AWS Big Data Blog

How Eightfold AI implemented metadata security in a multi-tenant data analytics environment with Amazon Redshift

This is a guest post co-written with Arun Sudhir from Eightfold AI.

Eightfold is transforming the world of work by providing solutions that empower organizations to recruit and retain a diverse global workforce. Eightfold is a leader in AI products for enterprises to build on their talent’s existing skills. From Talent Acquisition to Talent Management and talent insights, Eightfold offers a single AI platform that does it all.

The Eightfold Talent Intelligence Platform powered by Amazon Redshift and Amazon QuickSight provides a full-fledged analytics platform for Eightfold’s customers. It delivers analytics and enhanced insights about the customer’s Talent Acquisition, Talent Management pipelines, and much more. Customers can also implement their own custom dashboards in QuickSight. As part of the Talent Intelligence Platform Eightfold also exposes a data hub where each customer can access their Amazon Redshift-based data warehouse and perform ad hoc queries as well as schedule queries for reporting and data export. Additionally, customers who have their own in-house analytics infrastructure can integrate their own analytics solutions with Eightfold Talent Intelligence Platform by directly connecting to the Redshift data warehouse provisioned for them. Doing this gives them access to their raw analytics data, which can then be integrated into their analytics infrastructure irrespective of the technology stack they use.

Eightfold provides this analytics experience to hundreds of customers today. Securing customer data is a top priority for Eightfold. The company requires the highest security standards when implementing a multi-tenant analytics platform on Amazon Redshift.

The Eightfold Talent Intelligence Platform integrates with Amazon Redshift metadata security to implement visibility of data catalog listing of names of databases, schemas, tables, views, stored procedures, and functions in Amazon Redshift.

In this post, we discuss how the Eightfold Talent Lake system team implemented the Amazon Redshift metadata security feature in their multi-tenant environment to enable access controls for the database catalog. By linking access to business-defined entitlements, they are able to enforce data access policies.

Amazon Redshift security controls addresses restricting data access to users who have been granted permission. This post discusses restricting listing of data catalog metadata as per the granted permissions.

The Eightfold team needed to develop a multi-tenant application with the following features:

  • Enforce visibility of Amazon Redshift objects on a per-tenant basis, so that each tenant can only view and access their own schema
  • Implement tenant isolation and security so that tenants can only see and interact with their own data and objects

Metadata security in Amazon Redshift

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. Many customers have implemented Amazon Redshift to support multi-tenant applications. One of the challenges with multi-tenant environments is that database objects are visible to all tenants even though tenants are only authorized to access certain objects. This visibility creates data privacy challenges because many customers want to hide objects that tenants can’t access.

The newly released metadata security feature in Amazon Redshift enables you to hide database objects from all other tenants and make objects only visible to tenants who are authorized to see and use them. Tenants can use SQL tools, dashboards, or reporting tools, and also query the database catalog, but they will only see appropriate objects for which they have permissions to see.

Solution overview

Exposing a Redshift endpoint to all of Eightfold’s customers as part of the Talent Lake endeavor involved several design choices that had to be carefully considered. Eightfold has a multi-tenant Redshift data warehouse that had individual customer schemas for customers, which they could connect to using their own customer credentials to perform queries on their data. Data in each customer tenant can only be accessed by the customer credentials that had access to the customer schema. Each customer could access data under their analytics schema, which was named after the customer. For example, for a customer named A, the schema name would be A_analytics. The following diagram illustrates this architecture.

Although customer data was secured by restricting access to only the customer user, when customers used business intelligence (BI) tools like QuickSight, Microsoft Power BI, or Tableau to access their data, the initial connection showed all the customer schemas because it was performing a catalog query (which couldn’t be restricted). Therefore, Eightfold’s customers had concerns that other customers could discover that they were Eightfold’s customers by simply trying to connect to Talent Lake. This unrestricted database catalog access posed a privacy concern to several Eightfold customers. Although this could be avoided by provisioning one Redshift database per customer, that was a logistically difficult and expensive solution to implement.

