Data Platform
"Data Platform Services refer to Cloudaeon's suite of tools, technologies, and practices designed to handle the collection, storage, processing, and analysis of data, often at large scale and in real-time."
Amol Malpani (CTO, Cloudaeon)
Why choose us?
Cloudaeon's comprehensive data platform services, especially in the realm of AI/ML engineering, provides businesses with the infrastructure and tools they need to harness the power of their data, from raw ingestion to actionable insights or intelligent machine-driven operations.
If you're thinking about how to make AI drive better business outcomes, start with a free readiness assessment and let us know when you're ready to start the conversation.
Key Use Cases
- Business Analytics: Gaining insights into business operations, customer behavior, etc.
- Real-time Data Processing: Handling streaming data for real-time analytics or operations.
- Machine Learning: Developing, training, and deploying machine learning models.
- Data Warehousing: Storing historical data for reporting and analytics.
- Big Data Analytics: Processing and analyzing vast amounts of data.
Key Benefits
- Data Silos: Consolidating data from various sources into a unified platform.
- Scalability: Handling growing amounts of data and processing needs.
- Real-time Needs: Addressing requirements for real-time data processing and analytics.
- Data Quality: Ensuring data is clean, reliable, and ready for analysis or machine learning.
- Integration Challenges: Providing seamless integrations between various data tools and systems.
What We Offer
- Data Analysis and BI Tools: Tools for querying, visualizing, and gaining insights from data.
- Data Ingestion: Tools and processes to collect and import data from various sources.
- Data Storage: Including relational databases, NoSQL databases, data lakes, and data warehouses.
- Data Processing: Tools and frameworks to process and transform data (e.g., Apache Spark, Apache Kafka).
- Data Integration: ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes to move data between systems and transform it.
- Data Governance and Security: Ensuring data quality, metadata management, data lineage, and security.
- Orchestration: Managing, scheduling, and automating data workflows and pipelines.
- Machine Learning Platforms: Integrations or tools that facilitate the development, training, and deployment of machine learning models.
How We Work
- Define Objectives: Understand the business needs, such as analytics, reporting, or machine learning.
- Data Collection: Set up data ingestion mechanisms to gather data from various sources.
- Data Storage: Store the ingested data in appropriate storage systems based on the data type and use-case.
- Data Cleaning and Transformation: Use ETL or ELT processes to clean, transform, and integrate data from different sources.
- Data Analysis: Use BI tools to query and visualize data, extract insights, or further process data for machine learning.
- Model Development: In the case of AI/ML objectives, use the processed data to develop and train models.
- Deployment: Deploy machine learning models or integrate insights into applications or dashboards.
- Monitoring and Maintenance: Regularly monitor data quality, pipeline health, and model accuracy, making adjustments as needed.
Managed Services
- Infrastructure Management: Handling the setup, scaling, and maintenance of data storage and processing infrastructure.
- ETL and Data Integration Services: Setting up, managing, and optimizing data integration processes.
- Data Quality Assurance: Implementing processes and tools to monitor and maintain data quality.
- Machine Learning Infrastructure: Setting up and managing platforms for ML model development, training, and deployment.
- Security and Compliance: Ensuring that data storage, processing, and AI/ML operations are secure and compliant with relevant regulations.
- Monitoring and Alerting: Tools and services to monitor the health and performance of data pipelines, storage, and ML models.
- Backup and Recovery Solutions: Ensuring data durability and implementing disaster recovery solutions.
- Consultation and Training: Offering expert guidance on best data practices, emerging technologies, and training for the client's team.
Readiness Check
In 10 minutes, get a score to assess your Readiness & Maturity. You'll get a clear score to help your identify areas of improvement.
Getting Started
If you are ready to engage with us and would like do dive deeper into the subject, go ahead and book in a Discovery Workshop with our Practice Leads.