How To Migrate Data From Postgres To Iceberg?

Relational databases like Postgres have been the backbone of enterprise data management for years. However, as data volumes grow and the need for flexibility, scalability, and advanced analytics increases, modern solutions like Apache Iceberg are becoming essential. Iceberg’s open architecture and advanced features make it a compelling choice for organizations looking to optimize their data infrastructure.

In this blog, we’ll guide you through two approaches to migrate Postgres to Iceberg: manual and automated. We’ll also discuss key factors to consider during migration. Choose the method that best fits your needs, and let’s explore how you can make this transition seamless.

What is Postgres Server?

Postgres Database, developed by Postgres Corporation, is a leading relational database management system (RDBMS) designed to handle large-scale data storage, processing, and analytics. Known for its scalability and reliability, Postgres database uses SQL and its proprietary PL/SQL for transaction processing, business intelligence, and data warehousing.

Key Features Of Postgres Server

1. It is highly portable and can run on multiple operating systems, such as Windows, Linux, and UNIX, enabling seamless networking between applications on different platforms.
2. With features like Real Application Clusters (RAC) and In-Memory Database Cache (IMDB), Postgres ensures high performance and low latency for both small-scale systems and large distributed databases.
3. Tools like Postgres RAC, Data Guard, and Multitenant make sure you get continuous database availability to minimize downtime and offer disaster recovery solutions for critical applications.
4. Advanced Security options, such as data encryption, masking, and access control, can be used through tools like Database Vault and Label Security to protect sensitive data and comply with industry standards.
5. Reliable backup and recovery tools, including the Zero Data Loss Recovery Appliance for quick restoration of data in case of failures while minimizing backup time and ensuring data integrity.

What is Iceberg?

Apache Iceberg is an open-source table format designed for managing large-scale analytic datasets in data lakes. Originally developed by Netflix to address the limitations of Apache Hive, Iceberg provides a reliable and efficient way to handle data by enabling advanced features like schema evolution, time travel, ACID transactions, and consistent performance across various query engines like Apache Spark, Flink, and Trino.

Key Features of Iceberg

1. Allows you to modify your data structure without needing costly rewrites or migrations, making it ideal for dynamic environments.
2. Iceberg’s snapshot-based design lets you query historical data, which is crucial for tasks like auditing, debugging, and reproducing past analyses.
3. By supporting ACID properties, Iceberg ensures dependable data operations in multi-user environments, preventing partial updates or data corruption.
4. Hidden partitioning in Iceberg automates and optimizes how data is organized, boosting query performance without manual partition management.
5. Iceberg works effortlessly with popular big data tools like Apache Spark, Flink, Hive, and Presto, ensuring consistent table management and improved data processing efficiency.
6. With support for various compression techniques and data formats like Parquet, Avro, and ORC, Iceberg reduces storage costs and enhances data processing performance.

Why move Postgres Server data to Iceberg?

1. Transactional Reliability: Iceberg ensures reliable data operations by supporting ACID transactions in data lakes, making it suitable for applications that require high data integrity and consistency in large-scale environments.

2. Optimized Query Performance: Iceberg enhances query efficiency using advanced techniques like columnar storage and hidden partitioning, often outperforming traditional indexing methods used in relational databases for analytical workloads.

3. Cost-Effective Data Storage: By transitioning to Iceberg on cloud-based data lakes, organizations can significantly reduce storage costs while maintaining robust analytics capabilities, leading to lower operational expenses and more efficient resource allocation.

4. Simplified Data Collaboration: Iceberg’s versioning and metadata management simplify team collaboration by providing consistent dataset views, reducing errors caused by data inconsistencies and ensuring seamless data sharing across teams.

5. Scalable Data Management: Iceberg offers scalable and adaptable data management by supporting schema evolution and efficient partitioning, allowing data structures to evolve without disrupting existing operations. This scalability and its support for various file formats make Iceberg a versatile solution for managing large datasets effectively.

Step-by-Step Approach To Perform Postgres Server-Iceberg Migration

Prerequisites:

• Ensure your PostgreSQL server’s IP address, hostname, and port number are available.
• The PostgreSQL server version must be 10.0 or higher.
• Add Hevo’s IP address(es) to your PostgreSQL database IP Allowlist and enable log-based incremental replication on the primary instance.
• Create a non-administrative database user for Hevo with SELECT, USAGE, and CONNECT privileges.

Method 1: Using Hevo

Hevo helps you migrate data from Postgres to Iceberg efficiently. Here’s a simplified approach:

Step 1: Configure PostgreSQL as your source.

Step 2: Configure Apache Iceberg as your destination.

