Data Engineering
Better Care for Your Data
Addressing today’s business challenges requires real-time, accurate data. However, many organizations still rely on decades-old data management technology. These aging systems don’t perform well, aren’t scalable and struggle with processing ever-increasing volumes of data.
We can help you migrate your legacy systems and applications to modern technologies in the cloud. We’ll harness our 20+ years of data engineering experience to help you find critical business insights, boost efficiencies and gain a competitive edge.
Mastering Data Engineering Excellence
Enhanced Customer Experience
Tailor products and services with customer data insights to boost satisfaction and loyalty.
Data Integration
Merge data from diverse sources into an accessible format for comprehensive insights.
Data Processing Efficiency
Automate and optimize data processes for quicker analysis and decision-making.
Robust Data Governance
Establish policies and security measures to ensure data integrity and compliance.
Scalability
Scale data infrastructure seamlessly to accommodate growth without performance issues.
Cost Optimization
Reduce operational costs and hardware expenses through efficient data handling.
Cost Optimization
Reduce operational costs and hardware expenses through efficient data handling.
Improved Data Quality
Guarantee reliable and consistent data for trustworthy decision support.
Benefit from Decades of Data Engineering
Excellence
Proficient in Tools and Technologies
Data Collection and Ingestion
Data Storage and Organization
Data Processing and Transformation
Orchestration and Workflow Management
Performance and Cost Optimization
Data Quality Assurance
Data Security and Compliance
Re-Engineer Your Data Now
Data and Analytics Solutions
Big Data
Data Warehousing
Data Migration
Data Integration
Data Lineage
Data Mesh Architecture
Re-Engineer Your Data Now
Data Collection and Ingestion
- Identifying Data Sources: Pinpoint various sources, including internal databases, APIs, external datasets, streaming data from social media, IoT devices, etc.
- Data Acquisition: Develop mechanisms to collect or receive data from these sources. This could involve setting up APIs, web scraping, or using tools integrating different data sources.
Data Storage
- Designing Storage Solutions: Decide where and how to store the collected data. This might involve databases (SQL or NoSQL), data warehouses, or data lakes, depending on the nature and scale of the data.
- Implementing Storage: Set up the chosen storage solutions, ensuring they are scalable, secure, and optimized for the types of data queries and operations performed.
Data Modeling and Warehousing
- Data Modeling: Design models that define how data is connected, stored, and accessed. This involves creating schemas and defining relationships between data points.
- Data Warehousing: Implement and manage data warehouses that consolidate data from various sources into a central repository for analysis and reporting.
Data Processing and Transformation
- Extract, Transform, Load (ETL): Develop ETL pipelines to automate the process of extracting data from its source, transforming it into the desired format, and loading it into a data store or warehouse.
- Data Enrichment: Enhance data by merging it with other relevant data sources to add context or additional insights.
- Data Cleaning: Address issues like missing values, inconsistencies, and errors in the data.
- Data Transformation: Convert data into a format or structure that is more suitable for analysis. This could involve normalization, aggregation, and formatting operations.
Data Integration and Orchestration
- Integrating Diverse Data: Combine data from disparate sources to provide a unified view. This may involve dealing with different data formats and structures.
- Workflow Management: Use tools like Apache Airflow to orchestrate and automate data workflows, ensuring that data processes are executed in the correct sequence and at the right time.
Monitoring and Maintenance
- Monitoring: Continuously monitor data pipelines and storage systems for performance, errors, and other issues.
- Maintenance and Optimization: Regularly update, optimize, and maintain data pipelines and storage solutions to ensure they remain efficient, secure, and cost-effective.
Security and Compliance
- Data Security: Implement measures to protect data from unauthorized access and breaches.
- Compliance: Ensure data handling practices comply with relevant data protection regulations like GDPR or HIPAA.
Documentation and Governance
- Documentation: Maintain thorough documentation for data pipelines, models, and architectures to ensure clarity and continuity.
- Data Governance: Establish and enforce policies for data management, quality, and usage across the organization.
Collaboration and Support
- Supporting Data Consumers: Work closely with data analysts, scientists, and business stakeholders to ensure they can access and use the data effectively.
- Feedback Incorporation: Continuously improve data processes based on data user feedback and business requirement changes.
Approach to Data Engineering
Success Story
GCP Migration and Data Lake Implementation
A large Canadian organization wanted to migrate their Hadoop-based on-premises data lake solution to the cloud. The re-platforming needed to be completed within a strict timeline, as the client’s existing technical solution was scheduled to run out of support soon.
We implemented a data lake solution in Google Cloud by migrating an on-premises Hadoop-based data lake to a fully managed GCP environment.
Fully
managed solution
0
capacity limitations
High
availability and uptime
Success Story
Equa Bank’s Clients Were Fully Migrated to Raiffeisen Bank in 12 hours
“Raiffeisenbank successfully acquired Equa Bank and fully integrated it under the Raiffeisenbank brand. Thanks to the dedication and professionalism of the Adastra team, combining the data from the two client bases went smoothly and according to plan.
We also ensured continuous regulatory reporting throughout the course of the acquisition. once again confirmed their professionalism and expertise in data migration and working with data in general. I very much value our partnership and hope it will continue in the future. It is a pleasure to work with such pros.”
Miloš Matula | COO and Member of the Board of Directors of Raiffeisenbank
40x
shorter live migration (12 hours instead of 3 weeks)
700k
subjects were migrated
450k
people added to Raiffeisenbank’s client base
