Conquering Data Challenges For Your Generative AI Success
Embarking on the journey of implementing successful Generative AI requires a strong and reliable foundation, with data preparation at its heart.
- Case Study - Building Data Lake Architecture For Enterprise Data Governance
Building Enterprise Data-Lake Solution For Centralized Data Governance
One of our enterprise customers encountered significant challenges with poor query performance, high maintenance costs, and a lack of centralized data governance. We addressed these issues by building a comprehensive data lake using AWS services such as AWS Lake Formation, DMS, S3, Apache Iceberg, AWS Athena, and AWS Glue. This solution significantly improved query performance, reduced maintenance costs, and established centralized data governance. This success story demonstrates how our data integration with AWS services can transform enterprise data management.
Key Business Challenges
Customer faced significant challenges with data and infrastructure management on their on-premises setup. They struggled with scalability, high costs, limited skilled resources, and a lack of visibility on future strategies. These issues resulted in lost business to competitors.
They urgently needed to address these critical problems, starting with consolidating their data and improving data management. In summary, their data management issues included:
- Poor Query Performance: The customer experienced significant delays and inefficiencies in retrieving and analyzing data due to the scattered nature of their data sources and lack of an optimized data architecture.
- High Maintenance Costs: The existing data infrastructure required substantial financial investment to maintain, including high operational costs and frequent need for troubleshooting and upgrades, leading to budget overruns.
- Lack of Centralized Governance: With no unified data governance framework in place, the customer struggled with data consistency, quality control, and compliance, making it difficult to enforce policies and standards across the organization.
- Complex Data Management: Managing multiple disparate data sources created complexity and redundancy, causing difficulties in data integration, processing, and ensuring data accuracy, which hindered effective decision-making.
Customer Objectives
The challenges faced by the customer resulted in high costs due to ineffective data management. Data silos led to inefficiencies as stored data lacked integration, causing redundant storage costs without yielding meaningful insights. The organization lacked visibility into its data landscape, hindering decision-making and strategic planning.
These issues highlighted the need for a centralized data governance approach to optimize storage, integrate diverse data types, and unlock actionable insights. By addressing these challenges, the customer aimed to enhance operational efficiency and leverage data effectively for informed decision-making and future growth initiatives.
- Centralized Data Governance: Establish a unified governance model to manage scattered data across applications, databases, and files, ensuring clear data ownership within departments.
- Integration and Transformation: Integrate diverse data sources, clean, and transform data to improve accuracy and usability for analytics.
- Secure Data Sharing: Implement a secure mechanism for intra-departmental data sharing, facilitating collaborative analysis while maintaining data confidentiality.
- Metadata Management: Utilize a Data Catalog for persistent metadata storage to enhance data visibility and accessibility.
These initiatives aimed to prepare the organization for future AI/ML capabilities, leveraging the centralized data lake infrastructure for various advanced analytics and machine learning use cases.
Project Description
Our team chose Amazon S3 for storing data in a structured Apache Iceberg table format to ensure efficient management and scalability. To address the security challenges, we used AWS Lake Formation to provide secure access controls and manage data security across the platform. AWS Glue was used to handle the Extract, Transform, Load (ETL) process, seamlessly preparing data for analysis and storage in the data lake. Athena was implemented as the query engine, allowing SQL-based queries on the latest inventory data stored in Iceberg tables.
This comprehensive setup ensures robust data governance, scalability, and agility in data processing and analytics with following workflow:
- To start with, our data expert team has used AWS Database Migration Service (AWS DMS) to connect to the data source and move incremental data (CDC) to Amazon S3 in CSV format.
- Then, an AWS Glue PySpark job reads the incremental data from the S3 input bucket and performs deduplication of the data records.
- The job uses Iceberg’s MERGE statements to combine the data with the existing data in the target S3 bucket.
- For data governance, our team used the AWS Glue Data Catalog as a centralized repository, utilized by both AWS Glue and Athena.
- An AWS Glue crawler scans the S3 buckets to automatically detect and catalog the schema.
- Lake Formation was configure to centrally manages permissions and access control for the Data Catalog resources in S3.
- Athena is integrated with Lake Formation to query data from the Iceberg table using standard SQL.
Key Benefits
- Improved Query Performance: By utilizing Amazon S3 and Iceberg table format, the data lake significantly enhanced query performance, allowing faster and more efficient data retrieval and analysis.
- Reduced Maintenance Costs: The automated data pipeline and centralized data governance with AWS Glue and Lake Formation minimized maintenance efforts and costs, offering a scalable and cost-effective data management solution.
- Enhanced Data Governance and Management: With the AWS Glue Data Catalog and Lake Formation, the customer achieved centralized and streamlined data governance, ensuring secure and efficient data access and management across departments.
- Readiness for AI and ML Use Cases: The robust data lake architecture, combined with improved data quality and accessibility, positioned the customer to effectively leverage AI and ML technologies, enabling advanced data analytics and predictive insights.
Contact Us
Generative AI Strategies: How To Maximize Your Business Opportunities
No AI strategy can thrive or endure without high-quality data because data is the lifeblood that fuels generative AI…
Get Straight Into The Heart Of Your Data With Amazon Athena
Amazon Athena lets you query data where it lives without moving, loading, or migrating it. You can query the data from relational, non-relational…
Modernize Your Data Warehouse Solution for AI-Powered Future-Ready Analytics
Amazon Redshift is a cloud-based next-generation data warehouse solution that enables real-time analytics for operational databases, data lakes….
