Pragma Analytics Software Suite
Once deployed and adapted to your needs, the PASS software suite offers a powerful graphical front-end interface using the latest Web development technologies. This animation gives you some elements of the graphical capabilities of this interface, including the capacity at the representations of the data.
We invite you to follow the rest of this presentation to understand our solution.
Pragma Analytics Software Suite design
- The data ingestion layer ensures the formatting, the enrichment and the standardization of the data format,
- The broker message ensures the exchange of data between the ingestion layer and the backend,
- The backend provides storage, consultation, and the desired level of resiliency for the data.
Finally, relying on the API of the backend, we find:
- Data consultation tools and dashboards organization,
- Security use cases,
- Business workflow optimization.
For each of these reference functions, we have validated software that we can assemble in order to meet a set of specifications. Our expertise can also be proposed to validate your working hypotheses.
The modules we currently use are either open source modules like PMACCT (Netflow and sFlow data), or modules developed by us in golang (SNMP processing, CDR Charging Data Records of voice networks)
Message broker et inter process communication
To ensure communication between the modules of the same layer, we favor the use of the Zero MQ library. The data is exchanged in binary or via a simple format like message pack. The use of the JSON format can be a problem of performance at the time of its serialization / de-serialization.
In order to meet these needs, we use two types of backend: Druid but also clickhouse.
These two solutions are quite close to each other. Both are based on a model called “columnar db” or model OLAP. Druid allows automatic temporal aggregation that clickhouse does not allow. Clickhouse offers flexibility and an SQL interface that Druid does not always allow. Depending on your specific use cases we will direct you to the best solution for your needs. You can also consult our existing use cases.
As always, we cannot work without the use of the very robust PostgreSQL database which will be in charge of processing the meta-data of our big data solutions. This type of database is also used by the Frontend of the PASS solution.
Big data, the icing on the cake ! We take advantage of this section to clarify what big data means for Pragma Innovation. We select our solutions so that they are scalable horizontally. Also, a big data solution must have the possibility to be reduced to a single server with some TB of data. It must be able to evolve to PetaB and support our customers in their growth. It is therefore simple and inexpensive to evaluate the PASS stack.
Frontend and dashboards
This front end has a driver for the Druid backend but it is also able to interface with all systems offering an SQL interface. This frontend connects to the SQL database through the SQLAlchemy library.
If an existing tool has to be taken into account, it will be possible to consider an integration. For example, a graphical tool such as Grafana has plug-in for our backend.