new positioning

README.md

@@ -13,61 +13,154 @@
 - **[Forum](https://www.timescale.com/forum/c/promscale-and-observability)**
 - **[Blog](https://blog.timescale.com/tag/observability/)**
 
-<img alt="Promscale" src="images/promscale-logo.png" width="600px">
-
-**Promscale is the unified observability backend for metrics and traces
-powered by SQL and built on PostgreSQL and TimescaleDB**.
-
-It has native support for **Prometheus** metrics and
-**OpenTelemetry** traces as well as many other formats like StatsD, Jaeger and Zipkin
-through the OpenTelemetry Collector and is **100% PromQL compliant**.
-It's **full SQL** capabilities enable developers to **correlate metrics, traces and also
-business data** to derive new valuable insights not possible when data is siloed in
-different systems.
-
-Built on top of PostgreSQL and [TimescaleDB](https://www.timescale.com/) it inherits
-**rock-solid reliability, native compression up to 90%, continuous aggregates and the
-operational maturity** of a system that is run on millions of instances worldwide.
-
-For Prometheus users, Promscale provides a robust and highly scalable long-term storage
-system that is 100% PromQL compliant.
-✅ Promscale has consistently been one of the only long-term stores for Prometheus data that continue to maintain its top-performance, receiving
-[100% compliance test score](https://promlabs.com/promql-compliance-test-results/2020-12-01/promscale)
-each time (with no cross-cutting concerns) from PromLab's
-[PromQL Compliance Test Suite](https://promlabs.com/promql-compliance-tests/).
-
-For OpenTelemetry, Jaeger and Zipkin users, Promscale provides the ability to run deep analysis
-on traces using SQL while visualizing the distributed traces in Jaeger or Grafana.
-Install our lightweight [opentelemetry-demo](https://github.com/timescale/opentelemetry-demo)
-on your computer to quickly get a feel of what's possible. More details in
-[this blog post](https://www.timescale.com/blog/learn-opentelemetry-tracing-with-this-lightweight-microservices-demo/).
+<img alt="Promscale" src="docs/assets/promscale-logo.png" width="600px">
+
+**Promscale is a unified metric and trace observability backend for Prometheus,
+Jaeger and OpenTelemetry built on PostgreSQL and TimescaleDB.**
+
+Promscale serves as a robust and 100% PromQL-compliant Prometheus remote storage and as a durable and scalable
+Jaeger storage backend.
+
+Unlike other observability backends, it has a simple and easy-to-manage architecture 
+with just two components: the Promscale Connector and the Promscale Database (PostgreSQL with the
+TimescaleDB and Promscale extensions).
+
+Key features:
+* **Prometheus metric storage:** support for remote write, remote read, PromQL, metric metadata and
+exemplars.
+* **OpenTelemetry trace storage:** support for ingestion of traces through the OpenTelemetry Protocol
+(OTLP). Jaeger and Zipkin traces are supported via the OpenTelemetry Collector.
+* **Grafana integration:** query and visualize your metrics and traces using the PromQL, SQL and Jaeger
+datasources.
+* **Jaeger integration:** visualize traces in Jaeger by configuring Promscale as a Jaeger's GRPC
+backend storage. Use Promscale as the storage backend for the metrics required by the
+[Service Performance Management UI](https://www.jaegertracing.io/docs/1.36/spm/). No need for a
+separate system.
+* **Durable and reliable storage:** built on top of the maturity of Postgres and TimescaleDB with 
+millions of instances worldwide. A trusted system that offers high availability, replication, 
+data integrity, data compression, backups, authentication, roles and permissions.
+* **PromQL Alerts:** full support for PromQL alerting rules. You can reuse the Prometheus configuration
+that you already have.
+* **Multi-tenancy:** support for Prometheus multi-tenancy so you can restrict data access by tenant.
+* **Pick your query language:** PromQL for metrics and SQL for metrics and traces. With full SQL
+support together with TimescaleDB's advanced analytics functions, you can query and correlate metrics,
+traces, and business data to derive new insights.
+* **Flexible data management:** configurable default retention for metrics and traces as well as
+per-metric retention and APIs to delete metric series that are no longer needed.
+* **Downsampling:** increase the performance of long-term queries by downsampling metrics with PromQL recording
+rules and TimescaleDB continuous aggregates. Combine downsampling with per-metric retention to only keep the 
+data you need, reduce costs and accelerate performance.
+* **Out-of-the-box monitoring:** leverage the dashboard, alerting rules and runbooks built by the Promscale
+team to start monitoring Promscale since the first day following best practices from the team behind the
+product.
+
+[Learn more](https://docs.timescale.com/promscale/latest/about-promscale/#promscale-schema-for-metric-data) 
+about Promscale's architecture and how it works.
 
