Technologies we use

We use state -of-the-art technologies for Big Data and applied Machine Learning, allowing us to create great solutions and services. 

#Big Data Engineering

Data lake, Lakehouse, Big Data

Utilize data lake concepts and lakehouse architecture to process Big Data

Meta-data-driven data integration pipelines

Build scalable meta-data-driven data integration pipelines

Stream Data processing

Stream Data processing

Best practice integration

Leverage best practices in continuous integration and delivery

Data models

Design and develop data models that provides insights in real-time

Data Extraction

Extract data from different sources, both relational and non-relational

Live Data Quality

Build pipelines for live data quality inspection

Visualisations

Build visualizations to communicate results effectively

What new technology does is create new opportunities to do a job that customers wants done!

#Machine Learning

Many Algorithms

Various machine and deep learning algorithms

Applied NLP in extraction and Classification

Applied Natural Language Processing in Feature extraction and Data Classification

Continuous predictive analysis

Continuous predictive analysis

Tunings

Model selection and hyper-parameter tuning

Federated learning

Federated learning

Feature extraction

to be filled in

Classification and regression models

Design and develop classification and regression models

Feature selection and explainability analyses

Feature selection and explainability analyses

Deployment of models

Deployment of models

Technologies

Applied by our Experts

  • Microsoft Azure and Amazon AWS Cloud
  • Apache Spark
  • Spark Streaming
  • Kafka
  • Python
  • Databricks
  • Relational databases (e.g., SQL Server, PostgreSQL, MySQL, Oracle, etc.)
  • Big databases (e.g., Hive, HBase, Kudu, MongoDB, Elasticsearch, etc.)
  • Visualization tools (e.g., PowerBI, Tableau, Grafana, etc.)

Technologies

Applied by our Experts

  • Microsoft Azure and Amazon AWS Cloud
  • Apache Spark
  • Spark Streaming
  • Kafka
  • Python
  • Databricks
  • Relational databases (e.g., SQL Server, PostgreSQL, MySQL, Oracle, etc.)
  • Big databases (e.g., Hive, HBase, Kudu, MongoDB, Elasticsearch, etc.)
  • Visualization tools (e.g., PowerBI, Tableau, Grafana, etc.)

We work closely with our clients 

We use a proven methodology when working on AI related products in order to ensure the delivery of stable and secure results.

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