Deep Dive into Dremio's File-based Auto Ingestion into Apache Iceberg Tables

Published: at 09:00 AM

Manually orchestrating data pipelines to handle ever-increasing volumes of data can be both time-consuming and error-prone. Enter Dremio Auto-Ingest, a game-changing feature that simplifies the process of loading data into Apache Iceberg tables.

With Auto-Ingest, you can create event-driven pipelines that automatically respond to changes in your object storage systems, such as new files being uploaded to Amazon S3. This approach eliminates the need for constant manual intervention, enabling real-time or near-real-time updates to your Iceberg tables. Whether you’re ingesting structured CSV data, semi-structured JSON files, or compact Parquet formats, Dremio Auto-Ingest ensures a seamless, reliable pipeline.

But why choose Auto-Ingest over traditional methods? The answer lies in its ability to handle ingestion challenges like deduplication, error handling, and custom formatting, all while integrating smoothly with modern cloud infrastructure.

Understanding Auto-Ingest for Apache Iceberg

To fully appreciate the power of Dremio Auto-Ingest, it’s important to understand the core components and how they work together. At its heart, Auto-Ingest is designed to create a seamless pipeline that transfers files from object storage into Apache Iceberg tables with minimal manual intervention. Let’s break it down.

What is a Pipe Object?

The pipe object is the central feature enabling Auto-Ingest. Think of it as a pre-configured connection between your cloud storage and an Iceberg table. The pipe listens for events, such as the arrival of a new file, and automatically triggers the ingestion process. This eliminates the need for periodic manual data loads or complex batch scripts.

Here’s what makes a pipe object powerful:

How Does Auto-Ingest Work?

Auto-Ingest leverages an event-driven model:

  1. A file is added or updated in the storage location (e.g., an S3 bucket).
  2. A notification is sent to the queue specified in the pipe configuration.
  3. The pipe detects the notification and triggers the ingestion process using the COPY INTO command to move data into the Iceberg table.

This approach is both reactive and efficient, ensuring that your data remains fresh without the overhead of constant polling or manual triggers.

Benefits of Using Auto-Ingest

Why choose Auto-Ingest for your Iceberg tables? Here are some key benefits:

  1. Real-Time Updates: Ensure your Iceberg tables always reflect the latest data.
  2. Simplified Pipeline Management: Replace complex, custom ingestion scripts with a single declarative configuration.
  3. Data Quality Assurance: Built-in deduplication and error-handling mechanisms help maintain clean, accurate datasets.
  4. Scalability: Auto-Ingest works seamlessly with cloud-native object storage, enabling pipelines that scale with your data.

By combining the power of Apache Iceberg with Dremio’s Auto-Ingest, you can build modern, efficient pipelines that support both analytical and operational workloads with ease.

Step-by-Step Guide: Setting Up Auto-Ingest

By following these steps, you can automate data ingestion from cloud storage and ensure seamless integration with your data lakehouse.

1. Prerequisites

Before creating an Auto-Ingest pipeline, ensure the following:

2. Creating a Pipe Object

The CREATE PIPE command is the foundation of the Auto-Ingest setup. It connects your storage location to an Iceberg table, specifying ingestion parameters.

Syntax

CREATE PIPE [ IF NOT EXISTS ] <pipe_name>
  [ DEDUPE_LOOKBACK_PERIOD <number_of_days> ]
  NOTIFICATION_PROVIDER <notification_provider>
  NOTIFICATION_QUEUE_REFERENCE <notification_queue_ref>
  AS COPY INTO <table_name>
    [ AT BRANCH <branch_name> ]
    FROM '@<storage_location_name>'
    FILE_FORMAT '<format>'
    [(<format_options>)]

Key Parameters

Examples

Basic Pipe for CSV Files

CREATE PIPE my_pipe
  NOTIFICATION_PROVIDER AWS_SQS
  NOTIFICATION_QUEUE_REFERENCE 'arn:aws:sqs:us-east-1:123456789012:my-queue'
  AS COPY INTO sales_data
    FROM '@s3_source/data_folder'
    FILE_FORMAT 'csv';

