Eli Singer, CEO of Jethro, provider of an acceleration solution for interactive business intelligence (BI) on big data, says that despite its promise, BI on big data is fundamentally broken because SQL-on-Hadoop remains too slow.
“Today’s approach to BI on big data is not working,” Singer said in a statement Tuesday. “Under the SQL-on-Hadoop hype lies monumental failure rates with existing approaches.”
Singer’s answer is Jethro 3.0, the newest version of Jethro’s enterprise solution, a SQL query engine for Hadoop that combines indexing architecture with “auto-cubes,” aggregated cubes generated from usage patterns. Data engineering tasks — like pre-aggregating tables, manually building cubes and keeping up with new and changing applications — tend to be costly and labor-intensive.