DATA ANALYTICS
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We help modernize data warehousing using your on-prem solutions like Oracle, Teradata or MS SQL Server or by migrating your existing data warehouse solutions to Cloud native solutions such as AWS Redshift, Azure Synapse SQL Pool, GCP BigQuery, and Snowflake. We cover Telecom, Finance, Banking, Retail, Insurance, Transportation and Oil & Gas Industries. This primary service is focused on the developing and maintaining basic components of the Data Warehousing based on best practices including; Core and Semantics Data Models (LDM / PDM), data mapping, solution architecture & design, ETL/ELT, BI visualization, testing as well as project implementation. Our experts have an impeccable record in implementing data warehouses in different industries, and can confidently provide consultancy and assistance to organizations in delivering such complex services.
This primary service is focused on the design of a Data Warehousing using enterprise fit industry specific data model(s) and enable them for a single version of truth. Our consultants have vast experience on the following design services; – Solution Architecture – Framework Design – Data Modelling – Semantic Modelling
Our data engineers build tool and agnostic complex data pipelines to integrate data from multiple sources across the enterprise into enterprise data warehouse. We specialize in tools such as Informatica, Talend, SSIS, and DataStage
Our Integration Testing and Deployment services are designed to guarantee that the EDW installations implemented by us fulfill the customer’s quality standards and performance criteria. Quality Assurance is provided through System and Integration Testing, which is performed utilizing state-of-the-art and industry-standard procedures. Our services are based on a framework which enables organization for continuous improvement plan. The service portfolio offers certain advance analytics / Machine Learning capabilities for specific business improvement opportunities to bring the predictive nature of Data Quality in the environment.
Once data is available in the DWH through our implementation service, Our BI Visualization experts uses your data to generate meaningful insights and present it in the right way for helping you make informed decisions. We specialize in BI tools such as Tableau, MicroStrategy, Cognos, and Business Objects.
Develop smarter, context-sensitive analytics, which incorporates external, unstructured and environmental data. Our data architects implement Big Data Lakes, Infrastructure and Roadmaps to steer your analytics program.
We specialize in building data lakes and ensuring that they don’t turn into data swamps. Our data lakes are structured, containing multiple data tiers for data organization. Raw data is ingested and moved between these tiers where it is curated at each step thereby maximizing its business value for the organization while minimizing the cost of storage.
Whether it is a full-scale cloud migration or intelligently automating processes with machine learning, it requires a robust foundation of data extraction, integration, and governance. Data engineering entails numerous tasks, such as building and maintaining tools, infrastructure, and frameworks. Our consultants have a vast experience to develop advanced solutions that drive businesses forward.
Our data engineers, modelers and data architects build tool agnostic complex data pipelines to migrate, unify, and consolidate data from multiple sources across the enterprise into a central data lake both on-cloud and on-prem.
Replicate data between systems efficiently and securely. We provide consultancy and services to manage data transfer between platforms.
Seamlessly migrate from legacy systems to systems backed by emerging technologies.
Integrate structured, unstructured and semi-structured data using a combination of ETL and ELT techniques to make all data available in the right format at the right place.
Architect batch and real-time data integration solutions to provide timely data insights to the organization.
Comprehensively organize data, correcting inaccuracies, duplicates, and corruptions to remove noise and allow for accurate analysis and interpretation
Extracting features from data is a vital prerequisite for training machine learning models.