File Format Integrations with Edilitics | Structured Ingestion at Scale#

Edilitics supports a broad range of file formats to empower users with flexible data ingestion and schema-aware analytics workflows. These file-based integrations enable seamless import of offline or exported datasets—commonly used across departments like finance, engineering, and product analytics—into the Edilitics platform for analysis, transformation, and visualization.

Whether you're uploading CSVs for financial models, Parquet files from data lakes, or Pickle files containing serialized machine learning models, Edilitics ensures secure ingestion, automatic schema inference, and governed access.

Supported File Formats in Edilitics#

File Format	Description
Avro	Row-based data format supporting schema evolution, common in big data pipelines.
CSV	Tabular data stored in comma-separated format, ideal for structured records from spreadsheets or exports.
Excel	Commonly used spreadsheet files for small-scale tabular data.
Feather	High-speed binary format for dataframes, useful in Python or R-based analytics.
JSON	Flexible, structured text format often used for API responses and nested data.
Parquet	Columnar storage optimized for large-scale analytics and compression.
PDF	Extracts structured tables from document-based data (including password-protected PDFs).
Pickle	Python-native binary serialization format for complex objects, including ML models.

How File-Based Integrations Work in Edilitics#

Once uploaded, Edilitics handles your files through a secure, governed ingestion process:

✅ Virus Scanning on Upload

Every uploaded file is scanned for malware or harmful scripts to ensure safe ingestion and platform integrity.

✅ Intelligent Schema Parsing

Edilitics parses each file to infer actual data types (e.g., numeric, categorical, datetime) based on structure and content—avoiding generic string typing and enabling analytics-ready schemas.

✅ Encrypted Storage with Schema Binding

Files and their inferred schema are stored together in private, access-controlled buckets, encrypted at rest. This guarantees consistent data typing across all workflows using the file.

Use Cases Across Teams#

• Finance Teams:

  Upload Excel or CSV exports from ERP systems for reconciliation, forecasting, or reporting.

• Engineering & Product:

  Use JSON logs or Feather files for telemetry analysis, usage metrics, and release analytics.

• Data Science:

  Import Pickle files to deploy trained models or share feature-engineered datasets.

• Business Users:

  Upload structured PDFs with tabular data for parsing and integration into dashboards.

Operational Considerations#

• File Size Limit:

  Maximum upload size is 100 MB per file to ensure performance and stability during parsing and execution.

• Governed Access:

  File-based integrations can be used in replication, transformation, and visualization workflows but cannot be shared independently outside of these workflows unless explicitly granted access via the integration owner or admin.

• Non-Persistent Updates:

  Files are static once uploaded. Any schema or data changes require uploading a new version, ensuring clear version control in critical workflows.

Enterprise-Grade Security for File-Based Workflows#

Edilitics treats every file upload as a potential entry point into enterprise workflows, and applies strict controls accordingly:

• 🔐 End-to-End Encryption –

  Files and metadata encrypted at rest and decrypted only during execution.

• 🧾 Audit Logs –

  Every file upload, schema parse, and workflow execution is logged with user, timestamp, and activity.

• ✅ Governance Ready –

  Fully compliant with internal data policies, supporting SOC 2 and GDPR-aligned practices.

Streamline File-Based Data Ingestion#

Edilitics enables you to transform ad hoc datasets into structured, governed assets with no scripting, consistent schema parsing, and enterprise-grade data security. By supporting a wide spectrum of file formats and embedding schema intelligence directly into the ingestion process, Edilitics makes file uploads a reliable, compliant part of your data analytics lifecycle.