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Establish data pipelines for ingestion and processing
Target state: the target architecture would define ingestion, transformation and loading pipelines using open-source tools like Apache Airflow, Apache Spark and AWS Glue. It would ingest raw data from various sources into an S3 data lake in its native formats. Standard ETL/ELT processes would then transform and load the data into structured data stores based on predefined schemas, ensuring privacy and regulatory compliance. Processed data would be made available for analytics through services like AWS Athena.
Gap analysis: the current architecture retrieves and processes data but there is no mention of standardized data ingestion or processing flows. This could lead to non-compliant or insecure data handling practices as the infrastructure scales. It also does not support analyzing data across different sources in a unified manner.
Recommended action:
Define ingestion pipelines using Airflow/Glue to ingest raw data into S3
Design ETL processes in Spark to transform and load data into structured stores
Implement data quality checks and security controls in pipelines
Automate pipeline executions on a schedule
Monitor pipeline metrics for failures and performance
Segregate workloads into separate VPCs based on sensitivity
Target state: The target architecture would involve creating separate VPCs - a "restricted" VPC to host only workloads dealing with sensitive data like PII, and a "general" VPC for other less sensitive workloads. Network access controls and security groups would be used to prevent direct communication and data sharing between the VPCs, restricting access to sensitive data.
Gap analysis: The current architecture deploys workloads containing both sensitive and non-sensitive data into the same VPCs. This poses a risk if a breach occurs, as it could potentially expose all data. The architecture does not sufficiently segregate access and restrict sharing of sensitive data as required.
Recommended action:
Classify all workloads and data into sensitivity categories
Create a "restricted" VPC and a "general" VPC with appropriate network controls
Migrating sensitive workloads to the restricted VPC
Enforcing access controls between VPCs using security groups
Implementing monitoring of traffic and access between VPCs
Scale data processing infrastructure
Target state: To address scalability, databases will be sharded so each shard can scale independently. Databases will be configured for auto-scaling so additional read replicas are added automatically based on usage. The data will be partitioned by date, customer or other dimensions to distribute load. Compute services like ETL will be designed to work with a sharded and partitioned architecture for distributed processing.
Gap analysis: Our current architecture uses single instances for databases and data warehouses. While this meets needs, it does not allow scaling out in a cost efficient and performant way as data and workloads increase time. The databases and data warehouse may encounter performance issues and become a bottleneck if scaled appropriately.
Recommended action:
Perform database sharding based on access patterns and workload profiles
Implement auto-scaling configurations for databases and data warehouse
Refactor ETL pipelines to work with sharded and partitioned targets
Establish monitoring to auto-scale resources within defined thresholds
Gradually shift workload to new scalable architecture
Migrate relational databases to Amazon Aurora
Target state: The target architecture migrates relational databases to Amazon Aurora, which provides automatic scaling of storage and compute resources. Aurora replicas can scale out performance by adding more instances as load increases. Its self-healing capabilities also improve availability. This dynamic scaling ensures databases can adapt to varying workloads and support business growth in a cost-efficient manner.
Gap analysis: The current architecture uses multiple Amazon RDS deployments for relational databases. While RDS provides managed database services, it does not automatically scale compute and storage. This could limit the ability to dynamically scale databases as load increases. Manual scaling may not keep up with unpredictable growth.
Recommended action:
Assess database workloads and size requirements for migration
Migrate schema and data from RDS to Aurora using AWS Database Migration Service
Refactor applications to point to new Aurora endpoints
Decommission old RDS deployments after validating Aurora migration
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