Overview
Is your data unreliable? Quickly and easily elevate your data to new heights of quality, usability, and value. Leverage Generative AI, Machine Learning, specialized algorithms, and comprehensive knowledge bases to normalize important data. The API platform, ready to use out of the box, enables the discovery, matching, and resolution of inconsistencies in company names, organization names, individual names, or addresses within your critical data assets.
Upon launching the EC2 instance, you can immediately begin to call the core API with a single data value, or the Connect API that processes entire datasets with native connections to databases such as AWS RDS, SQL Server, Snowflake, Postgres, MySQL and others - or text files such as CSVs, TSVs, Parquet, or Excel. Effortlessly generate data quality, data consistency, and data matching reports, and create new datasets with standardized, matched, merged, and far more normalized and useful data overall.
The instance can also be easily configured to be a server to other instances on your VPN or across the Cloud, enabling high levels of data quality in applications, business processes, ETL/ELT, or anywhere else usable data is critical to success.
Highlights
- Improve the value of your data assets through data matching and data normalization of alphanumeric data, including organization/company names, individual names, and addresses - uses Generative AI & Machine Learning behind the scenes.
- Deploy Interzoid's entire matching system to an instance of AWS EC2, enabling full-control, unlimited access, and maximum flexibility. Get up and running with a single "launch" click. APIs can be accessed from the command, programmatically, or over HTTP/HTTPS on any port.
- Connects out of the box locally, over the network/VPN, or across the Cloud to data tables on AWS RDS, Postgres, MySQL, Snowflake, SQL Server, and many more, as well as text file datasets including CSV, TSV, Excel, and Parquet.
Details
Typical total price
$0.191/hour
Features and programs
Financing for AWS Marketplace purchases
Pricing
Instance type | Product cost/hour | EC2 cost/hour | Total/hour |
|---|---|---|---|
t2.micro AWS Free Tier | $0.17 | $0.012 | $0.182 |
t2.small | $0.17 | $0.023 | $0.193 |
t2.medium | $0.17 | $0.046 | $0.216 |
t2.large | $0.17 | $0.093 | $0.263 |
t2.xlarge | $0.17 | $0.186 | $0.356 |
t2.2xlarge | $0.17 | $0.371 | $0.541 |
t3.micro AWS Free Tier | $0.17 | $0.01 | $0.18 |
t3.small Recommended | $0.17 | $0.021 | $0.191 |
t3.medium | $0.17 | $0.042 | $0.212 |
t3.large | $0.17 | $0.083 | $0.253 |
Additional AWS infrastructure costs
Type | Cost |
|---|---|
EBS General Purpose SSD (gp3) volumes | $0.08/per GB/month of provisioned storage |
Vendor refund policy
Contact support@interzoid.com
Legal
Vendor terms and conditions
Content disclaimer
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
This AWS instance enables you to quickly and easily identify matches of inconsistent, duplicate/redundant, and otherwise non-normalized data of organization/company names, individual names, and addresses using an AI model built into the application platform.
There are two ways to do this, an API for a single data entity such as an organization name or an individual name, and also the command-line driven CONNECT application for analyzing a text file or database table, generating "match reports", as well as enabling match index tables to be created for more advanced purposes, such as SQL JOINS that overcome data inconsistency issues or matching/appending data between two datasets to get signficantly higher match rates, and also prevent redundant, non-normalized data to enter a database at the point of data collection.
Using the API, a similarity key is generated for the entity in the parameter of the call. Data that is "similar" will generate the same similarity key.
