Elastic announced Elasticsearch Query Language (ES|QL), its new piped query language designed to transform, enrich and simplify data investigation with concurrent processing.
ES|QL enables site reliability engineers (SREs), developers and security professionals to perform data aggregation and analysis across a variety of data sources from a single query.
Over the last two decades, the data landscape has become more fragmented, opaque, and complex, driving the need for greater productivity and efficiency among developers, security professionals, and observability practitioners. Organizations need tools and services that offer iterative workflow, a broad range of operations, and central management to make security and observability professionals more productive.
“ES|QL reflects our ongoing commitment to invest in innovations that empower enterprises to find the answers and insights they need faster,” said Ken Exner, chief product officer at Elastic. “We’re redefining how we interact with data to transform the foundations of decision-making. ES|QL empowers organizations to unlock the true potential of their data, bringing agility and efficiency to the forefront of their operations.”
Elasticsearch Query Language key benefits include:
- Delivers a comprehensive and iterative approach to data investigation with ES|QL piped query syntax.
- Improves speed and efficiency regardless of data’s source or structure with a new ES|QL query engine that leverages concurrent processing.
- Streamlines observability and security workflows with a single user interface, which allows users to search, aggregate and visualize data from a single screen.
"ES|QL is going to change everything,” said Amreth Chandrasehar, director of ML Engineering, Observability and Site Reliability Engineering at Informatica. “Once released, it will be our primary query expression language.”
ES|QL is currently available as a technical preview. The general availability version, scheduled for release in 2024, will include additional features to further streamline data analysis and decision-making.
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