced data movement and redundancy
- Direct access to analytics tools
- Higher control over data security, metrics, and role-based access
- Easy storage scaling by separating compute and storage
By building a data lakehouse, businesses can unify their data management system for more effective BI analytics. This solution can enable a smooth end-to-end process for data aggregation from curated sources.
The combination of data warehouses and data lakes (data lakehouses) gives the best of both worlds that helps organizations achieve digital transformation faster. This combined architecture supports the processing and streaming data in real time to ensure the data is ready for analysis.
Data lakehouse can easily stream the data directly into the lakehouse, which minimizes latency and augments data recency. Also, eliminating the need for a separate warehouse for BI cuts down the software licensing, computing infrastructure, and maintenance costs for the organization. Therefore, data lakehouse is crucial to maximizing the value of your organization’s data.
PreludeSys empowers organizations with exceptional data governance and advanced analytics services for improved data management to evaluate business performance. With PreludeSys, businesses obtain a consolidated view of multi-source data for holistic analysis.
PreludeSys’ data management and digital transformation services help businesses boost efficiency through data visualization and modeling. Create insightful strategies to drive business growth, flag problems, and discover new revenue streams with our integrated solutions.