Senior Data Engineer at a tech vendor with 10,001+ employees
Real User
Top 10
Jun 22, 2026
My main use case for Data Hub is data lineage tracking. With Data Hub, we track multiple sources, ingestion sources, and different sources where the data resides in S3. We bring all that metadata into Data Hub to track lineage on the data ingestion patterns that we perform or transformations that we do, and how they move from different tables or assets or the data pipelines. Whatever transformations we do with Spark and S3, Snowflake, all those are being tracked via Data Hub. We have S3 buckets and Snowflake tables, and all those lineage tracking is managed through the platform. My main use case is mostly covered as we used Data Hub for metadata tracking and lineage for whatever transformations that we do so that we can track each transformation down the line.
I use Data Hub for our data lineage, data management, data heritage, and our data dictionary. Our organization is quite large, with about 2,000 people working on different initiatives, and everyone wants to connect to a database somehow. As the data engineering team, we are responsible for connecting every single data source that we have, defining each one, and providing an accurate single source of truth for the data so everyone can have the same understanding of the data they are discussing. Since we are ingesting every database that the company has into our own data infrastructure through different tools, we needed to have a clear understanding of data quality, data lineage, and the data discovery part of the process.
data platform at a tech vendor with 1,001-5,000 employees
Real User
Top 10
May 30, 2026
My main use case for Data Hub is that we use it as a library for all the data assets that we generate. It serves as an internal data mart where people can search for whatever data they need, and they can search by tags, by roles, and then add more metadata to it. This provides visibility to the data. A specific example of how my team uses Data Hub in a real-world scenario is that we collect and manipulate a bunch of data layers. Because we have huge teams, the exposure to data that we have already manipulated can sometimes be hindered when using traditional systems. Data Hub acts as a search engine for all of the data. One example would be when the marketing team was looking for specific data around marketing. They discovered that once they searched it on Data Hub, it was easily visible. They did not have to retrieve it from the raw layer and manipulate it for their usage because another team had already built it. Regarding how my teams interact with Data Hub, we use Data Hub with a self-hosted system. We have connectors which look into multiple data sources, manipulation engines, and orchestration layers to gather the metadata, and then that is pulled into Data Hub. This is how we get data assets in Data Hub.
Our main use case for Data Hub is for data lineage and metadata governance for our UFC project, where we are utilizing multiple databases such as SQL Server, Databricks, and Snowflake. We have adopted Data Hub to create centralized metadata for all these databases. A specific example of how we use Data Hub for metadata governance in our UFC project involves getting data from multiple sources including Excel files, CSV files, APIs, and external databases, storing that data first into Amazon S3 buckets, and then into Snowflake staging areas. We transform the raw data using a DVT model, create a silver layer, and then load data into the gold layer for reporting. With Data Hub, we have a centralized view of the data flow, which makes it easier to track issues in downstream applications such as Power BI reporting. We also use Data Hub for onboarding new team members, as it was previously hectic to provide complete metadata details from our seven to eight data sources and over two hundred tables in our Snowflake database. Now, new team members can refer to the lineage of any table or column to understand the complete flow without relying solely on others.
I work with Data Hub as a user, but I also have some administrative responsibilities there. I'm not a final user; the final users are business users, and I play some administrative roles in the tool to have the metadata information available for all Uber users. I'm a Data Quality Engineer focused on data governance. I manage the metadata information for Uber, and I also use this to apply some data quality rules. My focus in my current job is to apply some rules and manage the metadata information and ensure it is accurate for the end users, which is why I'm using it.
My main use case for Data Hub is to enrich the metadata to classify for PII data. As an administrator, I crawl a number of data sources and bring the metadata into a single place, then assign the ownership, such as a data owner or steward, for all the data assets. With their help, we classify the data into PII direct and indirect, sensitive, non-sensitive, and so on. We add tags and glossary terms onto the data elements. The main use case is for DSAR compliance; for GDPR DSAR compliance, we try to identify the PII data in the catalog so that we know where the PII data is in our data inventory.
We adopted Data Hub in the context of a large enterprise customer operating in a regulated industry with a strong focus on data governance, data discoverability, and ownership clarity across multiple cloud-native platforms. The solution was deployed on AWS, and the main business problem was the lack of a centralized, reliable view of data assets, including poor data discoverability, unclear data ownership and stewardship, limited lineage visibility across ingestion and transformation layers, and high dependency on tribal knowledge held by a few individuals. Data Hub was selected as an enterprise data catalog and metadata backbone with the goal of enabling both technical teams and business users to easily understand, trust, and reuse data. We used Data Hub to create very good data discoverability, assign data ownership and stewardship, improve data quality processes, and establish good data governance for our customer in terms of data catalog, data lineage, and metadata management in general.
My main use case for Acryl Data is to extract insights from customer data. I use Acryl Data for a project in order to identify all the customers and find out which customer buys a lot of items.
