How Dremio’s Reflections Enhance Iceberg Lakehouses, Data Availability, AI/BI, and Infrastructure Scalability

The demand for quick, actionable insights is higher than ever. Businesses are moving beyond traditional data warehouses to adopt lakehouses and other flexible data architectures that better support real-time analytics, BI, and AI applications. Dremio is at the forefront of this shift, providing a robust, high-performance hybrid lakehouse platform that enables fast, scalable analytics in databases, data warehouses, and directly on data lake storage in the cloud or on-prem. It’s designed to bridge the gap between data lakes and consumers, offering an SQL-based interface that’s as responsive as a data warehouse but as flexible as a data lake.

Enhancing Dremio’s already industry-leading performance capabilities is its Data Reflections feature. Data Reflections are essentially Dremio’s answer to the headaches of materialized views and BI Cubes, using Apache Iceberg-based materializations that act as an optimized parquet representation with metadata that acts as an index that auto substituted when queries can benefit from it. With reflections, users can create pre-aggregated or pre-filtered logical views in Dremio's semantic layer and then materialize them as reflections by flipping a switch or with a simple ALTER TABLE statement. Reflections allow queries to run faster, more efficiently, and with minimal load on the original data sources. Dremio avoids repeatedly running the same transformations and computations by effectively caching these optimized snapshots, enabling smoother, faster analytics experiences. This article will cover several use cases for Reflections that eliminate many of the challenges in Lakehouse analytics.

But first, let’s dive into the types of data reflections in Dremio and how they serve different needs in an analytics ecosystem:

Types of Reflections

1. Raw Reflections
   • Raw reflections are row-level snapshots of datasets and represent all or part of the underlying data in its raw form. By precomputing raw reflections, Dremio can speed up queries that frequently require data filtering, sorting, or scanning large tables.
   • Use Case: Suppose a dataset consists of hundreds of columns, but most queries only require a small subset of these columns. A raw reflection allows you to create a lightweight version of the dataset with only the required columns. This subset can then be accessed instantly without scanning the entire dataset each time, making queries faster and more efficient.
2. Aggregation Reflections
   • Aggregation reflections are ideal for BI-style queries that involve aggregations, such as group-by queries and summarizations. Instead of recalculating aggregates for each query, Dremio stores the results in aggregation reflections, enabling real-time access to pre-summarized data.
   • Use Case: In a BI dashboard that tracks daily sales metrics by region, aggregation reflections can speed up queries that calculate total sales per day, average sales per region, and other such metrics. By pre-aggregating the data, Dremio reduces the time and compute resources needed to deliver these insights, enhancing dashboard performance.
3. External Reflections
   • External reflections are precomputed data tables materialized outside Dremio but mapped within it, such as tables in a relational database or data warehouse. This reflection type is beneficial when you already have a precomputed dataset in another source and want to optimize queries without maintaining another copy in Dremio.
   • Use Case: If your organization stores critical sales data in an external warehouse and regularly updates a summary table, an external reflection in Dremio can reference this table directly. Users can query the summary data through Dremio, which will substitute the external reflection in place of scanning the entire source dataset, providing faster insights without data duplication.
4. Starflake Reflections
   • Starflake reflections are designed to optimize complex join queries within star or snowflake schemas by leveraging non-expanding joins between fact and dimension tables. These reflections simplify query planning and can accelerate queries even if they involve just a subset of the join conditions.
   • Use Case: Imagine a star schema with a sales fact table and associated location and employee dimension tables. By creating a starflake reflection, Dremio can accelerate queries that join these tables, whether the query requests all or just some of the dimensions. This reflection type is particularly valuable for BI users working with complex schemas, as it allows for faster, more flexible querying on intricate data models.

Each type of reflection serves a unique purpose in optimizing data access, allowing Dremio to meet a range of analytics requirements, from raw data scanning to summarized BI reporting. The flexibility to choose between raw, aggregation, and starflake reflections makes Dremio a powerful tool for managing both real-time queries and historical data analyses on lakehouse data.

