Never overpay for unused infra
Mage doesn’t just process data, it revolutionizes how you think about scalability by intelligently scaling data pipelines, vertically and horizontally, in real-time while maintaining peak performance and reducing costs by up to 40%.
Dynamic scalability
Mage’s hyper-concurrency engine splits workloads into independent and self-managing units. These tasks are dynamically generated and distributed across your infrastructure, maximizing speed and processing power across all available resources.
Dynamic blocks adapt their behavior based on input data or runtime conditions, enabling the creation of flexible and complex data pipelines that can easily accommodate varying scalability requirements all without the need to write duplicate code.
Dynamic blocks
Mage AI’s dynamic blocks revolutionize pipeline architecture through adaptive parallelism and context-aware execution, transforming static code from rigid sequences into living neural networks of data processing.
Unlike static DAGs, these blocks enable fractal-like processing trees that auto-scale with data complexity.
Dynamic blocks represent a paradigm shift in data pipeline orchestration, enabling intelligent workload distribution and runtime flexibility that sets the platform apart from traditional ETL tools.
Asynchronous Execution Matrix
Sibling blocks execute concurrently without synchronization
Each branch maintains isolated context through UUID-bound metadata
Failure domains constrained to individual data partitions and doesn’t affect the sibling branches
Stream mode execution
Continuous data hydration enables processing records before the full dataset lands
Achieve 60% faster data delivery SLAs
90% memory reduction vs batch processing
Adaptive topology support
Hybrid parentage: Combine static/dynamic upstreams
Multi-parent orchestration through metadata inheritance
Auto-generated UUIDs prevent namespace collisions
Recursive reduction engine
Fan-in patterns to reduce each block’s data output into a single source of data
Multiple reduction strategies (concat, sum, merge)
Preserved data lineage through reduction stages
Big data, small cost
Mage AI’s smart resource management
Automatically matches processing power to workload demands
Eliminates wasted capacity with predictive scaling
Processes massive datasets without costly hardware upgrades
Reduces cloud spend while maintaining petabyte-scale throughput
Spark magic in lightning time
Run PySpark and SparkSQL alongside vanilla Python – zero infra tax, maximum data power. Mage AI provides a robust interface for monitoring and debugging your Spark pipelines, offering detailed insights into execution metrics, stages, and SQL operations.
Infrastructure autopilot
Mage auto-provisions optimized clusters on-demand per data pipeline needs.
Code hybridization engine
Seamless context handoff between Spark, Pandas, Polars, PyArrow, and other Python objects.
Execution metrics overview
Track Spark execution metrics during development and in production.
Stages and tasks analysis
Visualize task execution phases (e.g., shuffle read/write, deserialization) to identify bottlenecks.
Analyze key metrics such as input records, shuffle bytes, and GC time to optimize performance.
Drill into individual tasks to debug failures or inefficiencies.
SQL execution insights
View the query plan as a graph to understand how Spark processes data (e.g., scans, transformations).
Inspect detailed statistics like scan time, file sizes, and output rows for each stage of the query.
Track SQL statements across multiple jobs with completion status and durations.
Your favorite libraries in one place
From zero-copy Polars to petabyte-scale Iceberg – wield your favorite tools without infra tax.
Avoid vendor lock-in
Blend cloud SQL engines with OSS formats.
Cost arbitrage
Process cold data in DuckDB/Polars and hot data in BigQuery/Snowflake.
