- Designs database structures, selecting the right formats and services for effective data management.
- Implements data modeling strategies to optimize accessibility, performance, and value.
- Enables engineers to develop and optimize relational and non-relational database infrastructures and models.
Data Engineering
Designing and building systems to collect, store, and model data.
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real-world applications.
Our teams are made of the top 1% of talent. When you partner with Factored, you gain dedicated teammates backed by Data Engineering's leading capabilities and cutting-edge strategies.
Data Solutions Design.
Data Processing, Integration & Orchestration.
DataOps & Governance.
Joining you with cutting-edge strategies
Data quality validation across multiple databases
We designed it using Great Expectations and advanced tech, we made the solution compatible with Snowflake, PostgreSQL, Oracle, and more.
This simplified operations, ensured data integrity, and improved ease of use.
This simplified operations, ensured data integrity, and improved ease of use.
Automated ETL processes for data ingestion and quality
We used cutting-edge solutions and a configuration-driven approach to efficiently handle data from multiple vendors.
This reduced manual labor, minimized errors, and sped up portfolio and investment analysis decision-making.
This reduced manual labor, minimized errors, and sped up portfolio and investment analysis decision-making.
An efficient, automated rule-based engine for clinical trial compliance
We did this using Python to manage rules and detect invalid records in clinical trials.
We improved compliance, minimized non-compliance risks, and ensured valid trial records.
We improved compliance, minimized non-compliance risks, and ensured valid trial records.
To accelerate your roadmap in:
Data Storage & Analytics Design
Data Integration & Distributed Computing in Streaming and Batch Processing
- Processes real-time data flows using distributed computing and streaming best practices, leveraging tools like Apache Kafka and Flink.
- Handles large-scale batch data processing, both incremental and full, using distributed computing and orchestration tools such as Airflow, Spark, and Hadoop.
- Manages and automates ETL/ELT workflows, ensuring efficient data extraction, transformation, and loading.
- Coordinates complex workflows, monitors tasks, and manages dependencies for seamless data operations.
Data Architecture
Structures the framework for managing data within an organization, including
- Design of data models
- Selection of storage technologies
- Implementation of data processing frameworks
- Integration of data governance practices
Aligns with organizational objectives to ensure data is accessible, reliable, and actionable for decision-making.Understands functional and non-functional requirements in order to define, design, and develop a prototype of an end-to-end solution.
DataOps
A data management practice that uses agile principles and DevOps methodologies across the data lifecycle, including:
- Improving the speed, quality, and reliability of data.
- Implementing and deploying continuous integration and delivery pipelines for data workflows, applying best practices that ensure reliability and repeatability.
- Automating different technical and business needs to improve the quality of life for day-to-day requests.
- Fostering collaboration among data engineers, data scientists, analysts, and business stakeholders.
Data Governance & Security
A framework to ensure data availability, usability, integrity, and security within an organization.
Includes policies, standards, and procedures for managing and protecting data throughout its lifecycle:
- Creation
- Storage
- Access
- Disposal
Essential for:
- Fostering trust in data
- Ensuring compliance with regulations
- Protecting sensitive information from unauthorized access and breaches.
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