Operational data is produced by day-to-day business activities like transactions, user interactions, IoT sensors, etc. This high-velocity data needs specialized infrastructure for storage and analysis.
Operational data stores built on Amazon S3 and Amazon Redshift provide a powerful and flexible cloud architecture for real-time analytics.
In this step-by-step guide, we will dive into how to leverage these AWS services to build a performant and scalable operational datastore.
What is an Operational Datastore?
An operational datastore is optimized for storing and processing high volumes of real-time operational data. Key aspects include:
- Fast data ingestion from various sources
- Real-time analysis with minimal latency
- Flexible data model to accommodate variably structured data
- High availability and durability of data
- Scalability to handle data spikes
Unlike traditional data warehouses focused on business intelligence, operational datastores enable real-time decision-making by reducing data-to-insight times.
Benefits of S3 and Redshift
Amazon S3 and Redshift make an excellent combination for building operational datastores due to:
- High scalability – Both S3 and Redshift can scale storage and compute as per data volumes ingested.
- Performance – Redshift delivers fast query performance for real-time analytics by using columnar storage, MPP architecture, and advanced query optimization.
- Cost-effectiveness – You only pay for the resources you use. The separation of storage and computing allows for optimizing costs.
- Durability – Data stored in S3 and Redshift is replicated for high durability.
- – The S3 data lake allows capturing variably structured data which can be processed for Redshift.
- Managed services – Redshift and S3 are fully managed, reducing operational overheads.
A typical S3 and Redshift architecture looks like this:
- Operational data from various sources is streamed to the S3 data lake.
- Extract, transform, load (ETL) jobs process S3 data and load into Redshift.
- Redshift clusters store curated datasets for real-time analytics.
- BI tools query Redshift and visualize insights.
- Elastic compute resources auto-scale to match data volumes.
The decoupled storage and compute provide flexibility to scale resources independently.
Follow these key steps to implement an operational datastore with S3 and Redshift:
- Design the Analytical Data Model
The first step is to design the analytical data model optimized for your business reporting needs.
- Identify the key entities like customers, products, transactions etc.
- Define relationships between entities using foreign keys.
- Choose appropriate data types – optimize for range filters and aggregations.
- Normalize appropriately to avoid redundancies and inconsistencies.
- Include required dimensions, facts and aggregation columns.
- Model time series data for trended analysis using date dimensions.
The output is a relational schema optimized for analytics and business intelligence.
- Create Database and Schema in Redshift
Once the data model is ready, we can create a database and schema in Amazon Redshift to store the tables.
- Use the AWS console to create a Redshift cluster with optimal node type and count.
- Create a database to logically group tables.
- Define a schema corresponding to the data model.
- Specify dist and sort keys based on queries.
This prepares Redshift for loading and querying the data.
- Set Up Data Lake Storage on S3
We need to stage the source data on Amazon S3 before loading it into Redshift.
- Create an S3 bucket for the data lake.
- Logically partition the bucket into raw, processed, and curated zones.
- Apply appropriate data lifecycle policies for transition and expiration.
- Set up access controls for security and privacy.
This provides a durable and scalable data lake for staging data.
- Build ETL Process
Develop ETL process to extract data from sources, transform, and move to Redshift.
- Extract data from operational systems using APIs, batches, or streaming.
- Cleanse, validate, normalize, and conform data to the target model.
- Use Glue crawlers to infer schema and defined mappings.
- Insert data into Redshift tables using copy commands or API integration.
Robust ETL ensures high-quality data is loaded into the warehouse.
Develop Business Intelligence Dashboards
Connect your visualization layer to Redshift to build reports and dashboards.
- Use tools like Quicksight to connect and model data.
- Build interactive reports, charts, pivot tables, and dashboards.
- Schedule refresh to update with the latest data.
- Control access to data via row-level security.
Rich analytics uncovers business insights from the data.
Monitor and Maintain Data Pipeline
Ongoing monitoring and maintenance ensures the health of the data pipeline.
- Monitor data volumes, ETL metrics, and pipeline errors.
- Tune ETL queries and Redshift performance periodically.
- Scale Redshift clusters to match data growth.
- Manage data retention policies and purging.
- Update models, and mappings as needs evolve.
Typical use cases for an S3 and Redshift operational datastore include:
- User analytics: Analyze user journeys, feature usage, and other operational metrics for product improvement.
- Fraud detection: Detect fraudulent transactions, account activity, etc. in near real-time.
- IoT analytics: Ingest and process streams of sensor data for monitoring and analytics.
- Supply chain optimization: Gain insights into material flow, inventory, and logistics for efficient operations.
- Customer 360 analytics: Build composite customer profiles by integrating data across departments and channels.
Follow these best practices when architecting your S3 and Redshift operational datastore:
- Choose the right distribution and sort keys in Redshift for optimized performance.
- Partition large tables into smaller ones for better parallelism and fewer VACUUM ops.
- Compress tables and columns to reduce storage requirements.
- Isolate workloads by allocating dedicated clusters per usage pattern.
- Implement IP whitelisting and VPC endpoints for security.
- Backup critical data in S3 regularly.
FAQs – Frequently Asked Questions and Answers
- Does Redshift support unstructured data as well?
Answer: Redshift is optimized for structured and semi-structured data. For completely unstructured data, storing it directly in S3 and running analytics using Athena would be more efficient.
- How do you ensure data consistency between S3 and Redshift?
Answer: Use ETL best practices like committing data only after successful loads to Redshift. Implement reprocessing pipelines to reconcile gaps.
- Can you use other AWS databases like DynamoDB for an operational datastore?
Answer: Yes, DynamoDB can complement S3 and Redshift for certain operational workloads requiring key-value access to real-time data.
Combining the scale and flexibility of S3 with the performance of Redshift provides a powerful platform for real-time operational analytics. Planning the architecture, schemas, ETL processes and integrations is key for smooth implementation.
With robust monitoring and optimizations, an S3-Redshift datastore can deliver the performance and availability required for mission-critical workloads.