The amount of data has increased a lot and its complexity has also increased along with it, making it organizations challenging to handle large datasets. Organizations were largely dependent upon data warehouses initially for the storage of structured and unstructured data that is used mainly for business intelligence and reporting process. The data explosion in the past years has forced them to shift towards data lakes. As the amount of data increased, organizations have started looking out for new solutions that is capable of managing these large datasets, storing, analyzing and gaining value from them. And this search has now ended up in data lakes.
Data lakehouse is a combination of both data warehouse and data lakes, that contains features of these two architectures. The previous data architecture data lake was highly scalable and efficient. Since the data were stored in raw format, by time it will get messy and had become difficult for business organizations to store and analyze data. So, this all ended up in the adoption of a new technology, Data Lakehouse.
What is Data Lakehouse?
As the name suggests, Data Lakehouse is a new data architecture that combines features of data warehouse and data lake into a single entity to overcome the constraints of each. The evolution of how corporate companies and data centers manage increasing volumes and types of data has led to the creation of data lakehouses. Administrators can handle a wide range of raw data with data lakehouses, which have an interface and data governance comparable to traditional data warehouse administration.
Data Lakehouse enables organizations to store all kinds of data, including structured, unstructured, or semi-structured, in a single repository. Organizations can derive instant insights from data lakehouses through end-to-end streaming facility. Data lakehouse also provides the administrators the access to read or write operations on data. It also provides schema support to organization for enabling data governance. The new data architecture also enhances the query speed through indexing and data compaction. Transaction support for atomicity, concurrency, isolation, and durability (ACID) are delivered through data lakehouses.
Major Benefits of Data Lakehouses
Major benefits of data lakehouses includes:
- ETL Elimination – By filtering and converting data from your existing data lake into the destination schema, users can load it into your data warehouse using ETL/ELT tools. The data lake will be immediately linked to the query engine using data lakehouse, removing the need for extra ETL.
- Minimal Data Redundancy – Data redundancy is eliminated using Data Lakehouse. Data may be spread across different tools and platforms, such as cleansed data in a data warehouse for processing, metadata in Business Intelligence (BI) tools, temporary data in ETL tools, and so on. These data must be constantly maintained and checked to avoid data integrity concerns. Handling raw data using a single tool, organizations can avoid the problem of data redundancy.
- Enabling Easy Data Governance – The operational complexity associated with maintaining data governance across different technologies can be reduced by using a data lakehouse. However, by using a single data lakehouse tool, organizations can centralize data governance.
- BI tools are directly linked – Direct access to some of the most popular business intelligence tools is provided by data lakehouse (Tableau, PowerBI, etc.). It eventually minimizes the time it takes to go from raw data to visualization by orders of magnitude.
- Reduces Cost – The storage cost for data warehouses and data lakes are high and data needs to be processed at multiple places in a single time. The new data architecture can store these data in a much cheaper cost using as S3, Blob, etc., while comparing with data warehouses and data lakes.
As more businesses see the advantages of combining unstructured data with artificial intelligence (AI) and machine learning (ML), the data lakehouse strategy is expected to gain traction. It’s a step up in analytics maturity from the combined data lake and data warehouse approach, which was previously thought to be the sole choice for companies that wanted to keep their traditional BI and analytics workflows while simultaneously shifting to smart, automated data efforts.
Conclusion
Data lakehouses serve as a connecting point for data management stakeholders in a company. The dream for a single solution for managing structured and unstructured data remains alive and well, despite the fact that it is a newer notion. Several well-known suppliers are expanding their data lakehouse capabilities in this emerging sectors to provide better data management solution. A Lake House architecture, based on a portfolio of purpose-built services, will enable users to swiftly deliver insight from the data to all customers and also allows organizations to plan for the future by adding new analytic methodologies and technologies as they become available.