Pipe with Deduplication

CREATE PIPE deduped_pipe
  DEDUPE_LOOKBACK_PERIOD 7
  NOTIFICATION_PROVIDER AWS_SQS
  NOTIFICATION_QUEUE_REFERENCE 'arn:aws:sqs:us-east-1:123456789012:dedupe-queue'
  AS COPY INTO analytics_table
    FROM '@s3_source/analytics'
    FILE_FORMAT 'parquet';

3. Customizing File Formats

Dremio allows you to tailor the ingestion process based on your file type and data requirements. Here’s how to configure each format:

CSV Options:

JSON Options:

Parquet Options:

Example for CSV with custom settings:

CREATE PIPE custom_csv_pipe
  NOTIFICATION_PROVIDER AWS_SQS
  NOTIFICATION_QUEUE_REFERENCE 'arn:aws:sqs:us-east-1:123456789012:csv-queue'
  AS COPY INTO transactions_table
    FROM '@s3_source/csv_data'
    FILE_FORMAT 'csv'
    (FIELD_DELIMITER '|', EXTRACT_HEADER 'true', ON_ERROR 'skip_file');

4. Error Handling

Errors during ingestion are inevitable, but Dremio’s Auto-Ingest provides robust handling options:

ON_ERROR Options:

For example:

CREATE PIPE error_handling_pipe
  NOTIFICATION_PROVIDER AWS_SQS
  NOTIFICATION_QUEUE_REFERENCE 'arn:aws:sqs:us-east-1:123456789012:error-queue'
  AS COPY INTO error_log_table
    FROM '@s3_source/faulty_data'
    FILE_FORMAT 'json'
    (ON_ERROR 'skip_file');

With your pipe configured, Dremio automatically monitors your storage for changes and ingests new files into the target Iceberg table. This setup provides a scalable, reliable pipeline for all your data ingestion needs.

Real-World Use Cases for Dremio Auto-Ingest

Dremio’s Auto-Ingest for Apache Iceberg tables offers significant advantages across a variety of data engineering scenarios. Whether you’re building real-time pipelines or automating batch data processing, Auto-Ingest provides the flexibility and automation necessary to simplify workflows. Here are some real-world use cases to illustrate its impact.

1. Streaming Data Pipelines

Scenario: A smart city project collects real-time sensor data (e.g., temperature, traffic flow, air quality) from IoT devices. This data is stored as JSON files in an S3 bucket, and analytics teams require instant updates in their data warehouse for real-time dashboards.

Solution:

Example Configuration:

CREATE PIPE streaming_pipe
  NOTIFICATION_PROVIDER AWS_SQS
  NOTIFICATION_QUEUE_REFERENCE 'arn:aws:sqs:us-west-2:123456789012:sensor-queue'
  AS COPY INTO smart_city.sensor_data
    FROM '@iot_source/live_data'
    FILE_FORMAT 'json'
    (ON_ERROR 'skip_file');

Outcome:

2. Batch Data Processing

Scenario: A retail company ingests daily sales logs in CSV format from its regional branches into a central data lake. These logs must be processed nightly and appended to a historical sales Iceberg table.

Solution:

CREATE PIPE daily_batch_pipe
  DEDUPE_LOOKBACK_PERIOD 7
  NOTIFICATION_PROVIDER AWS_SQS
  NOTIFICATION_QUEUE_REFERENCE 'arn:aws:sqs:us-east-1:123456789012:sales-queue'
  AS COPY INTO retail.sales_history
    FROM '@s3_source/sales_logs'
    FILE_FORMAT 'csv'
    (EXTRACT_HEADER 'true', EMPTY_AS_NULL 'true');

Outcome:

3. Data Lakehouse Modernization

Scenario: A financial services firm is transitioning from a traditional data warehouse to a modern lakehouse architecture. The team wants to automate ingestion from various sources (e.g., transactional Parquet files and JSON logs) into Iceberg tables for unified analytics.

