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Why Building a Local Data Lake Will Waste Your Time (Unless You Do This)

The Dirty Secrets of Combining Mage and StarRocks

Author

Kate Roylin
November 30, 2024

Modern data engineering demand seamless integration, advanced analytics, and robust data governance. This article delves deep into how Apache Iceberg, Delta Lake, Mage, and StarRocks can be leveraged together to build scalable, performant, and future-proof data workflows. By combining these tools, you can unlock high-performance querying, real-time processing, and AI/ML integration with unparalleled ease.

Why These Tools Matter

In a world awash with data, the tools we choose can make or break our engineering efforts. Here's a quick overview of the technologies we'll explore:

  • Apache Iceberg: A modern table format optimized for big data, designed to handle petabyte-scale datasets.

  • Delta Lake: An open-source storage layer that brings ACID transactions to your data lakes.

  • Mage: A low-code platform for building and deploying machine learning models.

  • StarRocks: A high-performance OLAP database system for interactive analytics.

When combined, these tools form a powerhouse stack for advanced analytics and AI/ML workflows. Let’s explore their synergy step-by-step.

As data becomes the lifeblood of digital transformation, organizations face growing challenges in managing, processing, and analyzing vast amounts of information. Traditional data engineering workflows often struggle with scalability, data governance, and integration across diverse systems. These challenges highlight the need for modern tools and architectures that not only handle the volume of data but also unlock its potential for advanced analytics and machine learning (AI/ML) applications.

The Role of Integration, Analytics, and Governance

  1. Seamless Integration:
    The data ecosystem of any organization often spans multiple platforms, including cloud storage, on-premises systems, and third-party data lakes. Tools like Apache Iceberg, Delta Lake, Mage, and StarRocks facilitate the seamless flow of data across these systems. Integration ensures that the data pipeline remains robust, flexible, and capable of supporting hybrid environments.

    • Example: Apache Iceberg simplifies data ingestion by maintaining a unified table format, while Delta Lake ensures consistency during high-throughput operations. Mage complements this by building machine learning pipelines that work natively with these storage formats, and StarRocks provides a high-performance query layer to access this data in real-time.

  2. Advanced Analytics:
    Advanced analytics is the cornerstone of modern data-driven decision-making. Whether it’s interactive dashboards or predictive machine learning models, organizations need tools that can process and analyze data with speed and accuracy. With StarRocks, complex analytical queries return results in sub-seconds, enabling actionable insights on-demand.

    • Example Use Case: A retail company can use this stack to analyze real-time sales data, predict inventory needs using Mage-powered ML models, and present these insights in dashboards backed by StarRocks.

  3. Robust Data Governance:
    With growing regulatory requirements like GDPR and CCPA, organizations need to ensure that their data is secure, accurate, and auditable. Delta Lake provides ACID compliance to prevent data corruption, while Apache Iceberg supports time travel and schema evolution, making it easier to manage historical data and adapt to changing requirements.

The Combined Power of Apache Iceberg, Delta Lake, Mage, and StarRocks

Apache Iceberg: A Table Format for the Future

Apache Iceberg is a high-performance table format designed to handle petabyte-scale datasets with minimal operational complexity. Originally developed by Netflix to address limitations of existing table formats like Hive and Parquet, Iceberg offers advanced features that make it a game-changer for data engineering.

Key Features:

  1. Dynamic Partition Pruning:
    Iceberg eliminates the need for static partitioning by introducing dynamic partition pruning. This feature allows queries to target only the relevant partitions of data, significantly improving performance and reducing costs in large-scale environments.

  2. Schema Evolution:
    Unlike traditional table formats, Iceberg allows seamless schema changes without requiring a full table rewrite. This makes it easy to adapt to evolving data models, such as adding new columns or changing data types.

  3. Time Travel:
    Iceberg’s time travel feature enables querying historical snapshots of the data. This is useful for debugging, auditing, or even recreating past reports.

  4. Improved Metadata Management:
    Iceberg decouples metadata from the data itself, enabling fast table operations such as listing files, identifying changes, and managing versions without scanning the entire dataset.

Use Case:

Imagine a financial institution analyzing transactional data for fraud detection. Using Iceberg, they can:

  • Efficiently store and query millions of transactions daily.

  • Use time travel to investigate historical anomalies.

  • Dynamically prune partitions to focus on suspicious regions or time periods.

Delta Lake: Ensuring Consistency and Real-Time Data Access

Delta Lake is an open-source storage layer that brings ACID (Atomicity, Consistency, Isolation, Durability) transactions to big data workloads. Built on top of Apache Spark, it simplifies the challenges of managing data lakes by providing strong guarantees for data consistency and real-time updates.

Key Features:

  1. ACID Transactions:
    Delta Lake ensures data reliability by supporting transactions, so multiple jobs can read and write to the same table without corrupting the dataset.

  2. Unified Batch and Streaming:
    Delta Lake bridges the gap between batch and real-time data processing. A single Delta table can ingest streaming data while serving batch queries, reducing infrastructure complexity.

  3. Data Versioning:
    Delta Lake automatically tracks changes and maintains a history of table versions, making it easy to roll back to a previous state if errors occur.

