How to Overcome Athena-to-Snowflake Migration Challenges and Accelerate Analytics

Migrating from Amazon Athena to Snowflake can enable faster analytics, better scalability, and cost efficiency. However, organizations often face challenges in preserving data integrity, maintaining uptime for business-critical dashboards, and optimizing performance during the transition.

With the right strategy and tooling, especially replication platforms like CData Sync, enterprises can simplify migration while enabling continuous reporting.

This guide walks through practical steps for evaluating workloads, configuring CData Sync, handling schema differences, and monitoring performance. It also highlights best practices that ensure a smooth migration journey.

Evaluate your Athena workload and define migration objectives

A successful migration begins with a clear understanding of the current Athena environment. Not all datasets carry equal weight, so prioritization and goal setting are critical for ensuring business continuity and delivering measurable performance improvements once data lands in Snowflake.

Identify high-priority tables

Before migration, audit Athena catalog to identify which tables deserve top priority. Rank them by query volume, data size, and business criticality. High-priority tables are those that drive executive dashboards, fulfill service-level agreements, or feed downstream ML pipelines. Examples often include:

• Sales fact tables that summarize transactions across regions

• Customer-master tables used in personalization and marketing analytics

• Audit logs that must remain consistent for compliance

Industry case studies confirm that enterprises frequently need to support hundreds of pipelines and dashboards without interruption even while migrating datasets. By focusing on critical tables first, teams minimize risk and reassure stakeholders that key business processes remain reliable.

Set performance & latency goals

Defining success criteria early avoids surprises later. Establish measurable goals such as:

• Query latency ≤ 5 seconds for interactive dashboards

• Throughput ≥ 2 TB per hour during batch ingestion

These targets align with Snowflake’s elastic compute and auto-scaling features. Reports show that Snowflake typically returns targeted queries in 8–10 seconds, while Amazon Redshift workloads may take 30–40 seconds for similar complexity.

By tying performance metrics to business outcomes, expect shorter report wait times, quicker time-to-insight, and more responsive data products.

Set up CData Sync to connect Athena and Snowflake

Once migration goals are defined, the next step is to establish a reliable pipeline between Amazon Athena and Snowflake. This is where CData Sync plays a pivotal role, providing a no-code, enterprise-ready solution that automates data movement while ensuring security and performance.

Install & license CData Sync

Getting started with CData Sync is straightforward:

1. Download the installer from the CData trial page

2. Run the installer with administrative rights

3. Activate the license key provided in your confirmation email

CData Sync is designed with enterprise compliance in mind. Certifications such as SOC 2 and ISO 27001 ensure adherence to stringent global standards for security and privacy. Its pay-by-connection pricing model allows organizations to scale efficiently, paying only for the sources and destinations required in the pipeline.

Create Athena source connection

Amazon Athena can be configured as the source within CData Sync using the built-in connector. Authentication methods include:

• IAM roles for AWS integration

• Access Key and Secret Key pairs, configured according to least-privilege principles

The query push-down feature allows Athena to handle filtering and aggregations before data transfer, reducing both transfer size and processing costs. All traffic is secured with TLS 1.2 encryption in transit, and optional VPC endpoints can be configured to ensure private connectivity within AWS.

Create Snowflake destination connection

Snowflake is added as the destination by providing:

• The account URL for the Snowflake instance

• The warehouse name, database, and schema for loading data

• Authentication credentials via key-pair certificates or OAuth/SSO for compliance with enterprise

• Enabling Bulk loading mode to speed up initial migrations

To optimize costs, Snowflake’s auto-resume feature can be enabled. Warehouses automatically resume when queries are issued and suspend during idle periods, preventing unnecessary compute costs.

Map schemas, data types, and security controls

Migrating data requires more than moving records. It requires careful attention to schema alignment, naming conventions, and access controls.

Review data-type mapping

Athena and Snowflake use different type systems, so compatibility must be reviewed. Common conversions include:

• string → VARCHAR

• bigint → NUMBER(38,0)

For complex types, Snowflake provides flexibility:

• ARRAY and MAP fields can be flattened into relational structures.

• Alternatively, store them as VARIANT JSON for semi-structured querying.

Ensuring type compatibility during schema sync is a best practice emphasized in migration literature.

Handle case-sensitivity & naming

Snowflake interprets unquoted identifiers as uppercase, which can cause conflicts with Athena’s naming conventions. To maintain consistency, it is recommended to adopt lowercase, underscore-separated names during migration.

Example:
CustomerOrders (Athena) → customer_orders (Snowflake).

Configure role-based access

Athena IAM policies can be mirrored in Snowflake by creating equivalent roles. These roles can then be mapped in CData Sync under the Role-based access tab.

Snowflake also supports fine-grained security such as column masking for sensitive fields and row-level security for contextual access, features that can extend compliance protections beyond what Athena offers.

Execute initial load and enable continuous real-time replication

CData Sync supports both the full load and incremental replication, ensuring that data pipelines remain reliable as workloads evolve.

Run full data load

The first phase involves a Full Load. CData Sync uses Snowflake’s COPY INTO command to ingest large datasets efficiently. For very large tables, partitioning by date or primary key allows incremental updates, parallel loading, and reduces bottlenecks.

Case studies show that optimized tools can deliver up to 91% reduction in runtime compared to custom scripts, freeing engineering teams from manual troubleshooting.

Configure CData Sync for incremental changes

To keep Snowflake synchronized with Athena, enable incremental updates. This ensures inserts, updates, and deletes are continuously replicated without copying the entire dataset with every replication.

Polling intervals can be configured as low as 5 seconds, enabling end-to-end latencies under 5 seconds. If Athena lacks native incremental support for a dataset, changes can be inferred by querying information_schema tables or analyzing logs.

Validate data consistency

Migration is incomplete until validation is performed. Follow this checklist:

• Verify row count parity between Athena and Snowflake.

• Spot-check sample records for field-level accuracy.

• Confirm incremental replays do not create duplicate rows.

Optimize performance and monitor the migration pipeline

Long-term success depends on consistent monitoring and performance tuning of the migration pipeline.

Enable query push-down & parallel paging

By pushing filters and projections to Athena, only relevant subsets of data are transferred. Enabling parallel paging (default 4 threads) in CData Sync significantly accelerates throughput. High-volume tables can achieve up to 10× faster ingestion when these optimizations are in place.

Set up alerts & dashboards

Replication health should be monitored continuously. Alerts can be configured via email or Amazon SNS when replication lag exceeds thresholds such as 5 seconds.

While CData Sync has its own dashboard, external dashboards can be built in Power BI or Tableau, connected through CData Sync’s OData endpoint. These dashboards provide a visual overview of latency, error counts, and sync status. This enables proactive detection of issues before they impact analytics workloads.

Scale connections & tune batch size

Further optimization involves scaling the number of concurrent connections to handle independent tables in parallel, with 10–15 connections often serving as a practical starting point.

Batch sizes should be tuned to balance performance and responsiveness:

• 10,000 rows per batch is a good baseline.

• Larger batches improve throughput for wide, high-volume tables.

• Smaller batches minimize latency for lightweight tables.

This balance ensures efficient use of network and compute resources without overwhelming either Athena or Snowflake.

Frequently asked questions

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