Examples:
GE -> wAR3laPfUVvB784_iH0cw7aQbKhr26sophlZ4z7iqtM General Electric -> wAR3laPfUVvB784_iH0cw7aQbKhr26sophlZ4z7iqtM
AMAZON.COM -> EP88bx0VFDaIh-cOt86c8pOJ6lNkb_TWiKFpmMKXakY Amazon Inc. -> EP88bx0VFDaIh-cOt86c8pOJ6lNkb_TWiKFpmMKXakY
This can be achieved by calling the API that is running on this instance. For example, you can use cURL to make the API call:
$ curl 'http://localhost:8950/getcompanymatchadvanced?company=ibm&algorithm=wide '
Use the generated similarity key, rather than the actual data itself, to match, normalize, and/or sort company name data by similarity. This avoids the problems of data inconsistency, misspellings, and name variations when matching within a single dataset, and can also help matching across datasets or for more advanced searching.
Calling this API enables the capability to be built into an infinite number of applications, processes, pipelines, and more.
For Connect, which supports analysis, processing, and reporting of full datasets, the following data sources are supported.
File Types (requires pathname in the "connection" parameter when calling the API, either locally with full path or at a URL address)
CSV TSV Excel Parquet
Databases (local, on the network, or in the Cloud - requires connection string in the "connection" parameter)
AWS RDS (Postgres & MySQL) AWS Aurora (Postgres & MySQL) Postgres MySQL Azure SQL SQL Server Snowflake Databricks Google Cloud SQL (Postgres & MySQL) SkySQL (MySQL) CockroachDB
All database connections are native connectivity. The source can either be a table within the database or a logical view.
For more documentation, usage, and getting starting info, see the following starting point directory upon launch of the instance: $ /home/ec2-user/interzoid
Additional details
Usage instructions
This AWS instance enables you to quickly and easily identify matches of inconsistent, duplicate/redundant, and otherwise non-normalized data of organization/company names, individual names, and addresses using an AI model built into the application platform.
There are two ways to do this, an API for a single data entity such as an organization name or an individual name, and also the command-line driven CONNECT application for analyzing a text file or database table, generating "match reports", as well as enabling match index tables to be created for more advanced purposes, such as SQL JOINS that overcome data inconsistency issues or matching/appending data between two datasets to get signficantly higher match rates, and also prevent redundant, non-normalized data to enter a database at the point of data collection.
Using the API, a similarity key is generated for the entity in the parameter of the call. Data that is "similar" will generate the same similarity key.
Examples:
GE -> wAR3laPfUVvB784_iH0cw7aQbKhr26sophlZ4z7iqtM General Electric -> wAR3laPfUVvB784_iH0cw7aQbKhr26sophlZ4z7iqtM
AMAZON.COM -> EP88bx0VFDaIh-cOt86c8pOJ6lNkb_TWiKFpmMKXakY Amazon Inc. -> EP88bx0VFDaIh-cOt86c8pOJ6lNkb_TWiKFpmMKXakY
This can be achieved by calling the API that is running on this instance. For example, you can use cURL to make the API call:
$ curl 'http://localhost:8950/getcompanymatchadvanced?company=ibm&algorithm=wide '
Use the generated similarity key, rather than the actual data itself, to match, normalize, and/or sort company name data by similarity. This avoids the problems of data inconsistency, misspellings, and name variations when matching within a single dataset, and can also help matching across datasets or for more advanced searching.
Calling this API enables the capability to be built into an infinite number of applications, processes, pipelines, and more.
For Connect, which supports analysis, processing, and reporting of full datasets, the following data sources are supported.
File Types (requires pathname in the "connection" parameter when calling the API, either locally with full path or at a URL address)
CSV TSV Excel Parquet
Databases (local, on the network, or in the Cloud - requires connection string in the "connection" parameter)
AWS RDS (Postgres & MySQL) AWS Aurora (Postgres & MySQL) Postgres MySQL Azure SQL SQL Server Snowflake Databricks Google Cloud SQL (Postgres & MySQL) SkySQL (MySQL) CockroachDB
All database connections are native connectivity. The source can either be a table within the database or a logical view.
For more documentation, usage, and getting starting info, see the following starting point directory upon launch of the instance: $ /home/ec2-user/interzoid
Resources
Vendor resources
Support
Vendor support
Online support: for assistance, questions, or feed back contact support@interzoid.com
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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