Powerful, Codeless Json based ETL for Elasticsearch

 Dynamically Integrate with SQL, NoSQL or Flat Files

JsonEDI's Declarative ETL Simplifies & Automates Data Integration

 Integrate Elasticsearch with SQL, NoSQL or Both

FREE Webinar on Declarative ETL and ​Elasticsearch

JsonEDI uses the declarative rapid development methodology. Declarative means the developer only specifies "what" should be done, not "how" it should be done. The "what" is business oriented requirements (i.e. the data model in a data dictionary format) and the "how" is technical or logistical ETL requirements. These have been automated or are preconfigured.

JsonEDI's endpoints can be any JDBC, REST, File, native DB library or bulk loading tool (if accessible in Java) or ESB.

More information

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Explore Use Cases with Elasticsearch & NoSQL

  1. Since Elasticsearch only can be searched in single index it is often required that data be enriched for other sources or merging of data from SQL and NoSQL source databases. JsonEDI ETL solves with a our declarative Json ETL solution. Learn how changes from either source can update a single index or multiple indexes in ES.
  2. In fully automated mode, JsonEDI can take SQL or NoSQL source database with document/form data and pivot out the data into tabular SQL tables or flat files per form. New forms created in MongoDB are automatically created as new tables or files. New Json elements are automatically added to tables/files as columns. Json subarrays are similarly split out into sub tables (i.e. normalization). Each node in the Json hierarchy is either pivoted or split based on normalization rules. This is all maintained and tracked in our data dictionary without needing to write or manage code. This is ideal to maintain a reporting database, ODS, datamart or staging to a data warehouse.

Presented By

Fred Zimmerman

Founder JsonEDI


MAY 24



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