Customer-specific analytics built on Databricks and AWS S3
CLIENT: A multi-user platform providing IT services for individual consumers, small to large businesses and enterprise clients.
CHALLENGE:
One of the Client’s products is file storage, enabling users to store their documents, media and logs safely, efficiently and with zero maintenance required.
Constantly receiving large volumes of data from its customers, the Client wanted to process and analyze files in a distributed and efficient manner to give customers valuable insights based on their specific needs.
The solution required significantly more distributed computing compared to S3’s sequential storage, faster data processing and powerful analysis capabilities.
Requirements:
- Data Security and Privacy
To ensure that client data remains secure, private and compliant with data protection regulations as a top priority.
- Scalability
To manage and process multiple client’s data with the ability to prioritize handling based on the customer’s tier.
- Automation
To provide efficient on-boarding of the new clients along with seamless ingestion, processing, and analysis of their files.
- Customization
To meet the unique data analysis requirements of each customer while maintaining efficiency of processes.
SOLUTION:
- Data Security and Privacy
We used Databricks Unity Catalog to administer and audit accesses and permissions across multiple workspaces, simplifying governance and securing the data.
We configured customer-managed keys for the workspace’s root S3 bucket, and implemented AWS KMS key encryption.
We programmed Databricks to log user activities, providing an audit trail for data access and changes, and facilitating compliance with regulations.
- Scalability
We set up Hybrid Databricks cluster (a mix of on-demand and spot instances) with auto scaling and local storage, increasing flexibility of resources distribution so the system can prioritize certain tasks or customers.
We set up Databricks to use the power of Spark for parallel data management across multiple nodes, significantly enhancing processing speed and scalability.
- Automation
We automated Databricks jobs and notebooks to perform predefined tasks at specified times without manual intervention.
We automated quality checks and validation steps within the workflows to ensure the accuracy and reliability of processed data.
We automated installation and management of libraries and packages.
We automated alerts and notifications based on predefined conditions or anomalies detected during data processing.
- Customization
We created Databricks notebooks with client-specific requirements in mind, allowing for unique transformations and visualizations to be extended or updated per customers’ requests.
Databricks supports various libraries for different data analysis needs, allowing the Client to customize analyses using specific tools and frameworks.
RESULT:
- Enhanced Data Processing
Databricks enables the Client to efficiently process and analyze sizable data volumes and promptly deliver tailored valuable insights to customers.
- Customer-Centric Solutions
The Client gained an ability to provide fully customizable analytic solutions for their customers, increasing customer engagement, strategic differentiation and ultimately fostering business growth.
- Scalable Performance
The combination of Databricks’ adaptive scaling and AWS S3’s scalable storage guarantees consistent performance as data volumes change over time. It´s a solid base for business growth, allowing for expansion without sacrificing operational efficiency.
- Automated Workflows
Automated processes boost operational efficiency by speeding up tasks and reducing manual effort while contributing to resource allocation and cost saving.
- Compliance Assurance
Combining Databricks’ strong data governance with AWS S3’s data protection features, the Client can confidently navigate regulatory requirements, build trust, and reduce compliance risks.
Share:
