Canopus Infosystems offers comprehensive data engineering and data analytics solutions that help enterprises in extracting value from data through complete data engineering and analytics processes. We are committed to harnessing business data to drive business insights, process improvement, innovation, and automation.
We help enterprises solve their data challenges, improve end-user satisfaction, and provide actionable business strategies based on intelligent insights. Our data engineering team analyzes structured, semi-structured, and unstructured data with the right technology, processing tools, and approach. In addition, we provide complete data lifecycle management services, replacing our clients’ costly and siloed in-house data infrastructure and turning big data pipelines into robust systems prepared for agile business analytics.
We accelerate the full life cycle of data management covering data ingestion, data quality, catalog, data provisioning with a focus on improving time to value and self-service analytics for different user personas.
Our team of expert Data engineers & Data analysts helps to build a robust strategy across cloud, tech, and data governance pillars; guide to choose the right technology and modernize your data platforms with creating ETL data flows, Data Architecture, Data Ingestion Pipeline Design, Hadoop Information Architecture, Data Modeling & Data Mining, Machine Learning, and Advanced Data Processing Techniques with the following key skills:
- Data Engineering: Data Warehousing, ETL Pipeline, Amazon Glue, Informatica, PySpark
- Big Data: Hadoop Ecosystem, Hive, Spark, Cassandra, Kafka
- Machine Learning: Linear Regression, Logistic Regression, Decision Tree, SVM, Naive Bayes, KNN, K-Means, Random Forest and Gradient Boosting
- Deep Learning: Computer Vision, Tensorflow, Keras, Theano, Caffe, PyTorch
- Programming Language: R, Python, SQL, Scala, Java
- Database: MongoDB, Cassandra, Oracle, MySQL, SQLite, NO SQL, RDBMS
- Cloud Technologies: AWS (Amazon EC2, Amazon S3, Amazon RDS, Amazon Elastic Load Balancing), Azure (Cognitive Services, Databricks, HDInsight)
- Data Visualization: Tableau, PowerBI, QlikView
Our Data Engineering services deliver significant business advantages to:
- Leverage online/e-commerce data to fuel sales initiatives
- Streamline operations to improve operational efficiency through data analytics
- Inform business decision-making using Predictive Analytics
- User satisfaction with Self-service analytics model for business users and analysts
- Enhancing Data Security and diagnosing the causes of data breaches
- Leveraging operational data to improve efficiency; developing ML/AI use cases to improve sales and operations; accessing distributor data to get better supply chain visibility, identify gaps, and improve replenishment rates
Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses.
data analytics predominantly refers to an assortment of applications, from basic business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics. In that sense, it’s similar in nature to business analytics, another umbrella term for approaches to analyzing data — with the difference that the latter is oriented to business uses, while data analytics has a broader focus. The expansive view of the term isn’t universal, though: In some cases, people use data analytics specifically to mean advanced analytics, treating BI as a separate category.
Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals — all with the ultimate goal of boosting business performance. Depending on the particular application, the data that’s analyzed can consist of either historical records or new information that has been processed for real-time analytics uses. In addition, it can come from a mix of internal systems and external data sources.
Canopus Infosystems is a Data Engineering, Data Science & Analytics company based in India. We provide Data Science Products, Solutions, and Services to clients and industries including Logistics, Healthcare, Manufacturing, Marketing, Retail, Financial Services etc.
Supporting companies that prefer to rely on a trusted outsourcing partner in data analytics instead of deploying an in-house solution, we render data analysis services. In this case, our consultants just need access to our customer’s data sets, the infrastructure and resources for the analysis are on our side.
- Building data pipelines and ETL or ELT
- Experience with complex distributed computing
- Ability to work with structured and unstructured data
- Deployment of data science models
- Experience with data science applications
- Experience with continuous integration working with Docker and Kubernetes
- Build and scale large batch data pipelines and real-time ETL pipelines
- Gather business requirements and implement data processes
- Design and support data lakes and data marts
- Work with data scientists to deploy machine learning models
- Troubleshoot models in a production environment to ensure accuracy
Machine Learning App Development
We build advanced Machine learning and Deep learning algorithms with Artificial Intelligence that enables customer centricity by giving insight into data, which improves productivity and increases the depth of customer engagement and predictive modeling to make big decisions.
Machine learning is a set of artificial intelligence methods aimed at creating a universal approach to solving similar problems. Machine learning is incorporated into many modern applications that we often use in everyday life.