The ChaosSearch Data Lake Platform can now help data engineers reduce the cost, complexity, and time associated with accessing and analyzing complex nested JSON files.
JSON Flex allows organizations to store all their JSON content and analyze it, as if structured, at different permutations and nested levels.
ChaosSearch enables organizations to both keep and analyze all data efficiently and compactly without losing fidelity of insights.
Customers can use the patented Chaos Refinery to expand and explore all JSON data virtually and instantly on the fly, regardless of size or query structure.
JSON Flex eliminates the time sensitive process of data preparation, and individuals outside of IT can access critical insights that drive business decisions.
With JSON Flex, users can:
- Store “all logs” in full native format
- Index “all content” of a JSON file at once
- Create “index views” dynamically to explore JSON without limitations
“Until the introduction of JSON Flex, there hasn’t been a scalable analytics solution for complex, nested JSON files,” said Thomas Hazel, CTO, founder and chief scientist, ChaosSearch. “Organizations have been forced to rely on static, limited views of their data that ultimately lead to less valuable insights. We’re unlocking that data and democratizing it for the masses by delivering a platform that can automatically index and present all the data at once—making it easier to search, understand and leverage for business insights.”
ChaosSearch also announced a new capability to perform logical “relational inner joins” of log sources within the ELK stack.
Until now, correlating log sources required a pipeline to aggregate, de-normalize and ship logs from multiple sources into a single object store prior to ingestion.
With the ChaosSearch inner join capability, customers can ingest raw data from multiple log sources into multiple object stores and then perform relational inner joins in the Chaos Refinery where they can perform dynamic, conditional transforms and join multiple data sources into a single view (e.g., Index patterns, relational tables). This further reduces the need for data prep and pipelines and lets users experiment with log sources dynamically so they can create insights faster, according to the vendor.
For more information about this news, visit www.chaossearch.io.