What is Data Lakehouse
Businesses without access to or collection of data, face challenges in achieving growth. On the other hand, companies with vast amounts of data may waste resources if they fail to leverage that valuable information. It's counterproductive to spend money storing data without actually making use of it.
A data lakehouse is a data architecture that combines the benefits of both data lakes and data warehouses. It is designed to address the shortcomings of traditional data architectures and provides a more flexible and scalable solution for managing and analyzing large volumes of data.
Here are top 10 reasons why you need a data lakehouse:
1. Unified Data Platform
A data lakehouse serves as a comprehensive platform for storing and processing structured and unstructured data collectively. It enables the consolidation of data from diverse sources into one centralized repository. Importantly, this doesn't necessitate the migration of existing data from databases or object storage to the platform. Instead, it can act as a system that virtualizes your data, enhancing accessibility for data scientists, data engineers, AI/ML engineers, or data analysts to expedite the creation of BI dashboards and develop more robust AI models.
2. Flexibility
In contrast to conventional data warehouses, which often demand data structuring before ingestion, a data lakehouse accommodates the storage of raw, unstructured data. This adaptability is pivotal for managing diverse data types and adapting to evolving data requirements. As various data storage technologies emerge for different data types such as time-series, transactional, and unstructured data, a data lakehouse empowers you to seamlessly integrate your data.
The flexibility extends to avoiding vendor lock-in, granting you the freedom to choose technologies and solutions that precisely align with your needs. This flexibility empowers enterprises to opt for best-in-class solutions from different vendors rather than being confined to a single provider.
3. Scalability
Data lakehouses are designed to scale horizontally, enabling organizations to efficiently handle growing volumes of data. Not only TB or even PB level of data, but also data variety in various format such as images, videos, logs and many more. This scalability is essential as businesses accumulate more data over time. Through a data lakehouse you should be able to scale in term of data storage and computing.
4. Cost-Effective Storage
Utilizing cost-effective storage solutions for raw, unprocessed data, data lakehouses offer organizations a more efficient approach to managing storage costs compared to traditional data warehouses. The consolidated accessibility provided by a data lakehouse allows effective offloading of warm and cold data from expensive databases to more economical cloud object storage.
Therefore, the ability to query and perform offloading processes directly within the data lakehouse becomes crucial. Many enterprises incur unnecessary costs through data duplication, but with access to a unified data lakehouse platform, data duplication can be minimized, consequently reducing associated expenses.
Additionally, avoiding vendor lock-in enables enterprises to negotiate pricing and terms with multiple vendors, fostering a competitive environment that leads to cost savings and enhanced value for procured services and products.
5. Real-Time Analytics
Data lakehouses for data facilitate real-time data processing, enabling organizations to analyze and extract insights from data as it is generated. This capability is essential for prompt and well-informed business decision-making. The data lakehouse platform must have the capability to access the primary data source in real-time, including connections to time-oriented data sources like transactional databases or time series databases.
6. Data Quality and Governance
A properly structured data lakehouse incorporates functionalities for overseeing data quality and governance. This guarantees the precision, uniformity, and adherence to regulatory standards of the data, effectively tackling issues concerning data integrity and compliance. Your organization should possess the capability to trace data lineage, monitor user access to data, and record actions taken with the data.
Additionally, it should have the ability to revert to accurate data states in the event of undesired alterations. Crucially, your enterprise must implement access control management, allowing the centralized establishment of access policies across all distributed data sources.
7. Schema Evolution
Data lakehouses provide robust support for schema evolution, enabling modifications to data structures without impacting existing data. This capability is especially advantageous in dynamic environments where data schemas undergo frequent changes. It is essential for your data lakehouse platform to be flexible in accommodating schema evolution, and having a data catalog that can automatically capture the most recent data structure is crucial. This eliminates the need for manual creation of a data catalog, which not only consumes significant resources but also introduces confusion in case of inaccuracies in data structure.
8. Advanced Analytics and Machine Learning
Data lakehouses serve as the cornerstone for cutting-edge analytics and machine learning. Their capacity to store and handle extensive datasets in a versatile fashion facilitates the creation and implementation of machine learning models for predictive analytics. It is imperative for your data scientists and AI/ML engineers to have access to a unified and authoritative data source. Furthermore, timely access to the accurate data necessary for constructing their AI models is equally imperative.
9. Cost-Effective Data Processing
Many organizations incur substantial expenses associated with data warehouse engines, wherein query costs escalate with each volume of data queried. By separating storage and compute resources, data lakehouses allow your enterprise to scale processing resources based on demand, optimizing costs associated with data processing.
10. Time-to-Insight
By storing raw data and enabling on-demand processing, a data lakehouse minimizes the duration from data ingestion to actionable insights, thereby enhancing the overall time-to-insight for data-driven decision-making. An effective data lakehouse platform should facilitate instantaneous access to your data, expedite processing, and accelerate its usability. This eliminates the need to log in to various data storage environments, download, and store data repeatedly. The result is a significant time-saving by eliminating the need for cumbersome and time-consuming ETL processes.
Conclusions
If you're encountering challenges and are interested in establishing a data lakehouse, you've come to the right place! watsonx.data is a reliable data lakehouse platform that not only addresses these issues but also delivers the values mentioned above.
Read more about watsonx.data here:
https://www.ibm.com/data-lake
https://www.ibm.com/products/watsonx-data
Muz
Ecosystem Technical Enablement Specialist | Data & AI
IBM APAC Ecosystem Technical Enablement Team
#watsonx.data