Data-Centric Solutions & Analytics

Solutions Architecture
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Design full-stack architecture designs that include, but not limited to:
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Cloud (i.e. Azure) technology
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Back-end and front-end infrastructure and data science environments
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Unlock data from existing systems to create an enterprise platform. Further, synchronize the existing systems of record with new components and capabilities based on agreed-upon use cases
Data Engineering
From a data ingestion perspective, our team will ingest, cleanse, transform, normalize, and aggregate data from the source systems into a final data repository (to include any new data sources)
Develop and maintain APIs and API management to access data to support analytics and visualizations. Includes net new integrations to existing source applications
Set-up infrastructure environment to support use cases
Data Science / Advanced Data & Analytics / Business Intelligence
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In practice, our team has expertise in developing predictive models, statistical analysis, data mining, and text mining approaches that use unstructured and semi-structured data to predict future events.
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In addition to providing a future-looking method, we offer methods tailored to reporting (KPIs, reporting), data visualization designs, scorecards, and ad hoc querying using structured data
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Our team of experts has in-depth competency to discover and exploit data assets at the heart of use cases’ data-driven, metrics-driven approach using cutting edge tools and servers like below:
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Data discovery and wrangling tools like Altryx, Trifacta, and traditional ETL
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Data analysis and machine-learning like MatLab, SPSS, SAS, R, TensorFlow, SciPy/Panda, Jupyter Notebooks
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Visualization and reporting like Tableau, Qlik, Cognos, SAP Business Objects, Microsoft PowerBI
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Big data stores, compute and pipeline tools like HDFS, Hadoop, Apache Spark, Cassandra, IBM Spectrum LSF
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Operational data/content stores and caches like Apache Ignite, Redis, SQL Server, Oracle RAC, MySQL, MongoDB,
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Data governance like MDM, Informatica CDE
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