Opensee vs. Other Solutions

Dive deeper into data. Click the tabs to discover how Opensee Enterprise is pushing the boundaries of self-service data analytics for financial institutions and how we stand out from traditional solutions.

In Memory
OLAP
limitations

  • Limited scalability: restricted to either select a few days of history or granularity of data
  • Segment data sets in different cubes, no cross data sets queries possible
  • Expensive hardware (RAM)
  • Complex to run on large distributed infrastructures
  • Not fault tolerant

Opensee key differences

  • On-disk deployment
  • Fault tolerant and scalable at low cost (just add commodity hardware)
  • Allows for full granularity with any depth of history
  • Enables cross data sets queries
  • Still matches "In-memory" performance
  • Total user autonomy with all required functionalities
  • Ease of integration in FI architectures
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Traditional and Cloud data warehouse limitations

  • Generalist approach: technology not focused on financial institutions specific use cases
  • Lower performance
  • Cloud-based solutions = data hosting regulation issues
  • Pure technology stack: not ready to use by business end users
  • Not designed to support what-if scenarios

Opensee key differences

  • On-premise, cloud, multi cloud or hybrid deployment
  • 100X faster even against high performance and popular data warehouses on finance specific use cases
  • Integrated FI features (on the fly currency conversion …)
  • Native support of what-if scenarios
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Data warehouse accelerator limitations

  • Requires pre-calculated aggregates at multiple levels of dimensional hierarchies
  • Inability to meet data model adjustments and maintenance requirements
  • Requires to guess pre-determined usage only
  • Multiplies the size of original data set
  • Not adapted to real-time data insertion and updates - cache invalidation problem
  • Slow access to raw data

Opensee key differences

  • High performance without pre-aggregations
  • Maintain aggregation flexibility, access to raw level, ability to insert real time and access this data instantaneously
  • Allows to add columns with simple database filters
  • Total user autonomy with all required functionalities
  • Ease of integration in FI architectures
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Opensee Enterprise in depth

Discover our innovative three layer turnkey solutions

ClickHouse

Our analytical and query engine is based on ClickHouse, released in open source by Yandex

icon open source

Open-source big data analytical column oriented database

Used by deep tech companies with real time analytics needs on very large data sets

icon processot

Leverage most recent technologies

(SSD, Multi-processors, SIMD etc...)

clickhouse illustration

Benchmarks

ClickHouse benchmark for managing general calculations

Greenplum 18.48
Vertica 6.14
ClickHouse 1
18x

faster than Greenplum

6x

faster than Vertica

ClickHouse vs Redshift for managing VAR like calculations

Altinity averages vs Redshift - 08 Sept.2020
ClickHouse m5.8xlarge
arrayReduce
Redshift Dc2.8wlarge
x2join
Data size 6.38Gb 40.06Gb
Query 1 0.72 s 4.00 s
Query 2 1.04 s 112 s
Query 3 1.05 s 200 s
Query 4 0.45 s 6.6 s
100x

faster queries

6x

Compression

Functionalities

Use powerful tools to extract, aggregate, analyse and visualise data in full autonomy

illustration functionalities
Data Aggregation

Data Aggregation

Low Code Query API

With our Abstraction Data Model Layer, interact only with the business data model (and not the physical one), through an intuitive and easy-to-use Rest API

Python UDF functions

Implement very easily user-defined functions in Python leveraging Pandas and Numpy, in a fully integrated development environment. Deploy them on the server in one click and benefit from the built-in parallelisation. Big Data with Excel Macros style

Dynamic Columns (Groups), FX Conversion

As a user, benefit from on-the-fly meta-data such as dynamic groups (e.g. Tenor Group, Customer Groups...) or currency conversions

Single data model and cross-domain data set

Build a single data model and host multiple data sets in one single instance, whatever the size; join and query cross-domain data sets

Advanced Data Management

Advanced Data Management

Streaming and Rest Data ingestion API

  • Generic API for high-volume/high-speed client data insert/update/delete
  • Support for every standard file format (CSV, Protobuf, Parquet, ORC format, Avro, and many others). Include some parameterizable ETL components (Star-to-Star model...etc)

Collaborative Versioning

Thanks to our unique git-like versioning system, benefit from a full audit of any change, user based changes, what-if, collaborative versioning...

Auto-cube

Create a complete ready-to-use Opensee data set from any user provider data file with a single click

Real Time and Limit Management

Easily ingest vast quantities of data in real time, automatic recompute and live limit monitoring on any dimension

Data Visualisation

Data Visualisation

Opensee dedicated Desktop UI

All functionalities including dashboarding for expert users

Opensee dedicated Web UI

Light client, easy to use with capability to extract widgets

Connectors to standard BI

No need to migrate from current software, instead use our connectors to Excel, Tableau and other standard BIs

Clients proprietary UI

Leverage our easy-to-use Rest API and connect Opensee to your standard UI platform

Information System Integration

Information System Integration

On-Prem, Cloud or Hybrid

Choose to deploy Opensee on-premise, on a public cloud or build your Opensee hybrid environment

User authentications and entitlements

  • Grant users access to features using 15 different policies
  • Define permissions at any granularity level and drive them through your authentication and entitlement system (Active Directory, Basic, OAuth1, OAuth2, LDAP, OpenID …)

Auto Deployment Tool and Kubernetes

  • Automatic DataBase Cluster Configuration with replication, Automatic Cluster One-command deploy with our CLI, that also supports reconfiguration or resizing
  • Dockerized, Binaries (Bare Metal) or Kubernetes Installations are supported

Detailed Monitoring

  • Access in real-time all user activity with duration and used resources
  • Monitor in real-time all application linked events, including all errors and relevant debug information (cross system full stack-traces)

Open and see our use cases

illustration use cases
Fusing financial and technology expertise, Opensee
addresses critical use cases

Investment Bank. Asset Manager. Hedge Fund.

Get ahead of your big data challenges, break down data silos and manage your risk more efficiently. Explore the full depth of all your data without any limitations on size or scale and navigate through your sensitivities, Mark to Futures, VaR...

Specific use cases:

Market Risk • Counterparty Credit Risk • Front Office Real Time Risk

Wholesale & Retail Credit. Loan & Deposit. Liquidity & IRRBB.

Unlock the value of multiple data sets to manage the bank’s resources and produce regulatory reports with consistency, speed and granularity.

Specific use cases:

Wholesale Credit • Loan & Deposit • Liquidity & Risks on Banking Books

Order books. Trade data.

Improve your market intelligence and execution process. Leverage the depth and granularity of all your trade data (public and private) in any direction.

Specific use cases:

Trade data • Order data (RFQ, bid/orders...)

Ask our experts

Contact us and find out how we can help with your big data challenges. Click here to ask for a demo.

Let's talk!