The following screenshot shows what a connection from QuickSight to our data warehouse looked like without metadata security turned on. All other customer schemas were exposed even though the connection to QuickSight was made as customer_k_user.

Approach for implementing metadata access controls

To implement restricted catalog access, and ensure it worked with Talent Lake, we cloned our production data warehouse with all the schemas and enabled the metadata security flag in the Redshift data warehouse by connecting to SQL tools. After it was enabled, we tested the catalog queries by connecting to the data warehouse from BI tools like QuickSight, Microsoft Power BI, and Tableau and ensured that only the customer schemas show up as a result of the catalog query. We also tested by running catalog queries after connecting to the Redshift data warehouse from psql, to ensure that only the customer schema objects were surfaced—It’s important to validate that given tenants have access to the Redshift data warehouse directly.

The metadata security feature was tested by first turning on metadata security in our Redshift data warehouse by connecting using a SQL tool or Amazon Redshift Query Editor v2.0 and issuing the following command:

ALTER SYSTEM SET metadata_security = TRUE;

Note that the preceding command is set at the Redshift cluster level or Redshift Serverless endpoint level, which means it is applied to all databases and schemas in the cluster or endpoint.

In Eightfold’s scenario, data access controls are already in place for each of the tenants for their respective database objects.

After turning on the metadata security feature in Amazon Redshift, Eightfold was able to restrict database catalog access to only show individual customer schemas for each customer that was trying to connect to Amazon Redshift and further validated by issuing a catalog query to access schema objects as well.

We also tested by connecting via psql and trying out various catalog queries. All of them yielded only the relevant customer schema of the logged-in user as the result. The following are some examples:

analytics=> select * from pg_user;
usename | usesysid | usecreatedb | usesuper | usecatupd | passwd | valuntil | useconfig 
customer_k_user | 377 | f | f | f | ******** | | 
(1 row)

analytics=> select * from information_schema.schemata;
catalog_name | schema_name | schema_owner | default_character_set_catalog | default_character_set_schema | default_character_set_name | sql_path 
analytics | customer_k_analytics | customer_k_user | | | | 
(1 row)

The following screenshot shows the UI after metadata security was enabled: only customer_k_analytics is seen when connecting to the Redshift data warehouse as customer_k_user.

This ensured that individual customer privacy was protected and increased customer confidence in Eightfold’s Talent Lake.

Customer feedback

“Being an AI-first platform for customers to hire and develop people to their highest potential, data and analytics play a vital role in the value provided by the Eightfold platform to its customers. We rely on Amazon Redshift as a multi-tenant Data Warehouse that provides rich analytics with data privacy and security through customer data isolation by using schemas. In addition to the data being secure as always, we layered on Redshift’s new metadata access control to ensure customer schemas are not visible to other customers. This feature truly made Redshift the ideal choice for a multi-tenant, performant, and secure Data Warehouse and is something we are confident differentiates our offering to our customers.”

– Sivasankaran Chandrasekar, Vice President of Engineering, Data Platform at Eightfold AI


In this post, we demonstrated how the Eightfold Talent Intelligence Platform team implemented a multi-tenant environment for hundreds of customers, using the Amazon Redshift metadata security feature. For more information about metadata security, refer to the Amazon Redshift documentation.

Try out the metadata security feature for your future Amazon Redshift implementations, and feel free to leave a comment about your experience!

About the authors

Arun Sudhir is a Staff Software Engineer at Eightfold AI. He has more than 15 years of experience in design and development of backend software systems in companies like Microsoft and AWS, and has a deep knowledge of database engines like Amazon Aurora PostgreSQL and Amazon Redshift.

Rohit Bansal is an Analytics Specialist Solutions Architect at AWS. He specializes in Amazon Redshift and works with customers to build next-generation analytics solutions using AWS Analytics services.

Anjali Vijayakumar is a Senior Solutions Architect at AWS focusing on EdTech. She is passionate about helping customers build well-architected solutions in the cloud.