5X vs Redshift: A Comparison on Data Readiness

Redshift has long been a stalwart in the data warehousing space, known for its blazing-fast query performance, scalability, and deep integration within the AWS ecosystem. Recent innovations in RA3 instances, enhanced machine learning capabilities, and seamless integration with other AWS services such as Glue, EMR, and SageMaker have made Redshift even more powerful and versatile.

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But what about the core data readiness?

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The true measure of a data platform isn't query speed, storage capacity, or cool AI/ML features; it's data readiness. Clean, structured, and centrally modeled data is the fuel for BI, advanced analytics, data activation, and increasingly, AI use cases. A solid data foundation is crucial for AI and LLMs to deliver accurate and valuable insights. You may have all the AI power, but without clean, accessible data, your models are just as good as their input. 

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The five layers of a data-ready system are:

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1. Ingestion

2. Warehouse

3. Modeling 

4. Orchestration

5. Business Intelligence

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How does Redshift measure up against these layers of a data readiness platform? Let's find out.

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Redshift

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Ingestion

• Offers Amazon Glue, which requires manual intervention and lacks automated connectors.
• Uses Amazon S3 as a staging area to handle large data volumes through object storage integration.
• Setting up and managing complex data pipelines might require additional scripting or custom solutions.
• Compared to dedicated integration tools, Redshift has fewer native connectors for specific data sources.

Warehouse

• High performance and cost-effective when fine-tuned, but not user-friendly out of the box.
• Requires expertise for optimal performance, unlike more automated solutions like Snowflake or GBQ that work out of the box.
• Lacks advanced data processing capabilities found in Databricks or Azure services.

Modeling

• Supports basic data modeling features like defining tables, views, and primary keys.
• Supports ETL tools like EMR (Elastic MapReduce), AWS Glue, Databricks, Talend, etc.
• Doesn't natively support enterprise-grade modeling tools like dbt for complex data modeling tasks.
• Requires workarounds and third-party solutions, leading to inefficiency.

Orchestration

• Offers basic scheduling capabilities for data loads and queries, but lacks orchestration features for complex data pipelines.
• Apache Airflow is commonly used, but integration with Redshift is not seamless.
• Adds complexity due to limited native integration and separate management.

Business intelligence

• Weakest area with limited support through tools like Amazon QuickSight. It isn’t easy to use or well integrated.
• Often requires third-party BI tools to achieve desired insights and usability.

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How 5X complements Redshift 

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Ingestion

• Offers 500+ pre-built connectors from all of the most used data sources.
• Hours, day implementations for custom connector development for the long tail of connectors.
• Simplifies handling incremental data updates for scenarios requiring near real-time data pipelines.
• Support for Apache Iceberg Tables in S3 or other flat storage.

Warehouse

• Works with multiple cloud warehouses like GBQ, Snowflake, and Azure Synapse.

Modeling

• Integrates with dbt for enterprise-grade data modeling.
• Offers features like lineage tracking, version control, and modular transformations.
• Supports SQL, Python, and notebooks for transformation flexibility.
• Offers table and column-level data lineage.

Orchestration

• Offers Dagster to ship pipelines quickly with 1-click scheduling.
• Enterprise grade scheduling and DAGS with easy to use UI.
• Prebuilt templates to accelerate dev time.
• Easier to manage pipelines in a unified workspace.

Business Intelligence

• Compatible with any BI tool.
• Provides Superset as an inbuilt option in the platform.
• Deep integrations and provisioning Power BI, Looker, Sigma and Tableau from 5X.

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Redshift vs 5X: A comparison on core data readiness

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Other considerations

Total cost of ownership (TCO)

Redshift

Redshift provides some great warehousing capabilities but several factors contribute to its total cost of ownership:

• Data transfer fees: Costs associated with data transfer between AWS services and external sources, particularly if large datasets are frequently moved in and out of Redshift.

• ETL tools: Additional costs for using AWS Glue or other ETL services to handle data extraction, transformation, and loading.

• Data integration and management: Expenses related to integrating Redshift with other tools or services for data governance, monitoring, and analytics, which might require separate licenses or subscriptions.

5X

Consolidates all functionalities into a single platform. This eliminates the need for multiple tools and associated costs. This integrated approach can reduce TCO by 30% through simplified billing, reduced infrastructure, and operational efficiencies.

Integrated services

Redshift

Using Redshift often involves additional costs related to platform optimization and management:

• Platform optimization: Ongoing costs for tuning and optimizing Redshift clusters to ensure performance, including manual configuration and adjustments.

• Consultancy fees: Expenses for engaging data consultancies to optimize Redshift, manage ETL processes, and implement best practices. Even hiring a fractional Chief Data Officer (CDO) for strategic oversight and implementation can be a significant expense.

• Team building costs: Hiring and training a specialized in-house team for managing Redshift and related tools, including data engineers, ETL developers, and database administrators.

5X

5X’s integrated services are approximately 25% of the cost of US-based consultancies and 70% of the cost of building and scaling an in-house team in America.

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The verdict

If you're using Redshift because you're committed to the AWS ecosystem, 5X can make your life a lot easier. 

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While Redshift may not be the best data warehouse out there, it works well within AWS. If you need to be in the AWS Ecosystem, one option is deploying Snowflake on AWS through 5X. This would fill in its gaps, making data readiness and management smoother while sticking to a AWS deployment.  

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This flexibility means you can handle different tasks on the best platforms available, without leaving AWS. 

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