 <img src="docs/assets/promscale-arch.png" alt="Promscale Architecture Diagram" width="800"/>
 
-This repository contains the source code of the Promscale Connector. Promscale also requires that the Promscale Extension
-which lives in [this repository](https://github.com/timescale/promscale_extension) is installed in the TimescaleDB/PostgreSQL
-database. The extension sets up and manages the database schemas and provides performance and SQL query experience improvements.
+## Promscale for Prometheus
+
+Promscale provides Prometheus users with:
+
+* **A single-pane-of-glass across all Kubernetes clusters**<br/>
+Use Promscale as a centralized storage for all your Prometheus instances
+so you can easily query data across all of them in Grafana and centralize
+[alert management](https://docs.timescale.com/promscale/latest/alert/) and
+[recording rules](https://docs.timescale.com/promscale/latest/downsample-data/recording/).
+Use [multi-tenancy](https://docs.timescale.com/promscale/latest/scale-ha/prometheus-multi-tenancy/)
+to control who has access to the data for a Kubernetes cluster.
+
+* **Efficient long-term trend analysis**<br/>
+Use Promscale as a durable long-term storage for Prometheus metrics with a proven and rock-solid 
+foundation based on PostgreSQL and TimescaleDB with millions of instances worldwide. With
+[metric downsampling](https://docs.timescale.com/promscale/latest/downsample-data/)
+and [per-metric retention](https://docs.timescale.com/promscale/latest/manage-data/retention/#configure-data-retention-for-metrics)
+you can keep just the data you need for your analysis for as long as you need. This allows you
+to cut down the costs associated with using the same retention for all data in Prometheus and
+dramatically improves query performance for long-term queries.
+
+**Key features**: [100% PromQL-compliant](https://promlabs.com/promql-compliance-test-results/2021-10-14/promscale/),
+high availability, multi-tenancy, PromQL alerting and recording rules, downsampling, per-metric retention.
+
+If you are already familiar with PostgreSQL, then Promscale is a great choice for
+your Prometheus remote storage. You can scale to millions of series and hundreds
+of thousands of samples per second on a single PostgreSQL node thanks to TimescaleDB.
+
+To get started:
+1. [Install Promscale](https://docs.timescale.com/promscale/latest/installation/#install-promscale-with-instrumentation).
+2. [Configure Prometheus](https://docs.timescale.com/promscale/latest/send-data/prometheus/) to send data to Promscale.
+3. [Configure Grafana](https://docs.timescale.com/promscale/latest/visualize-data/grafana/) to query and visualize metrics from Promscale
+using a PromQL and/or a PostgreSQL datasource.
+
+## Promscale for Jaeger and OpenTelemetry
+
+Promscale supports ingesting OpenTelemetry traces natively, and Jaeger and Zipkin traces via
+the OpenTelemetry Collector.
+
+Promscale provides Jaeger and OpenTelemetry users with:
+
+* **An easy-to-use durable and scalable storage backend for traces**<br/>
+Most users run Jaeger with the in memory or badger storage because the two options for a more durable storage
+(Elasticsearch and Cassandra) are difficult to set up and operate. Promscale uses a much simpler architecture
+based on PostgreSQL which many developers are comfortable with and scales to 100s of thousands of spans per
+second on a single database node.
+
+* **Service performance analysis**<br/>
+Because Promscale can store both metrics and traces, you can use the new 
+[Service Performance Management](https://www.jaegertracing.io/docs/1.36/spm/) feature in Jaeger with Promscale 
+as the only storage backend for the entire experience.
+Promscale also includes a fully customizable, out-of-the-box, and modern
+[Application Performance Management (APM) experience](https://docs.timescale.com/promscale/latest/visualize-data/apm-experience/)
+in Grafana built using SQL queries on traces.
+
+* **Trace analysis**<br/>
+Jaeger searching capabilities are limited to filtering individual traces. This is helpful when troubleshooting problems once you know
+what you are looking for. With Promscale you can use SQL to interrogate your trace data in any way you want and discover issues
+that would usally take you a long time to figure out by just looking at log lines, metric charts or individual traces. You can see some
+examples in [our documentation](https://docs.timescale.com/promscale/latest/query-data/query-traces/) and in
+[this blog post](https://www.timescale.com/blog/what-are-traces-and-how-sql-yes-sql-and-opentelemetry-can-help-us-get-more-value-out-of-traces-to-build-better-software/)
+
+**Key features:** native OTLP support, high availability, SQL queries, APM capabilities, data compression, data retention
 