Solution:

Use multiple Auto-Ingest pipes to handle ingestion for different file types and schemas. Configure branch-specific ingestion for staging and production environments. Example Configuration:

Parquet Transactions:

CREATE PIPE transactions_pipe
  NOTIFICATION_PROVIDER AWS_SQS
  NOTIFICATION_QUEUE_REFERENCE 'arn:aws:sqs:us-east-2:123456789012:transactions-queue'
  AS COPY INTO finance.transactions
    FROM '@finance_source/transactions'
    FILE_FORMAT 'parquet';

JSON Application Logs:

Copy code
CREATE PIPE logs_pipe
  NOTIFICATION_PROVIDER AWS_SQS
  NOTIFICATION_QUEUE_REFERENCE 'arn:aws:sqs:us-east-2:123456789012:logs-queue'
  AS COPY INTO finance.app_logs
    FROM '@logs_source/application'
    FILE_FORMAT 'json'
    (DATE_FORMAT 'YYYY-MM-DD', TIME_FORMAT 'HH24:MI:SS');

Outcome:

4. Event-Driven Reporting

Scenario: A marketing team tracks user engagement metrics (e.g., clicks, time on site, purchases) stored as CSV files in real-time. Reports must be updated immediately after new data arrives.

Solution:

Use an Auto-Ingest pipe with an AWS_SQS notification provider to ensure new engagement files are ingested as soon as they are uploaded.

Example Configuration:

CREATE PIPE engagement_pipe
  NOTIFICATION_PROVIDER AWS_SQS
  NOTIFICATION_QUEUE_REFERENCE 'arn:aws:sqs:us-west-1:123456789012:engagement-queue'
  AS COPY INTO marketing.user_engagement
    FROM '@engagement_source/metrics'
    FILE_FORMAT 'csv'
    (FIELD_DELIMITER ',', EXTRACT_HEADER 'true');

Outcome:

These use cases showcase how Dremio Auto-Ingest can be a versatile and powerful tool for a wide range of data engineering challenges. Whether your focus is on real-time data processing, batch workflows, or transitioning to a lakehouse architecture, Auto-Ingest simplifies and enhances your pipeline capabilities.

Best Practices and Considerations for Dremio Auto-Ingest

To get the most out of Dremio Auto-Ingest for Apache Iceberg tables, it’s essential to follow best practices and understand key considerations. These guidelines will help ensure your ingestion pipelines are reliable, efficient, and optimized for performance.

1. Optimize Deduplication Settings

What It Does: The DEDUPE_LOOKBACK_PERIOD parameter ensures that duplicate files (e.g., files with the same name uploaded multiple times) are not ingested repeatedly.

Best Practices:

Example:

CREATE PIPE deduped_pipe
  DEDUPE_LOOKBACK_PERIOD 7
  NOTIFICATION_PROVIDER AWS_SQS
  NOTIFICATION_QUEUE_REFERENCE 'arn:aws:sqs:us-east-1:123456789012:dedupe-queue'
  AS COPY INTO my_table
    FROM '@s3_source/folder'
    FILE_FORMAT 'json';

2. Organize Storage for Better Performance

Why It Matters: Properly structured storage locations improve ingestion speed and reduce processing overhead.

Best Practices:

3. Choose the Right File Format

Impact of File Format: Different file formats affect storage size, query performance, and ingestion speed.

Best Practices:

4. Leverage Error Handling Options

Why It Matters: Errors during ingestion can interrupt pipelines or lead to data inconsistencies.

Best Practices:

Example:

CREATE PIPE error_handling_pipe
  NOTIFICATION_PROVIDER AWS_SQS
  NOTIFICATION_QUEUE_REFERENCE 'arn:aws:sqs:us-east-1:123456789012:error-queue'
  AS COPY INTO my_table
    FROM '@s3_source/folder'
    FILE_FORMAT 'csv'
    (ON_ERROR 'continue');

5. Monitor and Troubleshoot Pipelines

Monitoring Tools:

Common Troubleshooting Tips:

6. Optimize Regex and File Selection

Why It Matters: Using overly broad regex patterns or processing unnecessary files can impact pipeline performance.

