Amazon Glue Delivery Partner for Enterprise Data Integration
As an AWS Glue delivery partner, 10Pearls helps enterprises implement and optimize AWS Glue solutions to automate ETL pipelines and unify data across sources.
What is AWS GLUE?
Amazon Glue is a fully managed, serverless data integration service designed to simplify the discovery, preparation, and transformation of data for analytics, machine learning, and application development. It automates ETL workflows with built-in schema discovery, intelligent code generation, and seamless integration across the AWS data ecosystem.
Why choose 10Pearls as your AWS Glue delivery partner
At 10Pearls, we combine deep AWS data engineering expertise with a business-focused approach. We design data integration solutions that improve data quality and drive actual business value.
Certified AWS data engineering expertise
- AWS-certified data engineers and solutions architects.
- Proven experience with enterprise-scale Glue implementations.
Custom ETL solutions
- Tailored pipeline architectures for your unique data sources.
- Industry-specific transformation logic and data models.
End-to-end implementation
- From strategy and design to deployment and optimization.
- Comprehensive crawler, job, and workflow development.
Advanced transformation capabilities
- Complex PySpark and Scala development expertise.
- ML-powered data quality and deduplication solutions.
Performance & cost excellence
- Job optimization for maximum efficiency and minimal cost.
- Continuous monitoring and resource right-sizing.
Global Support & Augmentation
- 24/7 support across all time zones.
- Flexible team augmentation for large-scale projects.
Our end-to-end Amazon Glue services
10Pearls provides a comprehensive suite of AWS Glue services to help enterprises build modern data integration platforms that scale with their business. We combine AWS expertise with proven ETL methodologies and data engineering best practices to ensure your data pipelines deliver reliable, high-quality data.
Glue strategy & data integration architecture
- Data integration strategy & ETL modernization planning
- Designing scalable data pipeline architectures
- Integration with AWS Data services (S3, Redshift, Athena, Lake Formation)
- Data Lake & Lakehouse Architecture Design
Glue deployment & ETL pipeline development
- Glue Data Catalog Setup & Metadata Management
- Custom ETL Job Development (PySpark, Python Shell, Scala)
- Glue Studio Visual Pipeline Creation
- Crawler Configuration & Automated Schema Discovery
Data integration & connector configuration
- Zero-ETL Integration Setup (Aurora, RDS, DynamoDB)
- Native Connector Configuration for Enterprise Applications (Salesforce, SAP, ServiceNow)
- JDBC & Custom Connector Development
- Streaming Data Integration (Kinesis, MSK, Kafka)
ETL optimization & performance tuning
- Job Performance Analysis & Optimization
- DPU Right-Sizing & Cost Optimization
- Glue Data Quality Rule Implementation
- Incremental Processing with Job Bookmarks
Advanced data transformation & ML integration
- Complex Data Transformation Logic Development
- FindMatches ML for Deduplication & Record Matching
- Integration with SageMaker for ML Workflows
- DataBrew Visual Data Preparation Configuration
Managed operations & pipeline monitoring
- 24/7 Pipeline Monitoring & Incident Response
- Automated Job Scheduling & Orchestration
- Cost Management & Resource Optimization
Why AWS Glue matters for enterprises
AWS Glue provides a serverless, scalable solution that eliminates infrastructure complexity while enabling fast, reliable data pipelines that power analytics, AI, and business intelligence. This allows enterprises to:
Automate complex ETL workflows
Eliminate infrastructure management overhead
Connect 100+ diverse data sources
Scale from gigabytes to petabytes seamlessly
Accelerate data preparation with AI assistance
Business outcomes with Amazon Glue
AWS Glue can be used for more than just an ETL tool. For example, it is ideal for enterprises looking to deliver measurable business value through faster data preparation, reduced operational complexity, and the agility to adapt pipelines with growing business needs.
Accelerated data preparation
- Reduce ETL development time by up to 70% with auto-generated code.
- Prepare data for analytics and AI in minutes, not days.
Infrastructure-free operations
- Eliminate server provisioning and management overhead.
- Focus your team on data workflows, not infrastructure maintenance.
Cost efficiency
- Pay only for resources consumed during job execution.
- Optimize costs with automatic scaling and resource management.
Enhanced data quality
- Implement ML-powered data quality checks and anomaly detection.
- Ensure clean, trustworthy data for analytics and decision-making.
Rapid integration
- Connect to 100+ data sources with native connectors.
- Unify data from SaaS applications, databases, and data lakes.
Enterprise use Cases for AWS Glue
AWS Glue powers critical data integration workloads for modern enterprises enabling unified analytics and AI-driven insights. We leverage AWS Glue to solve business challenges helping organizations extract maximum value from diverse data sources across cloud and on-premises environments.
Building enterprise data lakes and lakehouses
- Catalog and organize petabytes of raw data from multiple sources
- Automate ETL to transform data into analytics-ready formats like Parquet and ORC
Modernizing legacy ETL pipelines
- Migrate from on-premises ETL tools to cloud-native serverless architecture
- Reduce operational overhead and accelerate data pipeline development
Unifying data from SaaS and enterprise applications
- Connect to Salesforce, SAP, ServiceNow, and 100+ other sources
- Centralize data for comprehensive business intelligence and reporting
Real-time streaming data integration
- Process streaming data from Kinesis, MSK, and Kafka in real-time
- Enable near real-time analytics for operational decision-making
Data warehouse population and maintenance
- Automate data loading into Redshift, Snowflake, or other warehouses
- Implement incremental updates and change data capture workflows
How we partner for successful delivery
We work as an extension of your team aligning data integration strategy with business objectives while ensuring seamless execution from discovery through production operations.
Discovery & Data Source Assessment
- Map your current data landscape, sources, and integration requirements
- Identify data quality issues, transformation needs, and business priorities
- Define success metrics, KPIs, and implementation roadmap
Architecture & solution design
- Design Glue architecture aligned with your data platform strategy
- Define ETL patterns, data models, and transformation logic
- Plan migration from legacy ETL tools or manual processes
Implementation & pipeline development
- Configure Glue Data Catalog and crawlers for metadata discovery
- Build and test ETL jobs, workflows, and data quality rules
- Integrate with data lakes, warehouses, and analytics platforms
Enablement & knowledge transfer
- Provide comprehensive documentation and operational runbooks
- Train your teams on Glue best practices and pipeline management
Ongoing Support & Optimization
- Continuous pipeline monitoring and performance tuning
- Cost optimization and job efficiency improvements
- SLA-backed support with proactive issue resolution
AWS case studies
Case study - TECHNOLOGY
Enhancing anti-fraud operations with secure cloud architecture
A cross-industry leader in fraud prevention needed a secure, multi-account AWS foundation for data validation, processing, and partner integrations. 10Pearls delivered a robust, compliant architecture to accelerate fraud detection and collaboration.
case study - energy
Optimizing Energy Insights with Unified Data Analytics
10Pearls designed a unified data and analytics platform on AWS that consolidated time-series data, continuous CDC sync, and ISO market feeds into a single, cloud-native environment.
Transform your data. Accelerate your insights.
Let’s discuss how AWS Glue can connect, prepare, and transform your data, enabling seamless analytics and creating a strong foundation for AI-driven innovation