Using CSVs for SaaS Data Migrations: Pros and Cons

One of the most common questions in SaaS data migration is: "What's the best way to extract data?" The answer depends on your dataset size, the data types you need to preserve, and how much engineering effort you can invest.

In this post, we break down the three main approaches — CSVs, APIs, and JSON exports — with honest assessments of where each works and where each falls apart.

Quick Comparison

How to Choose: A Simple Decision Tree

• Small dataset, text-only fields, no relationships to preserve → CSV can work, but expect to spend time on field mapping and error correction.
• Need to preserve relationships between records (e.g., contacts linked to deals linked to tickets) → API-based migration. CSVs flatten relational data and lose these associations.
• Large dataset, source platform offers a bulk JSON export → JSON export combined with API import. You get fast extraction and programmatic control over the data.
• Complex migration with mixed data types → A hybrid approach (JSON export + API calls to fill gaps) tends to give the best balance of speed and completeness.

CSVs for SaaS Data Migrations

Skills Required

• Data wrangling: Mastery of spreadsheet functions like VLOOKUP, HLOOKUP, and pivot tables is often necessary.
• Attention to detail: You'll need to meticulously map fields and ensure columns are correctly titled for a seamless migration.

Time Commitment

Preparing a CSV can take hours, largely due to the need for precise formatting based on the requirements of the target SaaS application.

Pros

• Ease of access: Most SaaS platforms allow easy download and upload of CSVs.
• Universal support: CSV uploads are supported by nearly all SaaS platforms.
• No engineering required: Anyone comfortable with spreadsheets can attempt a CSV migration.

Cons

• Limited data scope: Important data such as emails, calls, notes, attachments, and comments are often excluded. CSVs struggle with nested or relational data — you can export a list of contacts, but the links between contacts, deals, and conversation history are typically lost.
• Time-consuming: For large datasets, both downloading and uploading can be slow.
• Encoding and formatting errors: Special characters, date formats, line breaks within fields, and character encoding mismatches are common sources of import failures.
• No built-in validation: CSVs give you no way to verify referential integrity. You won't know a relationship was dropped until after import.
• Error-prone: SaaS platforms may return errors that take time to debug — mismatched column headers, unexpected null values, or truncated fields.

When CSVs Break Down

CSVs work reasonably well for small, flat datasets — a few thousand contacts with standard fields. They start to break down when:

• Your dataset has relational data (associations between objects)
• You need to migrate attachments or embedded files
• Records contain rich text, HTML, or multi-line content
• The dataset is large enough that manual error correction becomes impractical

APIs for SaaS Data Migrations

Skills Required

Connecting source and destination accounts via API is typically straightforward — often just generating an API key. The complexity comes later, during the actual migration.

Time Commitment

The total time depends on the rate limits of the source and destination APIs. Rate limits cap the number of requests within a set time frame, which directly controls migration speed for large datasets.

Pros

• Comprehensive: APIs often allow the migration of all data types, including emails, conversations, private notes, comments, attachments, and users.
• Preserves relationships: API-based migrations can maintain associations between records (e.g., linking a ticket to the contact who created it and the agent who resolved it).
• Programmatic validation: You can build verification checks into the migration script to confirm record counts, field mappings, and data integrity.

Cons

• Rate limits: Strict API rate limits can significantly slow down large migrations.
• API-specific failure modes: Migrations can encounter authentication failures, pagination bugs, timeout errors, and schema mismatches between source and destination platforms. These are solvable problems, but they require engineering effort to handle gracefully.
• Platform API variability: Not all APIs are equally capable. Some platforms expose limited endpoints, restrict certain data types, or have undocumented quirks that only surface during migration.

JSON Exports for SaaS Data Migrations

An important clarification: JSON is a data format, not a migration method on its own. When we talk about "JSON migrations," we mean exporting data as JSON from the source platform and then pushing it into the destination via API. The export is fast; the import is still subject to API rate limits.

Skills Required

JSON files can be handled programmatically, which makes data manipulation easier than working with CSVs. You'll need basic scripting ability (Python, Node.js, etc.) to parse and transform the data before importing it.

Time Commitment

JSON exports are quick to generate from the source. The bottleneck is the import side — data needs to be pushed through the destination's API, so the same rate limits apply.

Pros

• Fast extraction: JSON files are quick to generate from most SaaS platforms that offer bulk export.
• Comprehensive: In most cases, JSON exports include richer data than CSVs — nested objects, metadata, and timestamps are preserved.
• Code-friendly: JSON files are easy to manipulate programmatically, making data transformation and field mapping more reliable than manual spreadsheet work.

Cons

• Incomplete data: Some fields or data types might be missing from the JSON export, depending on the source platform's export capabilities.
• Still API-dependent on import: The destination platform rarely accepts a raw JSON upload. You're still going through the API, so rate limits and API failure modes still apply.
• Not universally available: Not all SaaS platforms offer a JSON export option. Some only provide CSV or require you to pull data through their API.

Post-Migration: Verifying Data Integrity

Regardless of which method you use, verification after migration is critical. A migration isn't complete until you've confirmed:

• Record counts match between source and destination
• Relationships are intact — tickets linked to contacts, deals linked to companies, etc.
• Attachments and files transferred correctly
• Custom fields and metadata mapped to the right destination fields
• Timestamps (created dates, updated dates) are preserved, not overwritten with the migration date

Without a verification step, you won't know what was lost until a user reports missing data weeks later.

Our Recommendation

There's no single best method — the right approach depends on your dataset and constraints.

For most non-trivial SaaS migrations, a hybrid approach works best: use JSON exports (where available) for fast bulk extraction, supplement with API calls to fill in any gaps, and build in a verification pass to confirm completeness. CSVs are a reasonable option for small, simple datasets, but they become a liability as soon as you need to preserve relationships or handle complex data types.