No Code API Consumption platform

Simplifies the complexities in parsing JSON & XML structures.

Request Engine

Convert Relational tables to JSON/XML/API in few clicks.

Response Engine

Convert JSON/XML/API to Relational tables in few clicks.

ERD Engine

Dashboards & Job Logs to view the complete run statistics.

Productivity

Reduce Development effort for API Consumption process (touch it & take it approach) by 95%

Technology Compatibility

API Finz Modules are exposed as secured REST APIs which are compatible with Enterprise
Schedulers, iPAAS platforms, POSTMAN and Web Applications

Execution Stats

Dashboards & Job Logs to view the complete run statistics

Features

REQUEST

  • Generate JSON/XML structures from structured data.
  • Input data can be Tables, Views or Flat Files.
  • Limit the number of objects to be created for POST.

RESPONSE

  • Converts Complex JSON/XML structures to structured format in few clicks.
  • Can Flatten or Normalize unstructured data.
  • Complex Arrays and Objects can be converted in few clicks.

CONNECTIONS

  • Direct API Connects with advanced authentication methods.
  • Handle Pagination & Parameters in API operations.
  • Easy Connections to RDBMS & Cloud storages.

SCHEDULER & DASHBOARDS

  • Scheduler to execute the API Integrations on defined frequencies.
  • Dashboards to analyze the usage.
  • Detailed reports on the conversions.

How it works

Solutions

Slide Structured Files 1.Parse the REST API Response to Flat Files Structured files (delimited files)
with the relationship key Model : Generate ERD from REST API metadata
Schedule the process

REST API JSON Data with complex arrays in one object File Storage can be in AWS S3 or Azure Blob or Data Lake or On-Prem
Each Array in JSON is created as a Child table with a relation key to the Parent table

Parses REST API Response with Authentications & creates structured data in RDBMS Tables
Integrate : Build Pipeline
Connect : Configure REST API & File Storage Connections
Slide Tables 2.Parse the REST API Response to Tables Structured data loaded to the
multiple related Tables Model : Generate ERD from REST API metadata
Schedule the process


REST API JSON Data with complex arrays in one object Tables can be any RDBMS in On-Prem or Cloud
Each Array in JSON is created as a Child table with a relation key to the Parent table

Parses REST API Response with Authentications & creates structured data in RDBMS Tables Integrate : Build Pipeline
Connect : Configure REST API & RDBMS (JDBC) Connections
Slide REST API 3. Build JSON Payload & Perform the REST API Request from the Tables JSON Data with complex arrays in one object
Model : Generate ERD from REST API metadata
Schedule the process

Tables Structured Data in multiple related tables Tables can be any RDBMS in On-Prem or Cloud
Each related table is created as an Array in JSON object
Converts the structured tables to JSON Payload & performs REST API Request with

authentication
Integrate : Build Pipeline
Connect : Configure REST API & RDBMS (JDBC) Connections
Slide REST API 4. Build JSON Payload & Perform the REST API Request from the Flat Files JSON Data with complex
arrays in one object
Model : Generate ERD from REST API metadata Schedule the process

Structured Files Structured Data in multiple related Flat Files File Storage can be in AWS S3 or Azure Blob or Data Lake or On-Prem Each related Flat File is created as an Array in JSON object
Converts the structured files to JSON Payload & performs REST API Request with
authentication
Integrate : Build Pipeline
Connect : Configure REST API & File Storage Connections
Slide Semi-Structured Files 5. Convert the Structured Tables to JSON or XML JSON or XML files with the complex
arrays in one object
Tables can be any RDBMS in On-Prem or Cloud Model : Generate ERD from JSON or XML template Schedule the process

Tables Structured Data in multiple related Flat Files File Storage can be in AWS S3 or Azure Blob or Data Lake or On-Prem Each related table is created as an Array in JSON object
Converts the structured tables to JSON or XML files & stores in File Storage Integrate : Build Pipeline
Connect : Configure File Storage Connection & RDBMS (JDBC) Connection
Slide Semi-Structured Files 6. Convert the Structured Files to JSON or XML JSON or XML files with the complex
arrays in one object
Model : Generate ERD from JSON or XML template Schedule the process

