ELASTICSEARCH METABASE HOW TO
ELASTICSEARCH METABASE PDF
ELASTICSEARCH METABASE CODE
ELASTICSEARCH METABASE LICENSE
ELASTICSEARCH METABASE SERIES

Grafana and Metabase are both open source tools. 'Beautiful' is the primary reason why developers consider Grafana over the competitors, whereas 'Database visualisation' was stated as the key factor in picking Metabase. Grafana and Metabase are primarily classified as 'Monitoring' and 'Business Intelligence' tools respectively. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating. Metabase is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. But supports other data sources via plugins Metabase: An open-source business intelligence tool. It currently has rich support for for Graphite, InfluxDB and OpenTSDB.

ELASTICSEARCH METABASE SERIES

It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. Grafana is a general purpose dashboard and graph composer. Grafana: Open source Graphite & InfluxDB Dashboard and Graph Editor. Even you can get the ability of custom services from Helical Insight team directly at very affordable costs.Grafana vs Metabase: What are the differences? ML and NLP integration, thus allowing Google search like interface to create reports.UI driven workflow engine with data pipeline capabilities.

ELASTICSEARCH METABASE LICENSE

Perpetual license option with distribution Support.

Perpetual license option without distribution.

Subscription license option with distribution.

Subscription license option without distribution.

Completely responsive and can be accessed from any device any screen size.

– Ability to add your own chart, your own chart customization, your own DBfunction, your own aggregate function.

ELASTICSEARCH METABASE CODE

– Placeholders to allow injecting of your own custom HTML, CSS, SQL, Groovy code etc.– API support for each and every frontend function, thus allowing to automate things and build custom UI also. In February 2022 the company introduced a preview of its support for vector embeddings, enabling the Elasticsearch technology to act like a vector database, which is a critical part of the AI.Caching and pagination for better performance.An ability to specify exporting config with specific config (like paper size, width, header, footer, custom code on export etc)

ELASTICSEARCH METABASE PDF

Various kind of exporting options like pdf jpeg png excel csv etc.Create map based analytics, infographs, reports etc with filters, drill down, drill through etc - Canned report allowing multipager document kind of reports like invoice, salary slip, bank statement etc.Support of more than 30+ visualization options and map.

Dashboard designer module with drag drop interface to create dashboard.

Self service drag drop interface allowing a no code platform.

Row level data security based on the user, role, org, profile etc thus ensuring people see only their own data.

User role management: Providing 4 layers of security like organization, role, user and profiles.

Embedding and SSO : Embedding options like iframe, webservices, object tag, embed tag etc - Single Sign On support with options like CAS, LDAP, AD, ADFS, JWT, oAuth etc.

DataSource: Support of variousdb including RDBMS, Columnar DB, Flat files, NoSQL db like MySQL MariaDB Postgrss SQLserver PrestoDB Vertica DynamoDB Mongo etc.

ELASTICSEARCH METABASE HOW TO

Refer to this link to learn how to connect and use elasticsearch : ĭisclaimer: I am one of the cofounder of open source BI product Helical Insight ( )īelow some of the key features which we have are Metabase and redash have nowhere near the reach that Elastic have, and neither are a datastore as far as I know.Ī powerful OPENSOURCE alternative which might be worth looking at. Why don't people ever compare ES + kibana to the likes of metabase and redash? For example, you cannot join between indexes and they have made it clear that they do not plan to do this anytime in the near future. In fact it is a bit more limited due to its use-case. The thing is, Elasticsearch is not that different to MongoDB in terms of traditional DWH. Kibana is slowly becoming a very good and slick BI tool. Up until 1 or 2 years ago, it was not enterprise ready, but these days it has become much more polished. Then why don't use use an RDBMS or MPP database? If it is well structured, you do not need a NoSQL system such as Mongo/elastic.ĮLK stack is a very good tool stack which has been developing rapidly for the past few years. SQL and tools in that ecosystem seem to just be the norm.

Elasticsearch metabase