Integrating Polaris Catalog Iceberg Tables with On-Prem Hive/HDFS Data for Hybrid Analytics Using Dremio

Organizations often have a blend of cloud and on-premises data sources, creating a need for tools that can seamlessly bridge these environments. Dremio has introduced a new connector for Polaris Catalogs managed by Snowflake's "Open Catalog" service, designed for Iceberg tables, and provides an open-source catalog solution for flexible data access and interoperability across cloud systems. At the same time, many organizations continue to store valuable historical data in on-premises environments like Hive or HDFS, which often contain legacy records essential for comprehensive analytics.

By integrating Polaris Catalog with on-prem Hive/HDFS data, organizations can leverage the best of both worlds, combining the scalability and flexibility of Iceberg in the cloud with the stability and depth of on-prem data. Through Dremio’s platform, users can create unified datasets that span these environments, enabling complex analytics without costly data transfers. This blog explores the benefits of joining Polaris Iceberg tables with Hive/HDFS data, provides an overview of the setup process, and explains how Dremio’s reflections can accelerate cross-environment analytics.

Benefits of Integrating Polaris Iceberg Tables with Hive/HDFS

Integrating Polaris Catalog and Hive/HDFS in Dremio offers numerous advantages to data teams, from reducing data silos to optimizing performance. Here are some key benefits of using Dremio to unify cloud-based Iceberg tables and on-premises Hive/HDFS data.

Unified Data Access

Connecting Polaris Catalog and Hive/HDFS in Dremio allows organizations to access cloud and on-prem data in one environment. This unified access reduces the need for data duplication or migration, helping to streamline data management and improve query efficiency. Analysts can query and join data from both sources without having to set up complex ETL pipelines or move large datasets. With Dremio, users gain the ability to explore and analyze data from both the Polaris Catalog and Hive/HDFS in a single workspace, simplifying workflows and making hybrid data analytics more accessible.

Integrating data across cloud and on-prem environments enables more powerful and comprehensive analysis. With Dremio’s virtual datasets, users can create views that join Polaris Iceberg tables with Hive/HDFS data, opening up new possibilities for insights that span recent and historical data. For example, joining current transactional data in Polaris with long-term customer records stored in Hive can provide a holistic view of customer behavior. This flexibility empowers teams to derive deeper insights and create richer reports by leveraging data from both environments seamlessly.

Cost and Performance Optimization

Dremio’s reflection feature allows for significant performance improvements by materializing frequently accessed data, reducing the load on both Polaris and Hive/HDFS sources. Users can achieve faster queries without high data transfer costs by configuring reflections on cross-environment views. Reflections enable Dremio to serve results from precomputed data rather than querying the raw data each time, improving speed and reducing compute costs. This optimized performance is particularly beneficial when working with large datasets or running complex analytical queries requiring data from cloud and on-prem sources.

Setting Up Polaris Catalog and Hive/HDFS Connections in Dremio

To enable hybrid analytics that spans Polaris Iceberg tables and on-prem Hive/HDFS data, the first step is to configure each source in Dremio. This setup allows for secure and efficient access to both cloud and on-premises data, providing the foundation for seamless cross-environment queries and analysis. Below are the configuration steps for connecting Polaris Catalog and Hive/HDFS sources in Dremio.

Configuring Polaris Catalog

Connecting Dremio to Polaris Catalog enables access to Iceberg tables stored in the cloud. Polaris Catalog offers a scalable, open-source metastore solution for Iceberg, designed to support flexible data management and easy interoperability across cloud environments.

1. Add Polaris Catalog as a Source
   • Navigate to the Datasets page in Dremio, select the Sources tab, and click the Add Source icon.
   • In the Add Data Source dialog, choose Polaris Catalog from the Metastores section.
2. General Configuration
   • Name: Enter a unique name for the Polaris Catalog source, like “Polaris_CloudData” (avoid special characters).
   • Catalog Name: Specify the name of the Polaris Catalog you want to connect.
   • Endpoint URI: Provide the endpoint URI for the Polaris Catalog service (refer to documentation for guidance on locating this URI).
3. Authentication
   • Choose an authentication method based on your security preferences:
     • Dremio: Store the Polaris Catalog password directly in Dremio.
     • Azure Key Vault or AWS Secrets Manager: Provide the URI (Azure) or ARN (AWS) to securely store and access the Polaris Catalog password.
   • Additionally, configure any necessary credentials for accessing storage, such as AWS S3 or Azure Storage, ensuring Dremio can connect to the Iceberg tables managed by Polaris.
4. Advanced Options
   • Asynchronous Access and Caching: Enable asynchronous access and local caching to optimize query times. This allows Dremio to handle asynchronous requests without waiting for data to load fully from Polaris.
   • Storage Authentication Settings: Define storage authentication settings for S3 or Azure Storage, including credentials or assumed roles if needed.

