endeca-to-elasticsearch-migration

To map Endeca Dimension schemas to Elasticsearch, you translate dimensions and properties into Elasticsearch index templates and field mappings. This process ports existing guided navigation data structures directly into Elasticsearch indices.

Migrating Forge and CAS pipelines to Elasticsearch involves replacing Endeca data ingestion with Elasticsearch indexing processes. You re-implement existing data transformation logic to feed documents directly into Elasticsearch indices.

Yes, you can port XMGR logic for Guided Navigation in Elasticsearch by translating the Endeca rule engine into Elasticsearch-based search rules. This enables dynamic ranking and complex navigation features natively.

Elasticsearch Multi-Select OR facets replicate Endeca guided navigation by allowing users to filter on multiple values within a single dimension simultaneously. You configure specific aggregation queries to achieve this behavior.

Yes, Elasticsearch supports dynamic ranking features like thesaurus and stopwords from Endeca through built-in text analysis and synonym filters. You configure these analyzers within index mappings to match Endeca search behavior.

To plan an Endeca to Elasticsearch migration sprint, you need to audit your Endeca deployment and draft an Elasticsearch mapping and migration plan. This provides the architectural blueprint for your initial development phase.