Welcome Partners and Customers

Join us for a data revolution

Explore our vendor-neutral approach

Joining as partner

We are a data governance house. We deal with organisational data issues. Our work consists of making a clear separation of ownerships between data strategy and ICT strategy. 

Our data discovery approach identifies data governance improvements which require better data infrastructure, data security and technology.

In many occasions, data infrastructure and integration requirements cannot be handled by customers and our data discovery outcome recommends technology solutions that integrate well together and meet customer expectations.

Therefore, our data governance approach becomes a funnel to make old technology obsolete and recommends better open source or commercial solutions to our customers.

Such transformational projects require business teams, data teams, technology teams and system integrators to work towards a connected enterprise.

As from what you read, we are a vendor-neutral company that provides opportunities to technology houses and system-integrators to join us in our data governance venture.

 

Fede L. Nolasco, Founder

Technology choice for customers

Regarding technologies, we document all identified data processes, interoperability of systems with your existing ICT and security teams. Most importantly, we assess the costs and benefits of connecting the data of your enterprise.

If a data solution needs better integrated tools, our recommendations of open source or commercial tools for our customers fall under the following categories.

  • data security tools
  • data modelling tools
  • data dictionary tools
  • database systems
  • data marts
  • data warehouses
  • data lakes
  • data integration tools
  • data quality tools
  • business intelligence tools
  • document management tools
  • metadata repository tools
  • enterprise data flow tools
  • enterprise search tools
  • data science tools
  • UI/UX tools

Become a partner


Drop us a mail to apply for partnership or request a meeting with our staff.

We use the following technologies

Dataedo

Data dictionary repository

Dataedo helps your team and organisation to create and maintain the Data Dictionary for existing databases. You can import schema from SQL Server, Oracle, MySQL, PostgreSQL, MariaDB, Azure SQL DB, Amazon, Google Cloud SQL, Percona MySQL, Amazon Redshift, Snowflake and Azure SQL Data Warehouse.

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PieSync

Data integration

You can use PieSync for syncing your contacts between cloudapps. PieSync offers a simple, affordable solution to connect your apps together and ensures that your contact data reaches all of your connected business apps. It does this by using Intelligent Two-way Syncing.

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MyIP.io

Data security

MyIP.io offers an Irish dedicated IP address. A dedicated IP can be white listed on your company firewall giving you access to all your private content via a secured connection. Our VPN partner secures your internet connection to guarantee that all of the data you're sending and receiving is strongly encrypted and secured from prying eyes.

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Microsoft R Open

Data science

We work with Microsoft powerful R distribution. R is a statistical language with enhaned capabilities to boost reproducibility and performance. The data analysis platform utilises multithread Intel Math Kernel Library (MKL) for matrix manipulation and features a high-performance default CRAN repository that makes it easy to provide one-point-in-time R packages. It is also integrated with MS SQL server and Power BI.

Microsoft R Open is available for installation on Linux, Mac, and Windows platforms. It is supported by RStudio Desktop and RStudio server editions.

RStudio

Data science

Our data scientists use RStudio in conjunction with Microsoft R Open, an open source statistical language to analyse data. RStudio is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management.


RStudio is available in open source and commercial editions and runs on the desktop (Windows, Mac, and Linux) or in a browser connected to RStudio Server or RStudio Server Pro (Debian/Ubuntu, RedHat/CentOS, and SUSE Linux)

Python

API integration and Data Science

We use Python for Web development, data science, data visualisation and API scripting. We work with web frameworks such as Django and Flask. These web frameworks help us create server-side code in Python. These web frameworks make it easy to build backend logic.

We also use Python to script machine learning algorithms to automatically detect patterns in a given input. We design a variety of applications such as recommendation systems, face recognition, voice recognition systems among other applications. We are familiar with machine learning libraries such as scikit-learn and TensorFlow.

PowerBI

Business Intelligence

Power BI is a cloud-based business analytics service that gives you a single view of your most critical business data. Monitor the health of your business using a live dashboard, create rich interactive reports with Power BI Desktop and access your data on the go with native Power BI Mobile apps.

With Power BI, you ask questions in natural language and get the right charts and graphs as your answer. Power BI apps include dashboards, reports, and datasets that provide every user with a personalized view of their business metrics that matter most.

PowerBI standard-based REST API integrates your application or service with Power BI. Integrating helps you deliver your solutions faster while focusing on your core value. PowerBi offers integration with R and other scripting languages.

Tableau

Business Intelligence

Tableau provides BI solutions for all kinds of industries, departments, and data environments. Following are some unique features which enable Tableau to handle diverse scenarios.

Speed of Analysis − As it does not require high level of programming expertise, any user with access to data can start using it to derive value from the data.

Self-Reliant − Tableau does not need a complex software setup. The desktop version which is used by most users is easily installed and contains all the features needed to start and complete data analysis.

