Data is rapidly becoming an organization’s most valuable asset. Data capital, or the value derived from data, has become the ultimate source of differentiation. But being able to handle on all this data can be difficult, especially when the amount of data stored by organizations is immense – and is projected to triple by 2025 to 5.5ZB. So as the data landscape grows, it can limit your ability to leverage all your data and manage it effectively.
Originally, data warehouses were how data was managed, but they are currently becoming obsolete as they are not able to meet new needs such as performing artificial intelligence or storing different types of data. Data warehouses have had a great run in being vital in decision support and business intelligence, but they were conceived only for handling structured data. Data lakes then emerged to fill the gap of mainly running machine learning on unstructured data, but they lacked key data warehouse features such as supporting transactions or enforcing data quality.
And that is what has set the stage for the need of data lakehouses to arise and become increasingly vital for businesses, as they combine the best features from both data warehouses and data lakes. It is now possible to support more diverse workloads including data science, machine learning, analytics and business intelligence tools with direct access to all types of data (unstructured, semi-structured and structured).
Data lakehouses are created by adding metadata, caching and indexing layers on data lakes, achieving optimized access for data science and machine learning tools. These metadata layers, like the open-source Delta Lake, offer data management features like ACID-compliant transactions, which ensure high data reliability and integrity, and sit on top of open data formats, like Paquet. This makes it easy for data scientists and engineers to access all their data, as they can use known tools like Spark and Kafka.
Some data lakehouses are currently hosted on the cloud, but businesses could benefit from the flexibility of executing these on-premises or colocation. For example, a data lakehouse on-premises can reduce data transfer fees, increase data security and allow independent scaling of compute and storage.
There’s no doubt your data is going to grow. Don’t limit your ability to leverage all your data and manage it effectively. A data warehouse or a data lake alone can’t help you compete in the future. Explore data lakehouses!
Learn how you can bring your business to the next level and create value from your data by visiting our Analytics Solutions page.
