www.theregister.com/2023/11/27/...t_databricks_data_platform/
"For example, Adidas has built a data platform around Databricks. The environment supports the global sportswear maker's development of machine learning models. It also supports BI workloads and the company has created an acceleration layer with the in-memory database Exasol.
Exasol CTO Mathias Golombek told The Register that the company was often brought in on projects where customers find their data platform is not supporting certain workloads with sufficient performance. "Customers like Adidas can have more than 10,000 BI users looking at dashboards which are constantly updated and consumed," he said. "You need a powerful acceleration layer and that's what we provide."
According to Exasol's market research, nearly 30 percent of customers suffer performance issues with their BI tools. "That means not enough people can access the BI dashboards or they are too slow or there are limits on the complexity of questions users can ask because of the underlying data system," Golombek said. Exasol product Espresso serves as a BI accelerator built on the company's in-memory columnar database with Massively Parallel Processing (MPP) architecture and auto-tuning capabilities.
Hyoun Park, CEO of Amalgam Insights, said that by renaming its platform and integrating GenAI features, Databricks was claiming to offer the same semantic context across all of a users' data while maintaining governance of intellectual property across the AI lifecycle. "This new product positioning indicates that it is no longer enough to simply put all of your data in one place and to conduct analytics on that data," he said.
Having come up with the lakehouse concept back in 2020, Databricks has sizable funding. A Series I VC round scooped up another $500 million in September for a nominal valuation of $43 billion. The cash pile could help the company define a "next generation term for where they see the next few years of development," Park said.
Nonetheless, the complexity of managing multi-node Spark clusters meant a third-party technology layer was needed to boost performance.
"Exasol has long been known for its speed in supporting analytics, based on in-memory MPP and auto-tuning," Park said. "High-performance analytics for structured data becomes increasingly challenging to support as data volumes increase and we are reaching an inflection point where data is starting to either outgrow or strain the complexity of managing multi-node Spark clusters.
"Although there are strategies to prioritize memory such as caching frequently used data, Exasol can be used as a tool to replicate structured Databricks data once there are no additional tactics to support faster queries without using up Spark cluster resources and administrative skills."
While Databricks and Microsoft are competing and collaborating to define a market for one-stop shop data platforms supporting BI, analytics, and machine learning in a single environment, organizations that require acceptable performance across thousands of impatient end users might need to shop elsewhere to get what they need. ®"
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