Skip to content

Latest commit

 

History

History
73 lines (61 loc) · 3.6 KB

ADR-17-Amazon-Redshift.md

File metadata and controls

73 lines (61 loc) · 3.6 KB
Raw

ADR-17: Amazon Redshift

Date:

2024-09-30

Status:

Accepted

Context:

The organization needs a data warehouse to consolidate data from various sources for reporting and analytics. The solution should provide high performance, scalability, and robust integration capabilities to support data-driven decision-making.

Key factors considered:

  • Performance: The data warehouse must support fast query execution and analytics on large datasets.
  • Scalability: The solution should handle increasing data volume and user demand without compromising performance.
  • Integration: Seamless integration with existing data sources, AWS services, and third-party BI tools is crucial.
  • Cost: The data warehouse must be cost-effective, providing good value for the required features.

Decision:

We will use Amazon Redshift as our data warehouse solution for storing and analyzing large volumes of data.

Considered Options:

Amazon Redshift

  • Pros:
    • Columnar storage and parallel query execution provide high performance for analytical queries.
    • Ability to scale both storage and compute independently, optimizing cost and performance.
    • Strong integration with AWS services like S3, Glue, and QuickSight.
    • Built-in security features and compliance with industry standards.
  • Cons:
    • Requires careful management of clusters to optimize performance and costs.
    • May require additional configuration for data loading and ETL processes.

Google BigQuery

  • Pros:
    • Serverless architecture that simplifies management and scales automatically.
    • Excellent performance for large-scale SQL queries.
  • Cons:
    • Cost can escalate with frequent querying and large data volumes.
    • Data transfer costs may apply when moving data in and out of Google Cloud.

Snowflake

  • Pros:
    • High-performance data warehousing with automatic scaling.
    • Supports various data types and provides easy data sharing capabilities.
  • Cons:
    • Generally higher operational costs compared to Redshift.
    • The pricing model can be less predictable based on usage patterns.

Azure Synapse Analytics

  • Pros:
    • Combines big data and data warehousing capabilities in a single service.
    • Strong integration with Microsoft tools and services.
  • Cons:
    • Complexity in management due to its hybrid nature.
    • Some features may not be as mature as those offered by AWS.

Rationale:

We chose Amazon Redshift because it effectively addresses our data warehousing needs:

  • Performance: Redshift's architecture allows for rapid query execution, making it suitable for complex analytics on large datasets.
  • Scalability: It can scale storage and compute resources as needed, ensuring that performance remains high as data volume grows.
  • Integration: Redshift's integration with AWS services allows for efficient data ingestion and analytics workflows. Cost-Effectiveness: While Redshift requires some operational oversight, its pricing structure aligns well with our budget and anticipated data growth.

Consequences:

  • Operational Management: Although Redshift is a managed service, some operational tasks, such as cluster management and optimization, will require attention.
  • Data Loading Complexity: ETL processes must be established to efficiently load data into Redshift, which may involve additional resources or tools.

Next Steps:

  1. Set up the Amazon Redshift data warehouse and configure it to meet our analytics requirements.
  2. Develop ETL workflows to ingest data from various sources into Redshift.
  3. Train relevant teams on best practices for using Redshift for data analysis and reporting.