  4. Scalable Metadata Handling:
    Delta Lake optimizes metadata operations, ensuring consistent performance even as table sizes grow to billions of records.

Use Case:

Consider an e-commerce platform tracking user behavior and sales data. Delta Lake can:

  • Stream real-time clickstream data into Delta tables for up-to-date analytics.

  • Merge daily batch uploads from warehouses with live streaming data.

  • Enable real-time dashboard updates for business decision-makers.

Mage: Empowering Machine Learning Pipelines

Mage is a powerful low-code platform designed to make machine learning accessible to both engineers and non-technical users. It simplifies the process of creating, training, and deploying machine learning models while integrating seamlessly with modern data engineering tools like Iceberg and Delta Lake.

Key Features:

  1. Low-Code Interface:
    Mage offers an intuitive drag-and-drop interface for building ML pipelines, making it easy for users to create sophisticated workflows without writing extensive code.

  2. Prebuilt Templates:
    Mage provides a library of prebuilt templates for common ML tasks, such as classification, regression, and time-series forecasting.

  3. Seamless Integration:
    Mage integrates with Iceberg and Delta Lake, enabling users to train models directly on large datasets stored in these table formats. Once trained, the models can be deployed back into the data pipeline.

  4. Automated Monitoring and Retraining:
    Mage continuously monitors the performance of deployed models and triggers retraining if accuracy drops, ensuring models stay relevant.

Use Case:

A logistics company could use Mage to:

  • Train predictive models on delivery time data stored in Iceberg.

  • Forecast delays based on real-time traffic data from Delta Lake.

  • Deploy updated models automatically when new patterns emerge.

StarRocks: OLAP at Lightning Speed

StarRocks is a next-generation Online Analytical Processing (OLAP) database designed to deliver real-time analytics at unmatched speed. With its columnar storage engine and distributed architecture, StarRocks excels in scenarios requiring high concurrency and low-latency queries.

Key Features:

  1. Real-Time Updates:
    StarRocks supports real-time data ingestion, making it ideal for dashboards and other interactive applications that demand up-to-the-minute accuracy.

  2. High-Performance Querying:
    Its vectorized execution engine accelerates analytical queries by leveraging modern CPU architectures, enabling sub-second query responses even on large datasets.

  3. Materialized Views:
    StarRocks automatically creates and manages materialized views, precomputing complex aggregations to further enhance query performance.

  4. Seamless Integration:
    StarRocks integrates with data lakes and ETL tools, including Apache Iceberg and Delta Lake, allowing organizations to run analytical workloads directly on their existing data infrastructure.

Use Case:

A streaming service can leverage StarRocks to:

  • Provide real-time recommendations based on user watch history.

  • Analyze viewership trends across different regions in seconds.

  • Enable business users to query engagement metrics without performance delays.

The Combined Impact

When combined, Apache Iceberg, Delta Lake, Mage, and StarRocks form a powerful data engineering ecosystem that addresses the end-to-end needs of modern workflows:

  • Apache Iceberg provides a scalable, flexible storage layer.

  • Delta Lake ensures consistency and real-time updates.

  • Mage empowers teams to build and deploy machine learning models efficiently.

  • StarRocks delivers high-speed analytical insights.

This integration unlocks new possibilities for real-time analytics, predictive modeling, and data-driven decision-making in any organization.

Synergy in Action: Why Combine These Tools?

When these tools are used together, they address the entire lifecycle of data engineering and analytics, from storage and processing to machine learning and visualization:

  1. Scalability: Iceberg and Delta Lake manage storage and processing at petabyte scale, while StarRocks handles high-query throughput.

  2. Real-Time Processing: Delta Lake streams live data for immediate updates, while StarRocks enables instant analytics.

  3. ML Integration: Mage allows you to build intelligent systems that learn from historical data in Iceberg or Delta Lake and provide predictions for actionable insights.

This synergy ensures a future-proof data ecosystem that is efficient, reliable, and easy to maintain. By leveraging these tools, organizations can remain agile, innovate faster, and stay ahead in today’s data-driven world.

Apache Iceberg – A Foundation for Scalable Data Lakes

Apache Iceberg is a game-changer for data lakes, introducing schema evolution, partitioning, and versioning capabilities that simplify complex workflows.

Key Features

  • Schema Evolution: Update your schemas without rewriting massive datasets.

  • Time Travel: Access historical versions of your data for debugging or auditing.

  • Partitioning Without Compromises: Unlike traditional systems, Iceberg dynamically optimizes partition pruning.

Example: Creating and Querying an Iceberg Table

Setting Up an Iceberg Table

CREATE TABLE customer_data (
  customer_id BIGINT,
  name STRING,
  email STRING,
  signup_date DATE
)
USING iceberg
PARTITIONED BY (signup_date);

Time Travel Query

SELECT * FROM customer_data.snapshots
WHERE committed_at = TIMESTAMP '2024-01-01 00:00:00';

Delta Lake – Bringing Reliability to Your Data Lakes

Delta Lake ensures that your data lakes remain reliable and consistent, even in the face of concurrent operations.

