Data Search and Indexing

Data types in Elasticsearch is a feature that allows users to define the type of data stored in their Elasticsearch index, enabling efficient data processing and analytics.

Elasticsearch Type is a data structure for organizing and storing documents in Elasticsearch.

Elasticsearch Scope is a feature in Dremio that allows users to optimize, update, or migrate data from Elasticsearch to a data lakehouse environment efficiently.

Elasticsearch Painless is a scripting language designed for data processing and analytics in Elasticsearch, offering powerful features and flexibility.

Elasticsearch Mapping is a feature that defines the way data is indexed and stored in Elasticsearch, providing structured data processing capabilities for efficient search and analytics.

Elasticsearch Indexes is a data structure that organizes and stores large volumes of data for quick and efficient search and analysis.

Elasticsearch Document is a unit of data stored in Elasticsearch that consists of a JSON object containing one or more fields. It is used for data indexing, searching, and retrieval in Elasticsearch-based applications.

Physical index is a data management technique that optimizes data processing and analytics by organizing and accessing data efficiently.

Data Indexing is the process of organizing and cataloging data to improve data processing and analytics for businesses.

Explore the power of Query Federation: Advantages, limitations, and best practices. Discover how it fits in a Data Lakehouse for seamless data integration

Improve collaboration and decision-making while ensuring data quality and compliance. Learn more about data catalogs here.

Unlock the full value of your data with data discovery. Discover, understand, and analyze your data to make better decisions and solve business problems.