Post Syndicated from Sam Mokhtari original https://aws.amazon.com/blogs/architecture/optimize-your-modern-data-architecture-for-sustainability-part-2-unified-data-governance-data-movement-and-purpose-built-analytics/
In the first part of this blog series, Optimize your modern data architecture for sustainability: Part 1 – data ingestion and data lake, we focused on the 1) data ingestion, and 2) data lake pillars of the modern data architecture. In this blog post, we will provide guidance and best practices to optimize the components within the 3) unified data governance, 4) data movement, and 5) purpose-built analytics pillars.
Figure 1 shows the different pillars of the modern data architecture. It includes data ingestion, data lake, unified data governance, data movement, and purpose-built analytics pillars.
Figure 1. Modern Data Analytics Reference Architecture on AWS
3. Unified data governance
A centralized Data Catalog is responsible for storing business and technical metadata about datasets in the storage layer. Administrators apply permissions in this layer and track events for security audits.
Data discovery
To increase data sharing and reduce data movement and duplication, enable data discovery and well-defined access controls for different user personas. This reduces redundant data processing activities. Separate teams within an organization can rely on this central catalog. It provides first-party data (such as sales data) or third-party data (such as stock prices, climate change datasets). You’ll only need access data once, rather than having to pull from source repeatedly.
AWS Glue Data Catalog can simplify the process for adding and searching metadata. Use AWS Glue crawlers to update the existing schemas and discover new datasets. Carefully plan schedules to reduce unnecessary crawling.
Data sharing
Establish well-defined access control mechanisms for different data consumers using services such as AWS Lake Formation. This will enable datasets to be shared between organizational units with fine-grained access control, which reduces redundant copying and movement. Use Amazon Redshift data sharing to avoid copying the data across data warehouses.
Well-defined datasets
Create well-defined datasets and associated metadata to avoid unnecessary data wrangling and manipulation. This will reduce resource usage that might result from additional data manipulation.
4. Data movement
AWS Glue provides serverless, pay-per-use data movement capability, without having to stand up and manage servers or clusters. Set up ETL pipelines that can process tens of terabytes of data.
To minimize idle resources without sacrificing performance, use auto scaling for AWS Glue.
You can create and share AWS Glue workflows for similar use cases by using AWS Glue blueprints, rather than creating an AWS Glue workflow for each use case. AWS Glue job bookmark can track previously processed data.
Consider using Glue Flex Jobs for non-urgent or non-time sensitive data integration workloads such as pre-production jobs, testing, and one-time data loads. With Flex, AWS Glue jobs run on spare compute capacity instead of dedicated hardware.
Joins between several dataframes is a common operation in Spark jobs. To reduce shuffling of data between nodes, use broadcast joins when one of the merged dataframes is small enough to be duplicated on all the executing nodes.
The latest AWS Glue version provides more new and efficient features for your workload.
5. Purpose-built analytics
Data Processing modes
Real-time data processing options need continuous computing resources and require more energy consumption. For the most favorable sustainability impact, evaluate trade-offs and choose the optimal batch data processing option.
Identify the batch and interactive workload requirements and design transient clusters in Amazon EMR. Using Spot Instances and configuring instance fleets can maximize utilization.
To improve energy efficiency, Amazon EMR Serverless can help you avoid over- or under-provisioning resources for your data processing jobs. Amazon EMR Serverless automatically determines the resources that the application needs, gathers these resources to process your jobs, and releases the resources when the jobs finish.
Amazon Redshift RA3 nodes can improve compute efficiency. With RA3 nodes, you can scale compute up and down without having to scale storage. You can choose Amazon Redshift Serverless to intelligently scale data warehouse capacity. This will deliver faster performance for the most demanding and unpredictable workloads.
Energy efficient transformation and data model design
Data processing and data modeling best practices can reduce your organization’s environmental impact.
To avoid unnecessary data movement between nodes in an Amazon Redshift cluster, follow best practices for table design.
You can also use automatic table optimization (ATO) for Amazon Redshift to self-tune tables based on usage patterns.
Use the EXPLAIN feature in Amazon Athena or Amazon Redshift to tune and optimize the queries.
The Amazon Redshift Advisor provides specific, tailored recommendations to optimize the data warehouse based on performance statistics and operations data.
Consider migrating Amazon EMR or Amazon OpenSearch Service to a more power-efficient processor such as AWS Graviton. AWS Graviton 3 delivers 2.5–3 times better performance over other CPUs. Graviton 3-based instances use up to 60% less energy for the same performance than comparable EC2 instances.
Minimize idle resources
Use auto scaling features in EMR Clusters or employ Amazon Kinesis Data Streams On-Demand to minimize idle resources without sacrificing performance.
AWS Trusted Advisor can help you identify underutilized Amazon Redshift Clusters. Pause Amazon Redshift clusters when not in use and resume when needed.
Energy efficient consumption patterns
Consider querying the data in place with Amazon Athena or Amazon Redshift Spectrum for one-off analysis, rather than copying the data to Amazon Redshift.
Enable a caching layer for frequent queries as needed. This is in addition to the result caching that comes built-in with services such as Amazon Redshift. Also, use Amazon Athena Query Result Reuse for every query where the source data doesn’t change frequently.
Use materialized views capabilities available in Amazon Redshift or Amazon Aurora Postgres to avoid unnecessary computation.
Use federated queries across data stores powered by Amazon Athena federated query or Amazon Redshift federated query to reduce data movement. For querying across separate Amazon Redshift clusters, consider using Amazon Redshift data sharing feature that decreases data movement between these clusters.
Track and assess improvement for environmental sustainability
The optimal way to evaluate success in optimizing your workloads for sustainability is to use proxy measures and unit of work KPI. This can be GB per transaction for storage, or vCPU minutes per transaction for compute.
In Table 1, we list certain metrics you could collect on analytics services as proxies to measure improvement. These fall under each pillar of the modern data architecture covered in this post.
| Pillar | Metrics |
|---|---|
| Unified data governance |
|
| Data movement |
|
| Purpose-built Analytics |
|
Table 1. Metrics for the Modern data architecture pillars
Conclusion
In this blog post, we provided best practices to optimize processes under the unified data governance, data movement, and purpose-built analytics pillars of modern architecture.
If you want to learn more, check out the Sustainability Pillar of the AWS Well-Architected Framework and other blog posts on architecting for sustainability.
If you are looking for more architecture content, refer to the AWS Architecture Center for reference architecture diagrams, vetted architecture solutions, Well-Architected best practices, patterns, icons, and more.