Best Practices:

Write regex patterns that are as specific as possible to match only the files you need. Avoid processing large directories unless required. Use the FILES clause or specific folder paths to limit scope. Example:

CREATE PIPE regex_pipe
  NOTIFICATION_PROVIDER AWS_SQS
  NOTIFICATION_QUEUE_REFERENCE 'arn:aws:sqs:us-west-2:123456789012:regex-queue'
  AS COPY INTO my_table
    FROM '@s3_source/folder'
    REGEX '^2024/11/.*.csv'
    FILE_FORMAT 'csv';

7. Plan for Schema Evolution

Why It Matters: Iceberg tables support schema evolution, but it’s crucial to manage changes thoughtfully to avoid ingestion failures.

Best Practices:

8. Integrate with Data Lakehouse Workflows

Why It Matters: Auto-Ingest simplifies transitioning to a lakehouse architecture, but aligning with broader workflows ensures smooth integration.

Best Practices:

By following these best practices and considerations, you can ensure your Dremio Auto-Ingest pipelines are robust, efficient, and well-suited to your data engineering needs. These guidelines will help you avoid common pitfalls and fully leverage the power of automated ingestion for Apache Iceberg tables.

Troubleshooting and Debugging Auto-Ingest Pipelines

Even with a robust Auto-Ingest setup, you may encounter issues during the ingestion process. Dremio’s system tables, such as SYS.COPY_ERRORS_HISTORY, provide detailed insights into ingestion errors, making it easier to diagnose and resolve problems. This section outlines common issues and how to effectively use the system table to debug your pipelines.

1. Common Issues and Resolutions

Notification Configuration Problems

File Format Mismatch

Partial or Skipped File Loads

2. Using the SYS.COPY_ERRORS_HISTORY Table

The SYS.COPY_ERRORS_HISTORY table logs detailed information about COPY INTO jobs where records were rejected due to parsing or schema issues. This includes jobs configured with ON_ERROR 'continue' or ON_ERROR 'skip_file'.

Key Columns in the Table

Example Query: Identifying Problematic Files

To view details about rejected files for a specific table:

SELECT executed_at, job_id, file_path, file_state, records_rejected_count
FROM SYS.COPY_ERRORS_HISTORY
WHERE table_name = 'my_table'
ORDER BY executed_at DESC;

This query highlights:

3. Drilling into Error Details

Once you identify a problematic job using the job_id, you can use the copy_errors() function to extract detailed error information.

Example: Retrieving Error Details

SELECT *
FROM copy_errors('1aacb195-ca94-ec4c-2b01-ecddac81a900', 'my_table');

This query provides granular information about errors encountered during the ingestion process for the specified job.

4. Best Practices for Debugging

Proactive Monitoring

Validate Source Data

Tuning Error Handling

Housekeeping the System Table

The SYS.COPY_ERRORS_HISTORY table can grow significantly over time. Manage its size using these configuration keys:

5. Common Query Patterns for Debugging

Find Recently Skipped Files

SELECT file_path, file_state, records_rejected_count
FROM SYS.COPY_ERRORS_HISTORY
WHERE file_state = 'SKIPPED'
ORDER BY executed_at DESC;

Analyze Partially Loaded Files

SELECT file_path, records_loaded_count, records_rejected_count
FROM SYS.COPY_ERRORS_HISTORY
WHERE file_state = 'PARTIALLY_LOADED'
ORDER BY executed_at DESC;

By leveraging the SYS.COPY_ERRORS_HISTORY table and related debugging tools, you can effectively monitor and resolve issues in your Auto-Ingest pipelines. These capabilities ensure your pipelines are resilient and capable of handling a wide variety of data ingestion scenarios with minimal disruption.

Conclusion

Dremio Auto-Ingest for Apache Iceberg tables brings a new level of automation and simplicity to data ingestion workflows. By leveraging event-driven pipelines, you can reduce manual intervention, ensure data freshness, and streamline the integration of your object storage systems with Iceberg tables.

From real-time updates to batch processing, Auto-Ingest handles diverse use cases with ease, offering powerful features like deduplication, error handling, and format-specific customization. By following best practices, monitoring your pipelines, and troubleshooting effectively, you can create reliable and efficient data ingestion workflows that scale with your business needs.

Whether you’re modernizing your data lakehouse architecture or building advanced analytics pipelines, Dremio Auto-Ingest is a must-have tool to unlock the full potential of your data.