As we explore more deeply, you’ll see how these reflections enable Dremio to outperform traditional BI solutions, streamline data preparation, enhance the performance of Iceberg lakehouses, and improve data availability across different sources. With Dremio’s reflections, the path to achieving faster, more reliable insights is clearer and more efficient than ever.

The Power of Aggregation Reflections in BI Dashboards vs. BI Cubes

Business Intelligence (BI) dashboards are a mainstay for organizations, providing decision-makers with instant access to key metrics, trends, and analytics. Traditionally, many BI solutions relied on BI cubes to enable fast querying on large datasets, but BI cubes come with limitations: they require regular manual updates, significant compute resources, and are often difficult to modify to accommodate changing business needs.

Dremio’s aggregation reflections present a modern, flexible alternative to BI cubes, enabling high-performance queries with minimal maintenance. Aggregation reflections store precomputed summaries (such as averages, counts, sums, and group-by results) that can be immediately accessed to fuel BI dashboards. By automating the refresh and storage of these aggregations, Dremio offers a dynamic, scalable way to keep BI dashboards fast and responsive without the overhead of traditional BI cubes.

Let’s explore why aggregation reflections offer a superior solution for BI dashboards:

1. Real-Time Performance for BI Dashboards

One of the main challenges with traditional BI cubes is the latency involved in updating and querying them. Since BI cubes often need to be manually refreshed, data in the dashboard can become stale quickly, especially in fast-moving business environments. Dremio’s aggregation reflections, on the other hand, can be configured to auto-refresh as new data becomes available, allowing BI dashboards to stay current without manual intervention.

Example: Suppose a company’s BI team needs to track real-time sales performance across regions. With aggregation reflections, each region’s sales totals and averages can be precomputed, stored, and refreshed as frequently as needed, ensuring that the dashboard always reflects up-to-the-minute data. In contrast, a traditional BI cube would need to be manually updated, often delaying real-time insights and creating bottlenecks.

2. Flexibility in Querying and Dynamic Analysis

BI cubes are designed to answer specific questions or support specific queries. They are often pre-structured to support particular combinations of dimensions (e.g., product categories, sales channels, regions), which limits the flexibility for ad hoc querying. Aggregation reflections, however, can be built to support a wide range of group-by combinations and aggregations, enabling a more dynamic and flexible analytics experience.

Example: Let’s say the BI dashboard needs to support dynamic querying, where users can drill down into sales data by different dimensions, such as product, region, or time period. With aggregation reflections, Dremio precomputes multiple dimensions and measures, allowing users to group by any combination of dimensions without re-running complex calculations. This flexibility enables users to explore data in new ways and adapt to changing analytical needs without requiring a redesign of the underlying data model.

3. Cost and Maintenance Savings Compared to BI Cubes

Maintaining BI cubes can be costly and time-consuming, as they require regular rebuilds and dedicated infrastructure. Dremio’s aggregation reflections eliminate these manual updates by automating refresh processes. Furthermore, because Dremio is a lakehouse platform, it leverages low-cost cloud object storage to store reflection data, reducing the need for specialized, high-cost hardware traditionally required for BI cubes.

Example: For a company with growing data volumes and increasingly complex dashboards, relying on BI cubes may mean investing in additional compute resources to scale up infrastructure. With Dremio, aggregation reflections on cloud storage enable seamless scaling without the need for specialized infrastructure, and updates can occur as frequently as needed. This efficiency lowers both compute costs and administrative overhead.

4. Enhanced Query Performance Without Data Movement

To achieve fast query speeds, BI cubes often require data to be extracted, transformed, and loaded (ETL) into the cube structure, resulting in substantial data movement and possible data duplication. Dremio’s reflections eliminate the need for ETL processes dedicated solely to BI cubes by working directly on the data in place, whether it’s in a data lake, an external warehouse, or even on Iceberg tables. This not only reduces latency but also decreases the operational burden associated with ETL pipelines.