Data Hub is an advanced platform designed to streamline data management processes, enhance data accessibility, and provide comprehensive analytics capabilities for informed decision-making. Data Hub offers a unified approach to handling large-scale datasets, empowering organizations to effectively manage, analyze, and extract insights from their data infrastructure. It provides robust features for data integration, storage, and visualization, supporting diverse business needs and driving...
My main use case for Data Hub is data lineage tracking. With Data Hub, we track multiple sources, ingestion sources, and different sources where the data resides in S3. We bring all that metadata into Data Hub to track lineage on the data ingestion patterns that we perform or transformations that we do, and how they move from different tables or assets or the data pipelines. Whatever transformations we do with Spark and S3, Snowflake, all those are being tracked via Data Hub. We have S3 buckets and Snowflake tables, and all those lineage tracking is managed through the platform. My main use case is mostly covered as we used Data Hub for metadata tracking and lineage for whatever transformations that we do so that we can track each transformation down the line.
I use Data Hub for our data lineage, data management, data heritage, and our data dictionary. Our organization is quite large, with about 2,000 people working on different initiatives, and everyone wants to connect to a database somehow. As the data engineering team, we are responsible for connecting every single data source that we have, defining each one, and providing an accurate single source of truth for the data so everyone can have the same understanding of the data they are discussing. Since we are ingesting every database that the company has into our own data infrastructure through different tools, we needed to have a clear understanding of data quality, data lineage, and the data discovery part of the process.
My main use case for Data Hub is that we use it as a library for all the data assets that we generate. It serves as an internal data mart where people can search for whatever data they need, and they can search by tags, by roles, and then add more metadata to it. This provides visibility to the data. A specific example of how my team uses Data Hub in a real-world scenario is that we collect and manipulate a bunch of data layers. Because we have huge teams, the exposure to data that we have already manipulated can sometimes be hindered when using traditional systems. Data Hub acts as a search engine for all of the data. One example would be when the marketing team was looking for specific data around marketing. They discovered that once they searched it on Data Hub, it was easily visible. They did not have to retrieve it from the raw layer and manipulate it for their usage because another team had already built it. Regarding how my teams interact with Data Hub, we use Data Hub with a self-hosted system. We have connectors which look into multiple data sources, manipulation engines, and orchestration layers to gather the metadata, and then that is pulled into Data Hub. This is how we get data assets in Data Hub.
Our main use case for Data Hub is for data lineage and metadata governance for our UFC project, where we are utilizing multiple databases such as SQL Server, Databricks, and Snowflake. We have adopted Data Hub to create centralized metadata for all these databases. A specific example of how we use Data Hub for metadata governance in our UFC project involves getting data from multiple sources including Excel files, CSV files, APIs, and external databases, storing that data first into Amazon S3 buckets, and then into Snowflake staging areas. We transform the raw data using a DVT model, create a silver layer, and then load data into the gold layer for reporting. With Data Hub, we have a centralized view of the data flow, which makes it easier to track issues in downstream applications such as Power BI reporting. We also use Data Hub for onboarding new team members, as it was previously hectic to provide complete metadata details from our seven to eight data sources and over two hundred tables in our Snowflake database. Now, new team members can refer to the lineage of any table or column to understand the complete flow without relying solely on others.
I work with Data Hub as a user, but I also have some administrative responsibilities there. I'm not a final user; the final users are business users, and I play some administrative roles in the tool to have the metadata information available for all Uber users. I'm a Data Quality Engineer focused on data governance. I manage the metadata information for Uber, and I also use this to apply some data quality rules. My focus in my current job is to apply some rules and manage the metadata information and ensure it is accurate for the end users, which is why I'm using it.
My main use case for Data Hub is to enrich the metadata to classify for PII data. As an administrator, I crawl a number of data sources and bring the metadata into a single place, then assign the ownership, such as a data owner or steward, for all the data assets. With their help, we classify the data into PII direct and indirect, sensitive, non-sensitive, and so on. We add tags and glossary terms onto the data elements. The main use case is for DSAR compliance; for GDPR DSAR compliance, we try to identify the PII data in the catalog so that we know where the PII data is in our data inventory.
We adopted Data Hub in the context of a large enterprise customer operating in a regulated industry with a strong focus on data governance, data discoverability, and ownership clarity across multiple cloud-native platforms. The solution was deployed on AWS, and the main business problem was the lack of a centralized, reliable view of data assets, including poor data discoverability, unclear data ownership and stewardship, limited lineage visibility across ingestion and transformation layers, and high dependency on tribal knowledge held by a few individuals. Data Hub was selected as an enterprise data catalog and metadata backbone with the goal of enabling both technical teams and business users to easily understand, trust, and reuse data. We used Data Hub to create very good data discoverability, assign data ownership and stewardship, improve data quality processes, and establish good data governance for our customer in terms of data catalog, data lineage, and metadata management in general.
My main use case for Acryl Data is analytics.
My main use case for Acryl Data is to extract insights from customer data. I use Acryl Data for a project in order to identify all the customers and find out which customer buys a lot of items.