Unlocking the Power of MPP: How Cloud Data Platforms and Matillion Maximize Performance
Massively Parallel Processing (MPP) underpins the scalability and performance of modern cloud data platforms. It allows platforms to process large volumes of data by dividing workloads across many compute nodes that operate in parallel.
TL;DR
Massively Parallel Processing (MPP) is the backbone of modern cloud data platforms like Snowflake, Amazon Redshift, BigQuery, Azure Synapse and Databricks. These platforms distribute compute across nodes to execute large workloads in parallel. Matillion supercharges these capabilities with its pushdown architecture, executing transformations natively within the data platform for optimal speed, cost, and scale.
Key Takeaways:
Enterprise Data Revolution: Massively Parallel Processing (MPP) enables organizations to process complex data workloads with unprecedented speed and efficiency
Platform Flexibility: Modern cloud data platforms like Snowflake, BigQuery, Amazon Redshift, Databricks and Azure Synapse Analytics offer unique MPP capabilities that can be strategically leveraged
Cost Optimization: Intelligent MPP implementation can significantly reduce computational expenses and improve resource utilization
Transformation at Scale: ELT tools like Matillion eliminate traditional bottlenecks by executing transformations directly within cloud data platforms
Future-Proofing Analytics: MPP technologies provide enterprises with the scalability and performance needed to handle increasingly complex data challenges
Enterprise Data Challenges: The MPP Solution
In today's data-driven landscape, enterprises face critical challenges that can make or break their competitive edge: massive data volumes, complex transformation needs, and the constant pressure to derive actionable insights quickly and cost-effectively.
Most enterprises are drowning in data but starving for insights. The real challenge isn't collecting data, it's transforming it into something meaningful and actionable.
Ian FunnellData Engineering Advocate Lead| Matillion
Common Enterprise Data Pain Points
Overwhelming Data Complexity: Enterprises struggle to manage exponentially growing data from multiple sources
Performance Bottlenecks: Traditional data processing methods create significant delays in critical business insights
Infrastructure Cost Management: Maintaining and scaling data infrastructure becomes increasingly expensive and complex
Skill Gap: Finding and retaining talent capable of managing advanced data transformation processes
Agility Limitations: Existing data workflows prevent rapid response to changing business intelligence requirements
MPP: The Game-Changer
By splitting large data processing jobs across many compute nodes, Massively Parallel Processing (MPP) enables scalable, high-speed execution. But how each cloud platform implements MPP matters, and understanding those nuances is key to maximizing performance and minimizing cost.
In this article, we’ll examine how five of the leading cloud data platforms, Databricks, Snowflake, Google BigQuery, Amazon Redshift and Azure Synapse Analytics, implement MPP, and how Matillion is uniquely positioned to leverage each platform’s strengths.
MPP isn't just a technical capability, it's a strategic advantage. It's about transforming data from a challenge into a competitive weapon for enterprises.
Ian FunnellData Engineering Advocate Lead| Matillion
Snowflake: Elastic Compute with Virtual Warehouses
Snowflake uses a multi-cluster shared data architecture that decouples storage and compute. This separation enables it to implement MPP using virtual warehouses, which function as independent compute clusters. When a query is executed, Snowflake breaks it into smaller query tasks and distributes them across nodes within a virtual warehouse, enabling true parallelism.
Key features of Snowflake’s MPP approach include:
Snowflake provides Virtual Warehouses: independent compute resources that can be instantly scaled up or down as needed. This enables flexible, on-demand performance for your data workloads without interrupting operations.
Dynamic execution plans: Snowflake's optimizer evaluates the best execution strategy on the fly, accounting for the current system state and query complexity
Snowflake's interface is primarily SQL based, making it excel at executing transformations natively via SQL. Snowflake offers SQL queries for all tasks including loading, querying, and administrating the platform.
Snowflake’s elastic scaling and robust SQL engine make it a natural fit for modern ELT tools like Matillion, which convert transformation logic into SQL and execute it directly where the data lives.
BigQuery: Serverless MPP at Petabyte Scale
Google BigQuery represents a radically different take on MPP. As a fully managed, serverless data warehouse, it abstracts infrastructure management entirely. Users don’t need to provision or scale compute manually—instead, BigQuery dynamically allocates compute resources based on query requirements.
At the heart of BigQuery’s MPP implementation is its Dremel-based execution engine, which turns SQL queries into execution trees. These trees are split into subtasks and executed across thousands of worker nodes simultaneously.
BigQuery’s strengths in MPP include:
Truly elastic scaling: Compute resources scale horizontally with zero user intervention
Infrastructure abstraction: There’s no need to manage clusters or nodes
Massive concurrency: BigQuery supports thousands of parallel queries without performance degradation
To get the most from BigQuery’s MPP engine, focus on writing flat, declarative SQL and avoid row-by-row operations wherever possible.
Amazon Redshift: Cluster-Based Parallelism
Amazon Redshift is the AWS fully managed, petabyte-scale data warehouse service for a wide range of SQL based data warehousing and analytics use cases. Designed for performance and scalability, Redshift enables organizations to run complex queries across structured and semi-structured data. Traditionally, Amazon Redshift operated in a provisioned clusters mode, where users select the type and number of nodes to allocate dedicated resources for their workload. Amazon Redshift now also offers a serverless mode, allowing users to run and scale analytics on demand without having to manage infrastructure.