Example: Imagine a retail company with vast customer data stored in an Iceberg table on their data lake. Rather than creating an ETL pipeline to move this data into a BI cube, the company can define aggregation reflections on the table, allowing Dremio to read and accelerate queries directly. This results in faster insights without the overhead of as much data duplication and pipeline management.

5. Streamlined Analytics for Diverse BI Needs

As businesses grow, their analytical requirements evolve, often leading to the need for more granular or complex reporting. With BI cubes, adapting to new reporting needs often means redesigning or creating additional cubes. Dremio’s aggregation reflections simplify this process by offering a more adaptable, versatile framework that easily scales to new reporting requirements.

Example: A finance team may initially require monthly revenue aggregates but later decide to include customer segmentation metrics for advanced analysis. With Dremio’s aggregation reflections, the team can easily define new summaries or adjust existing reflections, allowing for quick access to these new metrics without the cost or delay associated with reconfiguring BI cubes.

Aggregation reflections are a powerful asset for BI teams looking to enhance dashboard responsiveness, lower costs, and increase flexibility. By replacing traditional BI cubes with these flexible, automated reflections, organizations can meet their dynamic reporting needs while minimizing the operational overhead associated with BI maintenance.

In the next section, we’ll discuss how Dremio can accelerate data preparation processes and improve data accessibility for both BI and AI use cases, setting the stage for faster, more efficient insights.

Accelerated Data Preparation with Reflections for BI and AI

Data preparation is the essential foundation of any analytics process, whether for BI dashboards or AI-driven insights. However, preparing data can be time-consuming, requiring transformations, joins, filtering, and complex aggregations to get data into a format that’s ready for analysis. Traditionally, data prep workflows often rely on multiple data platforms, ETL processes, and manual interventions that can add delays, especially when transforming large datasets.

Dremio’s reflections offer a game-changing approach to data preparation, enabling teams to perform and accelerate these tasks directly on raw data. Reflections allow for rapid access to pre-prepared, optimized data snapshots, which are invaluable for supporting both BI dashboards and AI models that demand fresh, accurate data.

Here’s how Dremio’s reflections streamline and accelerate data prep:

1. Efficient Data Preparation Steps with SQL on Dremio

With Dremio’s SQL-based interface, data teams can perform preparation steps directly on data lake storage, avoiding time-consuming data movement between platforms. Complex transformations, filtering, and joining operations can be done within Dremio, leveraging the power of SQL to ensure data is in the right format for consumption.

Example: Imagine a data scientist preparing data for a recommendation model on e-commerce transactions. They need to clean, transform, and aggregate transactional data across several dimensions, such as customer segments, product categories, and purchase history. By using Dremio, they can perform these transformations in SQL, join the necessary tables, and immediately store these results in a reflection, all without the need for ETL pipelines.

2. Reflection-Accelerated Access to Prepared Data

Once data prep transformations are complete, creating a reflection on this pre-prepared data enables near-instant access to it in future queries. Reflections store the results of complex transformations, so subsequent BI and AI queries can access the precomputed data without needing to rerun the entire prep process. This accelerates insights and reduces strain on compute resources.

Example: Suppose a marketing team is analyzing customer demographics, transactional data, and product feedback to determine which campaigns to target at specific customer groups. Dremio’s reflections allow them to store this merged, cleaned, and aggregated data as a reflection. Now, queries run at top speed since Dremio can directly access the reflection without reapplying transformations each time a team member needs to analyze the data.

3. Simplifying Complex Joins and Transformations

Joining tables and applying transformations are everyday data prep tasks that require significant compute power, mainly when dealing with large datasets. Dremio’s reflections simplify this by storing the results of joins and transformations, allowing teams to access the data in its final form without reprocessing.

