There are several different types of analytics tools that can be used with Elasticsearch. Here are some of the most common ones:
1. Aggregations: Aggregations enable you to group and summarize data based on specific criteria, such as date ranges, geographic regions, or product categories. Elasticsearch provides a wide range of aggregation types, such as sum, average, min, max, and percentiles.
2. Metrics: Metrics enable you to calculate statistical measures on the data, such as counts, averages, and standard deviations. Elasticsearch provides a wide range of metric types, such as cardinality, geo bounds, and value count.
3. Data visualization: Elasticsearch provides integration with data visualization tools such as Kibana, which enables you to create interactive dashboards and visualizations that can be used to explore and analyze data.
4. Machine learning: Elasticsearch provides machine learning features that enable you to automatically detect anomalies, classify data, and perform predictive analysis on large volumes of data.
5. SQL: Elasticsearch provides a SQL query interface, which enables you to use SQL statements to query and analyze data stored in Elasticsearch.
6. Graph: Elasticsearch provides a graph exploration tool, which enables you to explore the relationships between different entities in your data and uncover hidden patterns and trends.
Overall, Elasticsearch provides a wide range of analytics tools that can be used to perform complex data analysis tasks. By choosing the right tool for the job, you can gain insights and value from your data and make data-driven decisions that can help drive your business forward.