-For a detailed description of the initial architecture of Promscale for Prometheus metrics that covers
-some key design principles, please see [our design doc](https://tsdb.co/prom-design-doc).
+**Try it out** by installing our lightweight [opentelemetry-demo](https://github.com/timescale/opentelemetry-demo) with a single
+command. Check [this blog post](https://www.timescale.com/blog/learn-opentelemetry-tracing-with-this-lightweight-microservices-demo/)
+for more details.
+
+To get started:
+1. [Install Promscale](https://docs.timescale.com/promscale/latest/installation/#install-promscale-with-instrumentation).
+2. [Send traces to Promscale](https://docs.timescale.com/promscale/latest/send-data/) in OpenTelemetry, Jaeger or Zipkin format
+3. [Configure Jaeger](https://docs.timescale.com/promscale/latest/visualize-data/jaeger/) to query and visualize traces from Promscale.
+
+Also consider:
+
+4. [Configure Grafana](https://docs.timescale.com/promscale/latest/visualize-data/grafana) to query and visualize traces from Promscale
+using a Jaeger and a PostgreSQL datasource.
+5. [Install the APM dashboards](https://docs.timescale.com/promscale/latest/visualize-data/apm-experience/) in Grafana.
+
+## Documentation and Help
+
+Complete user documentation is available at https://docs.timescale.com/promscale/latest/
 
 If you have any questions, please join the #promscale channel on
 [TimescaleDB Slack](https://slack.timescale.com/).
 
+## Promscale Repositories
+
+This repository contains the source code of the Promscale Connector. Promscale also requires that the Promscale extension
+which lives in [this repository](https://github.com/timescale/promscale_extension) is installed in the TimescaleDB/PostgreSQL
+database. The extension sets up and manages the database schemas and provides performance and SQL query experience improvements.
+
 This repository also contains the source code for **prom-migrator**. **Prom-migrator** is
 an **open-source**, **community-driven** and **free-to-use**, **universal prometheus
 data migration tool**, that migrates data from one storage system to another, leveraging Prometheus's
 remote storage endpoints. For more information about prom-migrator, visit
 [prom-migrator's README](https://github.com/timescale/promscale/blob/master/migration-tool/cmd/prom-migrator/README.md).
 
 You may also want to check [tobs](https://github.com/timescale/tobs) which makes it very easy to deploy a complete
-observability stack built on Prometheus, OpenTelemetry and Promscale in Kubernetes via cli or helm.
+observability stack built on Prometheus, OpenTelemetry and Promscale in Kubernetes via helm.
 
-# Documentation
-
-Complete user documentation is available at https://docs.timescale.com/promscale/latest/
-
-## ✎ Contributing
+## Contributing
 
 We welcome contributions to the Promscale Connector, which is
 licensed and released under the open-source Apache License, Version 2.
@@ -76,3 +169,4 @@ Agreement](https://github.com/timescale/timescaledb/blob/master/CONTRIBUTING.md)
 applies as in TimescaleDB; please sign the [Contributor License
 Agreement](https://cla-assistant.io/timescale/promscale)
 (CLA) if you're a new contributor.
+