Example: Consider a telecom company preparing usage data, customer demographics, and billing information to analyze customer churn. These datasets might be stored in separate tables with millions of rows each. With Dremio, they can create a reflection that joins and transforms this data, including only the necessary fields for churn analysis, which makes querying the final result much faster and more efficient for ongoing analysis.

4. Enabling Faster AI Model Training and Data Iterations

For AI and machine learning workflows, fast access to pre-prepared data can make a significant difference. When data is prepared and stored in reflections, data scientists can quickly access training datasets, test different features, and iterate on models without waiting for lengthy data prep steps. This translates into faster model iteration cycles and more productive data science workflows.

Example: A data science team working on a predictive maintenance model for manufacturing equipment needs to combine operational metrics, machine sensor data, and maintenance logs. By using Dremio to prepare this data and create reflections on the prepared dataset, they can access it instantly for model training. This eliminates the need to reprocess the data every time they retrain or test the model, saving valuable time and computing resources.

5. Reducing Compute Costs and Streamlining Resources

Preparing data repeatedly can lead to high compute costs, particularly when working with large datasets. Dremio minimizes the need for repeated transformations and joins by using reflections, reducing overall compute resource requirements. This is particularly beneficial in large organizations or in data lake environments where resource usage must be carefully managed.

Example: In a large retail organization, an analytics team runs weekly reports on regional sales, inventory levels, and customer trends. Instead of repeatedly running the same data prep transformations each week, the team can store the prepared data as a reflection. This way, Dremio can fulfill these queries directly from the reflection, bypassing the need for additional compute power each time the data is accessed, and providing substantial cost savings over time.

6. Use Cases for Reflection-Optimized Data Prep

Dremio reflections add particular value in various BI and AI use cases by supporting faster data prep, including:

• Data aggregations for customer segmentation: Storing aggregated customer metrics, such as average spend or frequency of purchases, can accelerate marketing and sales analysis.
• Feature engineering for machine learning models: Reflections can store complex features created from raw data, enabling quicker access and reuse for model training.
• Cross-departmental reporting: Multiple teams can access consistent, pre-prepared data views for sales, finance, or operations, reducing redundancy and maintaining data consistency.
• Real-time analytics on streaming data: In cases where reflections are updated in near real-time, organizations can quickly analyze streaming data, such as IoT or sensor data, without the lag typically associated with data prep pipelines.

Dremio empowers BI and AI teams to gain actionable insights more efficiently by enabling faster, reflection-optimized data preparation. Teams can focus on high-value analysis and model-building rather than waiting for data prep processes to complete, significantly speeding up the path from raw data to insights.

In the next section, we’ll examine the role of reflections in Iceberg lakehouses and discuss how Dremio’s reflection capabilities enhance performance, availability, and cost efficiency for real-time data applications.

Cost-Saving and Real-Time Benefits of Reflections on Iceberg Lakehouses

Iceberg lakehouses have become essential for organizations seeking the performance and flexibility of a data lake with the structured data management of a warehouse. Apache Iceberg is a powerful table format designed for large-scale analytic datasets, supporting features like schema evolution, partitioning, and transactional data handling, making it a popular choice for managing data in a lakehouse environment.

Dremio’s reflections add another layer of optimization to Iceberg lakehouses, allowing organizations to leverage live updates and incremental refreshes that significantly improve data accessibility, reduce resource consumption, and enhance cost efficiency. Here’s how reflections bring these benefits to Iceberg-based lakehouses:

1. Real-Time Data Availability with Live Reflections

One of the key benefits of Iceberg tables is their ability to support near real-time data updates, allowing users to see the latest information as soon as it’s available. Dremio’s live reflections enhance this capability by automatically refreshing when data changes, meaning users always have access to the most up-to-date snapshot of data without having to initiate manual refreshes.