Once configured, your Polaris Catalog source is ready for use in Dremio, where you can combine it with on-premises Hive/HDFS data in virtual datasets.

Configuring Hive/HDFS Connections

Adding Hive and HDFS as sources in Dremio allows access to on-prem data, facilitating hybrid analytics alongside Polaris Catalog data.

1. Add Hive/HDFS as a Source
   • On the Datasets page, go to the Sources panel, click the Add Source icon, and choose Hive (for Hive data) or HDFS (for Hadoop data) under Object Storage.
2. General Configuration for Hive or HDFS
   • Name: Enter a unique name for the source, such as “Hive_OnPremData” or “HDFS_Storage.”
   • Connection Parameters: Input connection details, including the Hive Metastore host and port for Hive, or the NameNode host and port for HDFS.
3. User Impersonation (Optional)
   • Enabling impersonation allows Dremio to query Hive or HDFS on behalf of specific users, helping to enforce access control based on the permissions associated with Hive or HDFS accounts. This feature is beneficial for managing sensitive on-prem data access.
4. Advanced Options and Metadata Settings
   • Set up caching, metadata refresh, and reflection policies to ensure Dremio provides optimized access to on-prem datasets.
   • Define metadata refresh intervals for top-level objects (such as databases and tables) and specific dataset details to keep your data up-to-date without overloading the system.

By completing these configuration steps, both Polaris Catalog and Hive/HDFS sources will be available in Dremio, enabling users to perform hybrid data analytics that leverage the strengths of each environment. With these connections in place, the next step is to create cross-environment views using Dremio’s virtual datasets and optimize their performance using reflections.

Creating Cross-Environment Views with Dremio Virtual Datasets

With Polaris Catalog and Hive/HDFS sources now configured in Dremio, you can create cross-environment views combining data from cloud-based Iceberg tables and on-prem Hive/HDFS datasets. Dremio’s virtual datasets provide a powerful way to join data across these environments without physically moving or duplicating it, allowing users to perform integrated analytics on a unified data layer.

Using Virtual Datasets for Cross-Environment Joins

Dremio’s virtual datasets enable you to seamlessly create SQL-based views that join data across Polaris and Hive/HDFS. This feature allows you to query and analyze data from multiple environments as if it were a single source.

• Creating a Cross-Environment Join:
  To create a virtual dataset that combines Polaris Iceberg tables and Hive/HDFS data, use Dremio’s SQL editor to perform JOIN operations across sources. For example, you could create a view that joins recent sales data in Polaris with long-term customer records stored in Hive, allowing you to analyze customer purchasing trends over time.
• Example Use Case:
  Suppose you want to analyze customer lifetime value by combining recent purchase data in Polaris with historical customer information in Hive. In the SQL editor, write a query that joins these tables on a common field, such as customer_id, and filter or aggregate the data as needed. This combined view can then be used directly in reports or analytics tools, providing a holistic view of customer behavior.

Curating Data Across Raw, Clean, and Gold Layers

Organizing data into curated layers (often known as raw/bronze, clean/business/silver, and gold/application layers) helps streamline access and ensure that different stages of data processing are clearly defined. This structure allows teams to access data at the level of refinement they need, from raw data for deep exploration to curated, business-ready datasets for decision-making.

