Data Lake Solutions
ProCogia harnesses its expertise in data lake best practices to construct scalable, secure, and highly efficient data repositories that cater to the diverse analytics needs of modern businesses.
Our data solutions are powered by the following technologies
Data Lake Experts
Data Lake Success Steps
Strategic Planning & Governance
Define clear objectives for how the data lake will support analytics and data science. Establish governance from the start — setting policies for access, quality, classification, and security to keep data organized, secure, and compliant.
Architecture & Data Management
Design a scalable, secure architecture that handles diverse data types and workloads, from batch to real-time. Strong metadata and data management practices ensure information is searchable, accessible, and reliable for analysis.
Optimization & Culture
Continuously monitor and optimize for performance and cost. Equip teams with training and tools to foster a data-driven culture, empowering users to maximize the lake’s value and drive innovation.
Our Solutions
Discover how our team of Data Engineering specialists can turn your data problems into data solutions.
Our data migration services involve the extraction of data from the source system, its transformation to match the target system and data ingestion into the target system. The result is a seamless and successful migration process, including:
- Data extraction, ensuring that all data from source systems is relevant and accurately captured
- Data transformation that matches your data with the target formats and standards, allowing ProCogia to then perform data cleaning, mapping, conversion and validation to ensure data integrity
- Data loading, where the transformed data is then aligned with the target system whilst accounting for data validation and error handling, for a smooth transition.
Our data mesh approach promotes domain-oriented teams, utilizing a federated data infrastructure. It involves:
- Forming cross-functional teams responsible for specific data domains
- Productizing data with ownership and quality
- Implementing a decentralized architecture for flexibility and scalability in data management.
We utilize both FinOps and MLOps models for various functions, including:
FinOps
- Monitoring cloud resource usage
- Cost allocation
- Cost optimization.
MLOps
- Data preparation and feature engineering
- Model deployment and monitoring
- Scalability and automation.
ProCogia specializes in dimensional modeling within our data warehouse designs to organize and structure data in a way that supports efficient querying and analysis. This involves processing data as “facts” (numerical measurements) and “dimensions” (descriptive), resulting in a robust and efficient structure for data analysis and reporting.
We process and assess the accuracy, completeness, consistency and integrity of data to ensure it meets the desired standards and requirements of an organization. Our Data Engineering team implements various techniques and practices to identify data issues, including:
- Data proofing
- Data cleansing
- Data validation rules.
We implement tools to oversee, track and receive notifications about the health, performance and anomalies within a data system. Our partners include AWS, Azure and other cloud technologies, providing us with the tools needed for revitalizing a company’s data ecosystem.
- Data proofing
- Data cleansing
- Data validation rules.
FAQs
These Data Pipeline FAQs highlight the importance of considering efficiency, scalability, data quality, automation, monitoring, and security in the design and operation of data pipelines. Achieving excellence in these areas ensures that data pipelines can support the dynamic needs of modern businesses effectively.
Get in Touch
Let us leverage your data so that you can make smarter decisions. Talk to our team of data experts today.