Example: Imagine a retail company tracking inventory levels across thousands of stores. With Dremio’s live reflections on their Iceberg tables, raw and aggregated reflections on inventory data are updated in real-time, so decision-makers can see current stock levels and make timely restocking decisions. This real-time availability is essential in fast-paced environments where up-to-date data is critical for operations and BI applications.

2. Incremental Refreshes for Cost and Resource Efficiency

Traditional data refreshes are often costly and time-consuming, as they require reprocessing the entire dataset each time an update is made. Dremio’s incremental refreshes solve this by updating only the portions of an Iceberg table that have changed since the last refresh. This approach minimizes the compute resources needed, which is especially valuable when dealing with large datasets, as it reduces both cost and time to refresh.

Example: Consider a financial services company that aggregates transactional data daily. With incremental refreshes on their Iceberg tables, only new transactions are processed, rather than the entire dataset. This targeted refresh drastically reduces the compute resources required, allowing the company to keep data up to date at a fraction of the cost.

3. Reducing Storage Costs with Partitioned Reflections

Iceberg’s partitioning capabilities enable efficient data storage and retrieval, especially when combined with Dremio’s reflections. By partitioning reflections on specific columns, Dremio can limit data scans to only relevant partitions, reducing unnecessary data processing and storage costs. Partitioned reflections also enable faster query responses, as Dremio can skip scanning partitions that aren’t relevant to the query.

Example: A telecommunications company analyzing call records could partition their reflections on columns such as region and call_date. Queries that target a specific region or date range only need to scan the relevant partitions, leading to faster response times and reduced storage overhead. This setup allows them to optimize both performance and cost, making the lakehouse more efficient.

4. Enhanced Query Performance for Iceberg-Based Lakehouses

With Dremio reflections, queries on the views you build on top of Iceberg tables can be significantly faster than views built on other data sources because of live and incremental reflection refreshes. \

Example: A media streaming service with an Iceberg-based lakehouse stores user engagement data, such as video views and interactions. By creating aggregation reflections on this data, Dremio enables fast, efficient queries to track trends in viewership and user preferences. Reflections provide the service with the ability to run ad-hoc analyses or serve BI dashboards in near real time, ensuring that analysts and decision-makers have quick access to actionable insights.

Dremio’s reflections transform Iceberg lakehouses by adding live and incremental refreshes, which enhance performance while keeping costs manageable and data fresh. With these capabilities, organizations can maintain fresh, high-quality data that powers both real-time insights and long-term analytics at a fraction of the traditional cost and operational effort.

In the next section, we’ll look at how Dremio reflections can enhance data availability, particularly for sources that may have limited or inconsistent availability. By using reflection hints and prioritizing reflections, Dremio ensures data continuity even when sources are unavailable.

Enhancing Availability with Reflection Hints for Unreliable Sources

Ideally, all data sources would be consistently available and highly performant. However, real-world data systems often face limitations due to network issues, scheduled maintenance, or high loads on source databases. Dremio’s reflections and reflection hints offer a powerful solution to this problem, allowing you to optimize queries for reliability and performance, even when data sources are prone to downtime.

By leveraging reflection hints, Dremio can prioritize reflections over direct source queries, effectively using cached data snapshots to ensure uninterrupted access. This approach is precious for enhancing availability in BI and analytics workflows, where timely data access is essential.

Here’s how reflection hints can maximize data availability in Dremio:

1. Addressing Availability Challenges with Reflection Hints

When querying data from multiple sources, there’s a risk that one or more sources may be temporarily unavailable. This can disrupt analytics workflows, especially when reports or dashboards rely on those sources. Reflection hints allow users to direct Dremio’s query planner to prioritize or even exclusively use reflections over live data sources, minimizing dependency on source availability.

Example: Suppose an analytics team frequently queries transactional data from a database that occasionally goes offline for maintenance. By setting consider_reflections and choose_reflections hints in Dremio, the team can ensure that the reflection of this data is always prioritized over the external database. This allows reports to run smoothly, even if the API is temporarily unavailable, ensuring that business intelligence insights are uninterrupted.