Core Strengths

  • ACID Transactions: Guarantees data consistency.

  • Unified Batch and Streaming: Enables real-time data ingestion.

  • Scalable Metadata Handling: Keeps performance intact even with billions of files.

Merging Data in Delta Lake

Suppose you have incoming updates for customer data. Delta Lake makes the MERGE operation seamless: 

MERGE INTO customer_data AS target
USING updates AS source
ON target.customer_id = source.customer_id
WHEN MATCHED THEN
  UPDATE SET target.email = source.email
WHEN NOT MATCHED THEN
  INSERT (customer_id, name, email, signup_date)
  VALUES (source.customer_id, source.name, source.email, source.signup_date);

Practical Integration

Delta Lake can integrate seamlessly with Iceberg for enhanced metadata control and time travel capabilities.

Mage – Simplifying Machine Learning Workflows

Mage abstracts the complexities of machine learning pipelines, enabling developers to focus on outcomes rather than infrastructure.

Highlights

  • Low-Code Interface: Build pipelines using an intuitive drag-and-drop UI.

  • Built-In Connectors: Integrate with various data sources, including Iceberg and Delta Lake.

  • Version Control: Reproduce experiments with ease.

Training a Model with Mage

Mage simplifies model training with its Python API: 

import mage_ai

pipeline = mage_ai.create_pipeline('customer_segmentation')
pipeline.add_data_source('delta_lake', path='/data/customers')
pipeline.add_transformation('standardize_email', lambda x: x.lower())
pipeline.add_model('kmeans', n_clusters=5)
pipeline.run()

StarRocks – High-Performance Analytics at Scale

StarRocks bridges the gap between OLAP and real-time analytics, providing sub-second query responses on massive datasets.

Benefits

  • Real-Time Updates: Ideal for dashboards and interactive queries.

  • Columnar Storage: Optimized for analytical workloads.

  • Integration-Friendly: Works well with data lakes and external tools.

Running Queries on StarRocks

Here’s how you can query Iceberg data via StarRocks: 

CREATE EXTERNAL TABLE iceberg_customers (
  customer_id BIGINT,
  name STRING,
  email STRING
)
STORED BY 'iceberg'
LOCATION 's3://your-bucket/iceberg-customers';

SELECT name, COUNT(*) AS total
FROM iceberg_customers
GROUP BY name
ORDER BY total DESC;

Bringing It All Together – A Unified Workflow

Architecture Overview

  1. Data Storage: Store raw and transformed data in Iceberg tables.

  2. Data Reliability: Use Delta Lake for real-time updates and ACID transactions.

  3. Machine Learning: Build ML models with Mage on cleaned and enriched data.

  4. Analytics: Perform real-time analytics using StarRocks.

Workflow Example

  1. Ingest Data: Use Delta Lake for streaming ingestion.

  2. Model Training: Run machine learning experiments with Mage.

  3. Querying: Use StarRocks to power user-facing dashboards.

Integration Pipeline

import mage_ai
from starrocks_connector import StarRocksClient

# Step 1: Load Data from Delta Lake
data = mage_ai.load_data('delta_lake', path='/data/customers')

# Step 2: Train a Model
model = mage_ai.train_model(data, 'kmeans', n_clusters=5)

# Step 3: Write Results to StarRocks
client = StarRocksClient(host='your-starrocks-host')
client.write(data, table='customer_segments')

  1. Optimize Partitioning: Choose partitioning strategies based on query patterns.

  2. Leverage Open Standards: Tools like Iceberg and Delta Lake follow open standards, reducing vendor lock-in.

  3. Adopt Real-Time Analytics: Use StarRocks to make actionable insights available instantly.

Conclusion: Building a Future-Ready Data Ecosystem

By integrating Apache Iceberg, Delta Lake, Mage, and StarRocks, software developers and IT professionals can create robust data pipelines that scale with their business needs. Whether you’re powering machine learning models or enabling interactive analytics, this stack ensures your workflows are efficient, reliable, and future-proof.

Modern data workflows demand scalability, efficiency, and seamless integration. By combining Apache Iceberg, Delta Lake, Mage, and StarRocks, businesses can build a robust, future-ready data ecosystem. Apache Iceberg simplifies large-scale data management with features like schema evolution and dynamic partition pruning, enabling efficient, flexible pipelines. Delta Lake ensures real-time data consistency and unifies batch and streaming workflows, providing a solid foundation for analytics and event-driven architectures. Mage empowers teams to integrate machine learning seamlessly into their pipelines through a low-code platform, transforming raw data into actionable insights.

Meanwhile, StarRocks excels in lightning-fast query performance, offering real-time, interactive analytics for complex workloads. Together, this stack offers a streamlined approach to modern data engineering, enabling faster time-to-insights, simplified operations, and scalable infrastructure. Organizations can leverage these tools to unify their data strategy, power advanced analytics, and enable cross-functional collaboration. Whether you’re handling massive datasets, training AI models, or delivering insights through real-time dashboards, this ecosystem ensures efficiency, reliability, and scalability. Start building your future-ready pipelines today and unlock the full potential of your data!