Blend Diverse Data Sets − Tableau allows you to blend different relational, semi structured and raw data sources in real time, without expensive up-front integration costs. The users don’t need to know the details of how data is stored. Tableau offers integration with R.

Qlik Sense

Business Intelligence

With the Associative engine at its core, and new augmented intelligence capabilities supporting the user, Qlik Sense helps anyone discover insights that query-based BI tools simply miss, driving data literacy for all skill levels. Freely search and explore across all your data, instantly pivoting your analysis when new ideas surface. Take advantage of machine suggestions and automation powered by the Qlik cognitive engine. And get total flexibility with a multi-cloud data analytics platform that supports the full spectrum of BI use cases – ideal for any analyst, team or global enterprise.


Interactive analysis, without boundaries
Ask any question and quickly explore across all your data for insight, using global search and interactive selections. All analytics update instantly with each click, no matter how deep you go, furthering analysis or pivoting your thinking in new directions. There is no limit to exploration and no data left behind.

Hackolade

Schema design

We work with Hackolade to deal with complex document models. Hackloade combines the flexibility of the document model with true data conformance and validation capabilities. Teams can benefit from the ease of development that the document model offers, while still maintaining the strict data governance controls that are critical for applications in regulated industries, through the MongoDB v3.2 validator or v3.6 JSON Schema support.

Define once object definitions that can be re-used in multiple places. A library of definitions standardizes content and insures consistency. It also simplifies the work of data modelers, so maintenance can be performed in one place and be automatically propagated to all places where the definition is referenced.

Hackolade applies some Entity Relationship theory to non-relational databases to represent denormalized data in a user-friendly way.

DBWrench

schema design

We work with DBWrench to design data models for multiple relational database systems including: Microsoft SQL Server, Oracle, PostgreSQL and MySQL.

DbWrench has a visual interface with great database design features including column templates to add common columns, quickly naming and enforcing naming conventions.

DBWrench offers forward and reverse engineering. You can generate DDL SQL scripts easily or reverse engineer an existing database in seconds.

DbWrench generates create and update scripts. You can execute full scripts or select partial scripts to run and you can execute scripts in the safety of transactions.

DBWrench has a SQL editor and you can create abbreviations for common commands and entity names. You can display query results in tables or as text.

pgModeler

Schema design

Easily create and edit database models with simple and intuitive interface. The forms indicate which fields must be filled in order to provide the correct generation of SQL code.

Built over Qt framework, pgModeler can be compiled under Windows, Linux and macOS. The build scripts are easily configurable in order to resolve specific dependencies on each system.

Missing some functionality? Feel free to implement it! Use the plug-in development interface and access all pgModeler's classes facilitating the creation of additional features without change a single line of the core code.

Design once and export to multiple versions. With its dynamic code generation pgModeler is capable to export the designed models to different versions of PostgreSQL (currently 9.x and 10.x).

MongoDB

Database system

MongoDB is one of the most popular open source document stores. It does also work as Key-value store. It operates on most popular OS platforms such as Linux, OS X, Solaris and Windows. MongoDB is a schema-less database.

The MongoDB Connector offers Read-Only SQL query support for BI. Its API is proprietary and uses JSON. MongoDB offers scripting support for languages such as Go, Scala, Python and R.

On the server-side, MongoDB offers JavaScript support. MongoDB is a distributed database and uses sharding as well as works with a master-slave replication system. MongoDB has MapReduce capabilities.

It uses also transaction concepts such as multi-document ACID transactions with snapshot isolation. Furthermore, it supports concurrency, durability and as well as has in-memory capabilities.

PostgreSQL

Database system

PostgreSQL is a widely used open source RDBMS system. Its primary database model is a relational DBMS. It serves also as document store and key-value store.

PostgreSQL is available on a wide range of operating systems including: Linux, OS X and Windows. PostgreSQL has a schema and offers also XML support and secondary indexes. SQL query support is also available.

API is supported through streaming API for large objects as well as ADO.NET, JDBC and ODBC standards. Scripting support languages include .Net, C, Java, JavaScript, R and Python.

Partitioning methods include by range, list and by hash. PostgreSQL does not support MapReduce. Its transaction concepts are ACID supported. Furthermore, it offers, concurrency, durability and fine-grained access rights according to SQL-standard.

Elasticsearch

search and analytics engine

Elasticsearch is a distributed, open source, RESTful modern search and analytics engine based on Apache Lucene. Its primary database model serves as search engine. Its secondary database model serves as document store.

Elasticsearch is available on OS X, Linux and Windows platforms. It is a schema-free database with secondary indexes as well as a SQL-like query language.

Its API is supported through a JAVA API as well as RESTful HTTP/JSON API. Most common scripting languages include: Java, JavaScript, PHP, Ruby, Python and R. Server-side scripts are available, and its distributed system uses sharding and a solid replication system. Furthermore, elasticsearch supports concurrency, durability and has in-memory capabilities.