2. Using Reflection Hints to Improve Query Stability

Dremio provides several types of hints—such as consider_reflections, choose_reflections, and no_reflections—that allow users to control how reflections are considered in query planning. This flexibility is useful when certain data sources may have variable availability or when certain reflections are known to be faster and more reliable than the underlying tables.

• Consider Reflections: By setting consider_reflections hints, users can limit the number of reflections Dremio considers for query planning, ensuring only specific reflections are used, rather than all available reflections. This reduces planning time and directs the optimizer to prioritize selected reflections.
• Choose Reflections: With choose_reflections hints, Dremio can be directed to favor a particular reflection over others in the query plan. This is useful if one reflection has better availability or is more optimal for a specific query pattern.
• No Reflections: In some cases, users may wish to bypass reflections entirely, such as when a real-time view of source data is required. This can be achieved with the no_reflections hint.

Example: A manufacturing company maintains a dataset on external machinery performance from a partner source that experiences regular downtime. By applying the choose_reflections hint to this dataset, Dremio prioritizes the reflection in query planning, ensuring that internal reports always have access to the most recent snapshot, regardless of the source's status.

3. Ensuring Consistent Access with Reflections on Views

When Dremio queries involve views that aggregate data from multiple sources, availability issues with any single source can disrupt the entire query. Using reflections on views allows Dremio to provide a reliable, cached version of a view, reducing the dependency on each individual source. When combined with hints to prioritize these reflections, Dremio can maintain consistent access to the view even if one of its underlying sources becomes unavailable.

Example: An e-commerce platform aggregates data from multiple databases to comprehensively view sales, inventory, and shipping statuses. By creating a reflection on this view and using the choose_reflections hint, Dremio can ensure that the view remains accessible, even if the inventory database goes offline for maintenance. This helps keep analytics workflows running without interruption, providing stable access to crucial metrics.

4. Reducing Load on Unavailable or Resource-Intensive Sources

For data sources with heavy loads, constant queries can degrade performance or contribute to downtime. Reflections allow Dremio to minimize direct queries to these sources by serving data from cached reflections instead, reducing the strain on resource-intensive systems. This approach is beneficial for databases that may have limited read capacity or are optimized for transactional rather than analytical queries.

Example: A financial services company uses a legacy database that houses historical transaction data but struggles with read-heavy analytics workloads. By reflecting this database and setting choose_reflections hints to ensure queries access the reflection, Dremio reduces the query load on the legacy system, improving its availability for core transactional processes. This setup also enables analytics users to query the data without impacting the operational workload of the database.

Reflection hints allow Dremio to maximize data availability, making reflections an invaluable asset for managing unreliable or resource-intensive data sources. By strategically using these hints, organizations can ensure consistent access to essential data, improve query performance, and reduce strain on source systems. This functionality helps organizations build resilient data architectures that deliver reliable insights, even in the face of data source limitations.

Let's explore how Dremio reflections can also support scalability and cost-efficiency when querying databases like PostgreSQL, SQL Server, and MongoDB, reducing the need for costly infrastructure upgrades while supporting high concurrency at minimal expense.

Scaling Database Queries Cost-Effectively with Reflections on RDBMS Data Sources

Many organizations rely on relational databases like PostgreSQL, SQL Server, and MongoDB to manage operational data, but scaling these databases to meet high analytical demands can be challenging. Analytical queries, especially when they’re complex or concurrent, can consume significant compute resources, leading to higher costs and potentially degrading database performance for transactional processes.

Dremio’s reflections offer a powerful alternative by offloading analytical query processing from source databases. By creating reflections on RDBMS data sources, Dremio allows teams to access data without taxing the original databases, effectively providing a "data warehouse" layer for analytics while avoiding the infrastructure costs of scaling the database itself.