Structured Files Structured Data in multiple related Flat Files File Storage can be in AWS S3 or Azure Blob or Data Lake or On-Prem Each related table is created as an Array in JSON object
Converts the structured files to JSON or XML files & stores in File Storage Integrate : Build Pipeline
Connect : Configure File Storage Connections
Slide Flat Files 7. Parse the JSON/XML Files to Flat Files Structured files (delimited files)
with the relationship key
Model : Generate ERD from JSON or XML template Schedule the process

Semi-Structured Files JSON or XML files with the complex arrays in one object File Storage can be in AWS S3 or Azure Blob or Data Lake or On-Prem Each Array in JSON or XML is created as new Child File with a relation key to the

Parent File
Parses JSON or XML files and creates structured files in File Storage Integrate : Build Pipeline
Connect : Configure File Storage Connections
Slide Tables 8. Parse the JSON/XML Files to Tables Structured data loaded to the multiple related tables Tables can be any RDBMS in On-Prem or Cloud Model : Generate ERD from JSON or XML template Schedule the process

Semi-Structured Files JSON or XML files with the complex arrays in one object File Storage can be in AWS S3 or Azure Blob or Data Lake or On-Prem Each Array in JSON or XML is created as a Child table with a relation key to

the Parent table
Parses JSON or XML files and creates structured files in File Storage Integrate : Build Pipeline
Connect : Configure File Storage Connection & RDBMS (JDBC) Connection
Slide Azure 9. Data Copy from AWS to Azure Unstructured files in AWS Unstructured files in Azure Structured, Semi-Structured and Schedule the process

AWS Structured, Semi-Structured and File Storage can be in AWS S3 or Azure Blob or Data Lake or On-Prem Copy the Files from AWS to Azure Integrate : Build Pipeline
Connect : Configure File Storage Connections
Slide AWS 10. Data Copy from Azure to AWS Structured, Semi-Structured and Schedule the process

Unstructured files in Azure Unstructured files in AWS Azure Structured, Semi-Structured and File Storage can be in AWS S3 or Azure Blob or Data Lake or On-Prem Copy the Files from Azure to AWS Integrate : Build Pipeline
Connect : Configure File Storage Connections
Slide Data Lake 11. Replicate Tables to Data Lake Structured files (csv or tab) Tables can be any RDBMS in On-Prem or Cloud Schedule the process

Tables Structured data in the multiple
related tables
Data Lake can be in AWS or Azure Copy the Files from Azure to AWS Integrate : Build Pipeline
Connect : Configure RDBMS (JDBC) Connection and Data Lake Connection

Demo Videos

CONNECTION FTP Connection
Method
SFTP Connection
Method
Rest API - Key
Connection Method
Database Connection
Method
Rest API - Dynamic
Variable
Connection Method
Azure Blob Storage
Connection Method
Azure Data Lake
Storage Connection
Method
AWS S3 Connection
Method
Rest OAUTH 2.0 JWT
Connection Method
Rest API - OAUTH 2.0
Client Credentials
Connection Method
Rest API - No Auth
Connection Method
Rest API - Basic Auth
Connection Method
MODEL Model Generation from
REST End Point
Model Generation from
JSON File
INTEGRATION Table Replication to
Data Lake
Response - Advance
Features
Simple Response Request- Advance
Features
Simple Request Convert to Table and
File to JSON or XML
Files Copy into
Data Lake

Pricing Catalog

Slide PRICING PRICING Generate ERD, Templates Connections Integration Integration Run Accounts Usage FEATURES CONTACT US Support 100 1000 100 10 Email 5 Users *Dedicated Email/Phone Call STANDARD Email/Phone Call CONTACT US 5000 500 50 10 Users *Dedicated PRO CONTAC T US 500 ENTERPRISE Unlimited *Dedicated Unlimited Email/Phone Call Unlimited Unlimited Unlimited