• Raw/Bronze Layer:
  The raw layer holds unprocessed data from Polaris and Hive/HDFS, providing direct access to the source data as ingested. This layer serves as the foundation for all transformations and can be revisited if any reprocessing is needed.
• Clean/Business/Silver Layer:
  In the clean layer, data is transformed and standardized for consistency across sources. Here, data from Polaris and Hive can be harmonized, such as by aligning date formats or normalizing naming conventions. This layer is typically where data cleaning and enrichment take place, making it easier to query data without needing to handle inconsistencies.
• Gold/Application Layer:
  The gold layer represents the final, business-ready dataset optimized for reporting and analytics. Here, data is fully curated and structured, often containing aggregated values and metrics that directly support decision-making. For example, a gold-layer view might combine Polaris sales data and Hive customer data to show total lifetime spend by customer, segmented by geography or customer type. Dremio’s reflections can optimize gold-layer views by precomputing frequently accessed data, allowing analysts and BI tools to retrieve insights quickly. By curating data into these layers, teams can streamline access and provide a clear pathway from raw data to refined insights.

Using Dremio’s virtual datasets and these curated data layers, you can easily organize and join data across Polaris and Hive/HDFS, providing a unified and efficient approach to data analysis across cloud and on-prem environments. The next step is to leverage Dremio’s reflections to accelerate performance on these cross-environment views, ensuring fast and scalable access to your hybrid data.

Accelerating Cross-Environment Queries Using Dremio Reflections

Dremio’s reflections play a crucial role in optimizing performance when querying cross-environment data. By creating materialized views of commonly accessed datasets, reflections help accelerate query response times, enabling fast and efficient access to data across both Polaris Catalog and Hive/HDFS. This section provides an overview of reflection types, configuration options for gold-layer views, and refresh policies to keep your reflections up-to-date.

Reflection Types and Use Cases

Dremio offers two main types of reflections — raw and aggregation — each designed to enhance query performance in different ways:

• Raw Reflections:
  Raw reflections store row-level data from a table or view, optimizing queries that need access to specific columns or filtered data subsets. Raw reflections are particularly valuable for large datasets where users often query specific rows or fields. For example, if you have a dataset that joins Polaris’s sales transactions with Hive’s customer details, a raw reflection can speed up queries that filter by customer ID, providing quicker access to detailed transaction records.
• Aggregation Reflections:
  Aggregation reflections precompute summary metrics, such as averages or totals, on specified fields. This type of reflection is ideal for BI and reporting queries that involve aggregation functions like SUM, AVG, or GROUP BY. For instance, if you are analyzing sales data from Polaris and Hive by region or customer segment, an aggregation reflection can precompute the necessary aggregates, making dashboard queries and reports significantly faster.

Setting Up Reflections on Gold Layer Views

Reflections are particularly beneficial for the gold layer, where business-ready datasets are curated for analytics and reporting. Applying reflections to these views ensures that commonly accessed data is precomputed, reducing the need to repeatedly query Polaris and Hive/HDFS sources.

• Creating Reflections:
  To create a reflection on a gold-layer view, navigate to the dataset’s Reflections tab in Dremio, select the reflection type (raw or aggregation), and configure fields based on your data needs. You can specify columns to partition or aggregate, allowing Dremio to store precomputed results that match the structure of your analysis.
• Example:
  Suppose you have a gold-layer view that aggregates sales data by region and product category. To optimize this view, set up an aggregation reflection with dimensions like “region” and “product category” and measures such as “total sales.” This configuration allows Dremio to serve up precomputed summaries, reducing query times for analyses that track regional sales performance and eliminating the need for on-the-fly calculations.

Reflection Refresh Policies: Incremental and Live Refresh

To ensure that reflections remain current without overloading system resources, Dremio offers options for incremental and live refresh on iceberg tables, allowing you to select the best refresh strategy based on data update patterns and usage frequency.

• Incremental Refresh:
  Incremental refresh updates only new or modified data in a reflection, which is particularly efficient for data sources with frequent appends or updates. For example, suppose Polaris tables receive new daily sales data while Hive data remains relatively static. In that case, an incremental refresh ensures that the reflection stays current with only the latest additions, minimizing compute overhead.
• Live Refresh:
  Live refresh keeps reflections in sync with the underlying datasets in real-time, making it ideal for use cases requiring up-to-date insights. For instance, a live refresh policy is valuable for dashboards displaying near-real-time metrics, like inventory levels or customer transactions. Live refresh ensures that the latest data is always available for reporting without manual updates.