Here’s how reflections enhance scalability and cost efficiency when querying databases:

1. Avoiding the Need for Expensive Database Scaling

Relational databases are designed for transactional workloads and can struggle to meet the demands of large-scale analytical queries. Scaling these databases to support both operational and analytical loads often requires significant investment in hardware or cloud resources. By using reflections, Dremio enables organizations to access precomputed snapshots of database tables, reducing the need to scale up database infrastructure for analytics.

Example: A financial institution stores customer transactions in PostgreSQL but faces high costs when trying to scale the database to handle complex BI queries. By reflecting this transactional data in Dremio, the analytics team can access and analyze data without additional load on the PostgreSQL database. This setup allows the institution to avoid costly database upgrades, keeping compute requirements low while still supporting extensive reporting needs.

2. Increasing Concurrency and Supporting More Users

In high-concurrency environments, where multiple users or applications query the same database simultaneously, performance can degrade quickly. Dremio’s reflections provide a way to serve analytics users with high concurrency without overloading the database, allowing more users to access insights concurrently with minimal impact on performance.

Example: A SaaS company using SQL Server for user data encounters concurrency limits as multiple analysts and BI dashboards query the database in real time. By creating a reflection in Dremio on user data, the company can support high query volumes in its analytics applications without increasing SQL Server’s workload, thus maintaining smooth performance for both operational and analytics users.

3. Reducing Compute Load on Source Databases

Reflections in Dremio act as a read-only layer for analytical queries, minimizing the compute load on source databases. This setup ensures that database resources are prioritized for essential transactional operations rather than analytics, improving database efficiency and ensuring that operational processes are not disrupted by analytics queries.

Example: A logistics company uses MongoDB to store data on shipment tracking, which operations teams regularly update. Complex analytics queries from the BI team, however, have occasionally caused slowdowns in database performance. By reflecting the MongoDB dataset in Dremio, the company can continue to serve analytics queries without compromising MongoDB’s performance for real-time operations.

4. Extending Data Warehouse Functionality Cost-Effectively

For organizations looking to enhance their analytics capabilities, reflections offer a cost-effective way to add data warehouse-like functionality to existing databases. Reflections provide the benefits of materialized views, pre-aggregation, and optimized data storage without the infrastructure costs of a full data warehouse.

Example: An e-commerce company with sales data in MySQL needs data warehousing functionality to aggregate data for quarterly reporting. Rather than setting up a dedicated data warehouse, the company can create aggregation reflections on its MySQL tables in Dremio. This allows the analytics team to access pre-aggregated data for reporting, offering the benefits of a data warehouse without the associated cost.

5. Supporting Hybrid and Multi-Cloud Architectures

For companies that use multiple databases across different cloud environments, reflections allow data to be unified in Dremio without the need for extensive data movement. Reflections on data from various sources can be created in one central location, making it easier to perform cross-database analytics and reducing the need for complex ETL pipelines.

Example: A media company uses PostgreSQL in AWS for customer data and SQL Server in Azure for content metrics. By reflecting both datasets in Dremio, the company can analyze and visualize combined insights without the complexity or cost of moving data between clouds or synchronizing data sources. This setup supports cross-cloud analytics seamlessly and at a lower cost.

Conclusion

Dremio’s reflections provide a transformative approach to optimizing data access and performance for BI, AI, and real-time analytics. By creating precomputed snapshots of data, reflections speed up query times, enhance availability, reduce compute loads, and enable cost-effective scaling across a variety of data architectures, from Iceberg lakehouses to traditional RDBMS databases.

Through live and incremental reflection updates, Dremio enables real-time access to data in Iceberg lakehouses, ensuring fresh data for decision-making while minimizing resource consumption. Reflection hints offer a unique way to maintain data availability, even for sources that may be unreliable or resource-intensive, enhancing business continuity and user experience.

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