For detailed instructions and the latest information, refer to the Hevo documentation: Postgres to Iceberg migration.

Method 2: Using Custom Code

Prerequisites for Migrating Postgres to Iceberg

• Access and Permissions: Ensure admin access to Postgres with permissions for exporting tables. Also, verify access to Spark and Iceberg environments.
• Spark and Iceberg Setup: Install Apache Spark and configure it with Iceberg libraries. Ensure compatibility with your cloud storage system (e.g., S3, HDFS).
• Schema and Export Tools: Verify Postgres table schemas align with the Parquet/ORC formats used by Iceberg. Install Postgres Data Pump or use custom scripts for data export.

Now, let’s examine the step-by-step method for migrating the Postgres to Iceberg.

Step 2.1: Export Data from Postgres

Use pg_dump to export Postgres data to CSV or Parquet files. For CSV:

bash

COPY (SELECT * FROM your_table) TO '/path/output.csv' WITH CSV;

This step can be a hurdle in exporting Postgres data due to the manual effort and potential errors like file permission issues, incorrect file paths, etc, due to large datasets. This is where tools like Hevo make this a more streamlined process. 

Step 2.2: Create Iceberg Table in Spark

Define a new Iceberg table in Spark matching the Postgres schema:

sql

spark.sql("""

CREATE TABLE iceberg.db.your_table (

    id INT,

    name STRING,

    created_at TIMESTAMP

) USING iceberg

PARTITIONED BY (created_at)

""")

Step 2.3: Load Exported Data into Iceberg Table

Use Spark to load Parquet files into the new Iceberg table:

python

spark.read.format("parquet").load("s3://your-bucket/Postgres-data.parquet") \

.write.format("iceberg").mode("append") \

.save("iceberg.db.your_table")

Step 2.4: Validate Migration Accuracy

Run queries on the Iceberg table to verify data integrity:

spark.sql("SELECT * FROM iceberg.db.your_table LIMIT 10").show()

Step 2.5: Switch Operations to Iceberg Table

After validation, point all workloads from Postgres to the new Iceberg table.

Limitations of Manual Migration for Postgres to Iceberg

1. Manually moving data from PostgreSQL to Iceberg involves multiple steps, such as exporting data using pg_dump, converting it to CSV, and loading it into Iceberg using Spark. This process is time-consuming and requires significant technical expertise, adding to its complexity.
2. Risk of data inconsistencies or errors during the transfer process as ensuring data integrity and consistency across both systems can be challenging without automated tools, as manual methods may lead to incomplete backups or require additional steps to recreate indices.
3. Inefficiency in handling large datasets as you may experience latency in data operations and requires proper file management systems, complicating the overall data management efficiency.

Use Cases of Connecting Postgres Server and Iceberg

1. Improved Data Analytics: By integrating PostgreSQL with Iceberg, organizations can leverage Iceberg’s advanced analytics capabilities to handle large datasets efficiently. This integration enables faster data processing and insights, improving decision-making processes.

2. Scalable Data Management: Iceberg helps manage large volumes of data from PostgreSQL, providing a scalable solution for data lakes. This allows businesses to store and analyze massive amounts of data without the scalability limitations of traditional relational databases.

3. Real-Time Data Integration: Using tools like Airbyte or Estuary Flow, you can integrate PostgreSQL with Iceberg to synchronize real-time data. This ensures that data is always up-to-date, supporting real-time applications and analytics.

4. Cost-Effective Storage: Transitioning data from PostgreSQL to Iceberg on cloud storage like S3 reduces storage costs while maintaining robust analytics capabilities. This cost-effectiveness allows for better resource allocation and reduced operational expenses.

5. Simplified Data Pipelines: Integrating PostgreSQL with Iceberg simplifies data pipelines by automating data transfer and management. This reduces the complexity and manual effort required in traditional data transfer processes, making it easier to create dashboards and reports.

Conclusion

Moving your PostgreSQL data to Apache Iceberg is a strategic decision that unlocks the benefits of modern data lake architecture, offering scalability, cost savings, and advanced analytics capabilities. This transition enables you to efficiently manage large datasets, improve data collaboration, and enhance decision-making processes. However, manual migration can be complex and time-consuming, requiring significant resources and expertise. That’s where Hevo comes in – it simplifies the process with automated tools and expert guidance, ensuring a seamless and hassle-free migration experience. By using Hevo, you avoid the challenges of manual migration and get to enjoy a streamlined PostgreSQL-to-Iceberg transition, allowing you to focus on leveraging your data for business insights and growth. Sign up for a 14-day free trial and perform more such migrations.

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