Our Big Data Processing
Data Extraction
Commencing a big data project involves f identifying pertinent data sources, encompassing both structured and unstructured data derived from diverse systems, sensors, social media, and other digital platforms. Following this, we proceed to extract the data and accurately prepare it to ensure it’s ready for further processing and analysis.
Data Transformation
We undertake the crucial task of data transformation to ensure its compatibility with the desired format for analysis. This process encompasses vital steps such as data cleaning, integration, and enrichment, all aimed at elevating the quality and utility of the data, resulting in improved insights and usability.
Data Loading
The next step is loading it into a powerful big data platform that facilitates seamless accessibility and analysis. This platform is purpose-built to handle these volumes of data, offering exceptional scalability and availability for your needs. With the aid of cutting-edge big data technology, we ensure that your data is efficiently managed and readily accessible for comprehensive analysis.
Data Visualization/BI Analytics
Then our big data experts can start analyzing and visualizing it. Big data as a service (BDaaS) includes advanced visualization tools and business intelligence platforms, enabling our clients to gain insights from their data and make data-driven decisions.
Machine Learning Application
We also apply machine learning algorithms to the data, which allows us to discover patterns and relationships that may not be apparent through traditional analysis methods. Our machine learning models are designed to continuously learn from new data, providing ongoing big data analysis.
Technologies and Tools We Use
- Business Intelligence (BI)
- Data Collection and ETL
- Warehouse, Storage and Management
MS PowerBI
Google Looker
IBM Cognos
QuickSightue.js
Tableau
AWS Glue
GCP DataFlow
GCP DataFusion
AWS SageMaker
AirFlow
Azure Databricks
AWS Data Pipeline
MS SQL Server
MongoDB
Google BigQuery
Google Spaner
IBM DB2
Amazon Redshift
MySQL
PostgreSQL
Cassandra
Industries
Retail companies use big data services to collect, clean, and process large amounts of data to understand customer behavior better, optimize supply chain operations, and personalize marketing campaigns.
Within the retail domain, organizations heavily depend on robust and sophisticated data engineering services to efficiently acquire, thoroughly cleanse, and process vast quantities of data.
By leveraging advanced data engineering techniques, retail enterprises can unlock a wealth of actionable intelligence, optimize their operational strategies, and foster meaningful connections with their customer base, resulting in heightened business performance and sustainable growth engagement.
We work closely with the manufacturing sector, utilizing data-driven insights to streamline operations, improve efficiency, and elevate product quality, empowering manufacturers to make informed decisions and drive innovation.
Leveraging data analysis techniques to facilitate precision farming, optimize yield, and enhance crop management, enabling farmers to maximize productivity and sustainability.
Optimizing logistics, inventory management, and demand forecasting, enabling businesses to enhance operational efficiency, reduce costs, and precisely meet customer demands.
Contributes to optimizing routes, improving fleet management, and enhancing overall transportation logistics, ensuring timely and efficient delivery of goods and services.
Enables businesses to gain actionable insights, personalize campaigns, and enhance customer targeting and engagement, resulting in improved marketing effectiveness and customer satisfaction.
Support risk assessment, fraud detection, and personalized financial recommendations for customers, enabling them to make informed decisions and enhance overall financial well-being.
Focused on improving patient care, optimizing operations, and enabling data-driven research and diagnostics, ultimately leading to enhanced healthcare outcomes and efficiency.
Data analysis capabilities to aid in policy-making, enhance public services, and improve decision-making processes, contributing to efficient governance and citizen welfare.
Ensuring data integrity, consistency, and quality across various systems and platforms, enabling businesses to have reliable and accurate data for informed decision-making and streamlined operations.
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Retail
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Manufacturing
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Agriculture
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Supply chain
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Transportation
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Marketing
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Financial Services
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Healthcare
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Government agencies
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MDM (Master Data Management)