MS SQL

Database system

MS SQL primary database model is a relational DBMS. It does also support document store, graph DBMS and key-value store.

MS SQL runs mainly on Linux and Windows platforms. Its database model comes with a data schema, offers also XML support and takes care of secondary indexes.

MS SQL has an excellent SQL query interface as well as includes support for C++, Java, JavaScript, Python and R.

MS SQL allows to run a major amount of server-side scripts including: transact SQL, .NET, R, Python and on SQL 2019 also Java. Tables can be distributed through federation. It has full ACID support as well as concurrency, durability and in-memory capabilities. It offers fine-grained access rights through SQL standard.

MySQL

Database system

MySQL is a widely used open source relational RDBMS system. In addition, the database can also be used as document store and key-value store.

MySQL runs on various operating platforms including: Linux, OS X, Solaris and Windows.

MySQL comes with a data schema, it has XML support as well as provides secondary indexes. An excellent SQL query interface provide support for simple and complex SQL queries.

A proprietary native API is supported. Programmers can access SQL objects from ADO.NET, JDBC and ODBC standards. Most common supported scripting languages are: C, C++, Java, JavaScript, Python and R.

Partitioning is carried out through horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric. In addition, is uses master-master and master-slave replication. Furthermore, MySQL offers full ACID support, concurrency, durability as well as in-memory capabilities.

Amazon Redshift

Cloud Data Warehouse

Redshift is a large-scale data warehouse service for use with business intelligence tools. Redshift’s primary database model is a relational DBMS. As secondary database model it is also used as key-value store.

Redshift is based on PostgreSQL and operates on a hosted operating system. Redshift provides data types, data scheme and XML support. The secondary indexes in Amazon Redshift are restricted.

Redshift has full SQL support and its API supports JDBC and ODBC standards. User-defined function can be scripted in Python. It does not make use of triggers and does not support MapReduce.

Redshift uses sharding for partitioning as well as comes with a proper replication system. It uses Immediate Consistency and provides full ACID support.

Furthermore, if offers concurrency, durability and in-memory capabilities with fine grained access rights according to SQL standards.

Snowflake

Cloud Data Warehouse

Snowflake’s primary database model is a relational DBMS. As secondary database model it is also used as key-value store.

Snowflake operates on a hosted operating system. Snowflake provides data types, data scheme and XML support.

Snowflake has full SQL support and its API supports CLI Client, JDBC and ODBC standards. User-defined function can be scripted in Python and JavaScript. It does not make use of triggers and does not support MapReduce.

Snowflake uses partitioning as well as comes with a proper replication system. It uses Immediate Consistency and provides full ACID support.

Furthermore, if offers concurrency and durability with fine grained authorisation concept, user roles and pluggable authentication.

Microsoft Azure SQL Data Warehouse

Cloud Data Warehouse

MS Azure SQL Data Warehouse is an Elastic, large scale service leveraging a broad eco-system of SQL Server. Azure’s primary database model is a relational DBMS. As secondary database model it is also used as key-value store.

Azure operates on a hosted operating system. Redshift provides data types, data scheme but no XML support. It comes with secondary indexes.

Azure has full SQL support and its API supports ADO.NET, JDBC and ODBC standards. User-defined function can be scripted in Transact SQL. It does not make use of triggers and does not support MapReduce.

Azure uses sharding and horizontal partitioning as well as comes with a proper replication system. It uses Immediate Consistency and provides full ACID support.

Furthermore, if offers concurrency and durability with fine grained access rights according to SQL standards.

Google BigQuery

Cloud data warehouse

BigQuery’s primary database model is a relational DBMS. As secondary database model it is also used as key-value store.

Google BigQuery operates on a hosted operating system. It provides data types and data scheme but no XML support. There are no secondary indexes in Google BigQuery.

Google BigQuery has full SQL support and its RESTful API offers an HTTP/JSON API. User-defined function can be scripted in Javascript. It does not make use of triggers and does not support MapReduce.

BigQuery has no partitioning method as well as does not comes with a proper replication system. It uses Immediate Consistency and is designed for querying data and therefore is not full ACID support.

Furthermore, if offers concurrency, durability but does not have in-memory capabilities. Access privileges ( owner, writer, reader) are for whole datasets, not for individual tables.

Fivetran

Data Integration

Fivetran comes with pre-built, zero-maintenance connectors that replicate a database, a file, an event and app data to your data warehouse and continually update it — so anyone can leverage the power of centralized data. Without writing a single line of code, analysts can query and combine critical data sources for a holistic view of business performance.

Fivetran builds robust, automated pipelines with standardised schemas that free you to focus on analytics, not ETL.  You can add new data sources as fast as you need to, not waiting months to start using your data. Pipeline failure should never compromise an analytics project, so Fivetran iterates and battle-test pipelines, then monitor and maintain them 24x7.

Fivetran pipelines support data warehouses like BigQuery, Snowflake, Azure and Redshift and allow a data user to affordably query anything, at any time.