By setting appropriate refresh policies, Dremio ensures that cross-environment reflections stay accurate and efficient, enabling faster data access while optimizing system resources. With these reflections in place, analysts and decision-makers can enjoy fast, reliable access to hybrid data, supporting more responsive and data-driven insights across the organization.

Practical Use Case Scenarios

Let's explore a few practical scenarios to showcase the benefits of combining Polaris Catalog Iceberg tables with Hive/HDFS data. These examples demonstrate how Dremio’s cross-environment capabilities and reflections can help solve real-world analytics challenges by leveraging data across cloud and on-premises environments.

Scenario 1: Customer Lifetime Value Analysis

Understanding customer lifetime value (CLV) is crucial for guiding marketing and retention strategies in a retail or e-commerce setting. Recent customer engagement data often resides in Polaris, while historical transaction data remains in Hive.

• Objective:
  Analyze customer lifetime value by combining recent purchase data from Polaris with historical transactions in Hive to create a unified view of each customer’s purchase history.
• Solution with Dremio:
  Create a virtual dataset that joins Polaris tables with Hive data based on a common identifier, such as customer_id. This view can then aggregate purchase data by the customer over time.
• Benefit of Reflections:
  Implementing an aggregation reflection with “customer_id” as a dimension and “total spend” as a measure precomputes each customer’s total spending. This setup allows fast access to CLV metrics, enabling marketing teams to segment customers by value and tailor outreach efforts accordingly quickly.

Scenario 2: Real-Time Inventory Monitoring

Keeping inventory data up-to-date for manufacturing and logistics companies is essential to ensure supply meets demand. Polaris can store recent inventory transactions, while Hive stores historical stock records.

• Objective:
  Monitor inventory levels in real time by combining recent updates in Polaris with historical data in Hive to create a comprehensive view of stock levels across warehouses.
• Solution with Dremio:
  Use Dremio’s SQL editor to create a virtual dataset that joins Polaris’s real-time inventory data with Hive’s historical records. This combined view can then be used to track changes in inventory over time.
• Benefit of Reflections:
  Apply a raw reflection to this view, partitioning by “product_id” and “warehouse_id.” With a live refresh policy, the reflection stays current with inventory updates from Polaris, ensuring that the inventory dashboard remains accurate and up-to-date for operational decision-making.

Scenario 3: Financial Performance Analysis

Organizations often analyze financial performance by combining current revenue data stored in Polaris with expense and budget records in Hive.

• Objective:
  Join revenue data from Polaris with budget and expense records in Hive to create a comprehensive view of financial performance. This enables the finance team to monitor profitability and budget utilization in real time.
• Solution with Dremio:
  Create a gold-layer virtual dataset that joins Polaris revenue data with Hive’s expense records by common dimensions such as “department” or “project code.” The dataset can then be used in reports and dashboards to show key financial metrics like profit margins and budget consumption.
• Benefit of Reflections:
  Configure an aggregation reflection on the dataset, with dimensions for “department” and “time period,” and measures such as “total revenue” and “total expenses.” This reflection allows for rapid access to precomputed financial metrics, enabling finance teams to monitor KPIs without long query times or heavy resource consumption.

These scenarios highlight the value of Dremio’s virtual datasets and reflections for cross-environment analytics. By joining Polaris and Hive/HDFS data, Dremio enables companies to extract valuable insights from both cloud and on-premises data sources. These insights support timely, data-driven decisions across departments, from marketing and operations to finance and strategy.

Conclusion

Integrating Polaris Catalog Iceberg tables with on-premises Hive/HDFS data in Dremio unlocks the potential of hybrid data environments. By enabling seamless access and querying of both cloud-based Iceberg tables and on-prem data, Dremio empowers organizations to bring together valuable historical and real-time data for comprehensive analytics without costly data migrations or complex data engineering.

In summary, Dremio’s platform bridges the cloud and on-prem data divide, providing a unified, high-performance solution for hybrid data analytics. By combining the strengths of Polaris and Hive/HDFS in a single environment, organizations can gain a deeper understanding of their data, drive operational efficiencies, and deliver real-time insights that support strategic growth.

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