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Databricks is one of the reliable data analytics companies working on the mission to combine the power of data+AI in one single platform to help businesses solve some of the most challenging problems using the data.
The company proposes a data analytics solution to the world name Data Lakehouse, which can satisfy the diverse need for data from small to complex businesses.
Let’s take a deep look at the Data Lakehouse to gain complete exposure to it:
Data Lakehouse is built on an open and reliable data foundation which makes it very capable of handling all types of data without losing its hand on the security and governance approaches.
The combination of the data lakes and data warehouses’ impeccable capabilities solves the data problem and provides support for openness, flexibility, and machine learning.
Data Lakehouse is one of the best solutions that take care of the transaction support every scale and domain of the business requires. Utilizing SQL provides support for ACID transactions to ensure consistency across multiple parties.
Data Lakehouse is equipped with DW schemas such as star/snowflake schemas to ensure they are compatible with the schema enforcement and evolution. Moreover, the solution has robust governance and auditing mechanisms to bring the best level of data integrity.
Data Lakehouse implies the business intelligence on the source data. This results in reducing the operating cost of the data and finding valuable insights from it. So, Databricks reduces staleness, improves recency, reduces latency, and reduces operational costs associated with having to maintain two copies of data in a warehouse and a data lake.
The storage and compute components are separate clusters, so these systems can scale to accommodate more users and larger data sets simultaneously.
Some tools and engines can access the data directly via the API, including machine learning and Python/R libraries, whose formats are open and standardized, like Parquet.
Among the data types supported by the lakehouse are pictures, video, audio, semi-structured data, and text, all of which are needed for a wide range of new applications.
Data science, machine learning, SQL, and analytics are all included in this category. Multiple tools may be required to support all of these workloads, but the data repository underlies them.
More and more organizations are starting to understand the value of using unstructured data alongside AI and machine learning, making the data lakehouse approach increasingly popular. For organizations wanting to migrate from legacy BI and analytics workflows to smart, automated data initiatives and continue to stay on their analytics journey, it’s a step up from the combined data lake and data warehouse model.