Our Data Systems Division assumes the role of your expert Big Data, Cloud, and Machine Intelligence partner. This team was designed to serve three core purposes:

  1.  Overcome every roadblock in the way of our clients reaching their goals, through designing and deploying custom software solutions within Cloud Computing Environments.
  2. Empower Client and other StackPros teams with data and analyses that are otherwise indecipherable.
  3. Let the machines do the work they are best at, bringing exponential efficiency improvements to our clients' optimization and decision-making through the integration of Machine Learning, Deep Learning and Artificial Intelligence systems.

Our Data Systems team primarily works within Google Cloud Platform (GCP), Amazon Web Services (AWS) and Salesforce Cloud environments. Some products worked on include:

Relational and Non-Relational Databases

There are advantages and disadvantages of using relational vs. non-relational databases, dependent on the existing data architecture of your business. StackPros can help with:
Relational Databases such as IBM DB2, Google Clould SQL, Amazon RDS (Redshift), Microsoft SQL, MySQL, Oracle, PostgresSQL, Microsoft Azure SQL Database, etc.

Non-Relational (NoSQL) Databases such as MongoDB, IBM Domino, HBase, Cassandra, Amazon SimpleDB, Google Cloud Datastore, Azure DocumentDB, etc.

Computation, Programming & Developer Tools

Tools used to house applications and scripts within containers and virtual machines, including:
Cloud Application Processing Tools such as Google Compute, Amazon EC2, Microsoft Azure Virtual Machines, etc.

Big Data Tools

Tools used for the management and manipluation of Big Data environments, including:
Spark, Hadoop, Google BigQuery, Google Cloud Dataflow, Google Cloud Dataproc, Amazon Redshift, Amazon Kinesis, Amazon EMR, etc.

Data Visualization, Realtime Dashboards

Tools and platforms used to create Data Visualizations and Realtime Dashboards, including:
Tableau, Looker, Qlikview, PowerBI, Domo, etc.


Machine Learning, Deep Learning & Artificial Intelligence

Tools used for the enablement and management of Machine Learning, and more specific Deep Learning and AI applications, including:
Google Cloud Machine Learning Engine, Google Cloud Datalab, Amazon Machine Learning, TensorFlow, Scikit Learn,

Data Storage, Data Warehousing, Data Lakes, etc.

Tools and platforms used for scalable data storage environments, purpose-built to serve requirements for data intake, real-time data streaming, etc., including:
Cloud storage tools such as Google Cloud Storage, Microsoft Azure Data Lake, Azure Block Blobs, Amazon S3, etc.

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