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Frequently Asked Questions
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We support a wide range of export formats to fit your business and analytics needs. Data can be delivered as flat files (CSV, TSV, or custom-delimited), relational database tables, cloud object storage (S3, Azure Blob, Google Cloud Storage), or directly into data warehouse destinations like Snowflake, BigQuery, or Amazon Redshift. Custom formatting and schema mapping are also available.
Yes. We ensure that your exported data is compatible with leading reporting and analytics platforms. Data can be structured and formatted to import seamlessly into tools such as Tableau, Power BI, Snowflake, or Looker. Flat files can be customized with your preferred delimiters, headers, and encoding for smooth integration with your existing workflows.
Our migration process is designed for speed, transparency, and control:
Connect & Configure: Use our intuitive interface to connect your endpoints, define mappings, and set export parameters.
Extract & Structure: We securely extract your data—preserving metadata, relationships, and history—and organize it into your chosen format or destination.
Automate or Export: Run a one-time migration or schedule recurring syncs. Every export is secure, auditable, and fully configurable for your ongoing data needs.
Check out our videos
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AI initiatives don’t fail because of bad models. They fail because objectives, data, and business processes aren’t clearly understood first. In this thought-leadership piece, Sesame Software CEO Rick Banister explains why AI should be treated as a tool, not a goal, and how examining data quality and workflows during the discovery phase often delivers more value than deploying AI itself.
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AI models are only as good as the data pipelines feeding them. This guide shows enterprise IT teams how to build AI-ready data pipelines with the quality, governance, and infrastructure control that machine learning requires.
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If your team is exploring how to bring AI into your enterprise workflows, you’ve probably hit a familiar challenge: the data isn’t ready. It’s trapped in siloed systems, inconsistent across platforms, or missing altogether. And while plenty of vendors will offer to “start fresh,” building a new data foundation from scratch is time-consuming, expensive, and often unnecessary. Here’s the good news: you may already have what you need if you can access, move, and prepare your dat
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