Redshift executes queries in parallel across nodes using compiled query plans and optimized execution strategies. The platform supports complex analytics and can scale both storage and compute via new node types like RA3.
Key characteristics of Redshift’s MPP model:
Manual tuning tools: Users can optimize data distribution with DISTKEYs and SORTKEYs
Concurrency scaling: Redshift can spin up transient clusters during high query loads
Materialized views: Improve performance for frequently executed queries
Redshift typically demands more hands-on tuning than Snowflake or BigQuery, especially around distribution keys, sort orders, and workload management. However, it supports SQL pushdown effectively, making it another excellent choice for ELT pipeline platforms like Matillion.
Azure Synapse Analytics: Unified Analytics with MPP
Azure Synapse Analytics (formerly SQL Data Warehouse) is Microsoft’s cloud analytics platform that also leverages MPP architecture. It combines big data and data warehousing capabilities into a unified platform. Synapse uses distributed processing across dedicated or serverless pools, making it capable of handling complex queries at scale.
Azure Synapse Analytics’s MPP key features include:
Dedicated and Serverless Pools: Azure Synapse offers both dedicated SQL pools for high-performance data warehousing and serverless SQL pools for on-demand querying. The dedicated pool relies on MPP for data distribution and parallel processing, while the serverless pool dynamically allocates resources.
Optimized Data Distribution: Synapse allows users to control how data is distributed across compute nodes using distribution methods like hash, round-robin, or replicated
Integration with Azure Ecosystem: Synapse is deeply integrated with other Azure services such as Azure Data Lake, Azure Machine Learning, and Power BI, enhancing its ability to scale analytics workflows
Pushdown Optimization: Azure Synapse is optimized for SQL queries and performs well with pushdown operations. However, like other platforms, performance can degrade when relying on non-native SQL operations, such as custom UDFs
Synapse’s MPP implementation is rooted in its distributed SQL engine, which divides queries into smaller tasks and processes them in parallel across multiple compute nodes. Synapse uses Massively Parallel Data Warehouse (MPDW) architecture to deliver high performance for complex analytics workloads.
Databricks: Scalable Analytics and AI on a Unified Platform
Databricks is a cloud-based platform built on Apache Spark, offering powerful support for data engineering, analytics, and machine learning. It enables collaborative development across SQL, Python, Scala, and R, and unifies batch, streaming, and advanced analytics.
Key features of Databricks’ distributed engine:
Delta Lake: Brings ACID transactions and versioning to data lakes
Auto-scaling clusters: Adjust resources automatically for varying workloads
Photon Engine: High-speed SQL execution for analytics performance
AI/ML Integration: Built-in tools for managing end-to-end machine learning workflows
Databricks is particularly well-suited for organizations seeking a highly flexible platform that can power everything from ETL pipelines and data lakes to real-time streaming and advanced AI/ML workloads. While it demands more technical proficiency than turnkey platforms like Snowflake or BigQuery, its capabilities make it a top choice for modern data and AI teams.
Platform Comparisons
While all five platforms, Snowflake, BigQuery, Amazon Redshift, and Azure Synapse Analytics, employ MPP to handle large-scale data processing, performance can vary depending on how well transformation logic aligns with the platform’s native architecture.
Platform
Strength
Potential Pitfalls
Snowflake
Efficient for most SQL operations
Automatic query optimization
Scales easily with auto-scaling compute clusters
Complex joins may need manual tuning
Poorly optimized queries may require refactoring
BigQuery
Serverless architecture
Scales automatically to handle large datasets
Optimized for SQL execution
Struggles with non-SQL constructs like JavaScript UDFs, which break parallel execution
Requires reliance on native SQL
Amazon Redshift
Effective when data distribution is well-designed
Elasticity with RA3 nodes and concurrency scaling
Deep SQL pushdown support
Performance can suffer without proper data distribution design
Can benefit from manual tuning
Azure Synapse Analytics
Flexible model with dedicated and serverless pools
Integrates well with Azure ecosystem
High performance with MPP and data distribution
Poor data distribution can impact performance
Non-native SQL operations like custom UDFs can degrade performance
Databricks
Highly flexible with support for SQL, Python, R, Scala
Optimized for big data and machine learning workloads
Powered by Apache Spark, supports Delta Lake for ACID transactions and versioning
Steeper learning curve for non-SQL users
Requires tuning for optimal Spark job performance
Costs can rise with inefficient job execution
Matillion: The Enterprise MPP Transformation Engine
Matillion distinguishes itself by being purpose-built for cloud data platform integration. Unlike traditional ETL software that processes data externally, Matillion employs a revolutionary pushdown architecture, converting transformations directly into platform-native SQL.
By pushing transformations into platforms like Snowflake, Amazon Redshift, and Databricks, Matillion delivers:
Minimal data movement: Reducing transfer overhead and potential points of failure
Full parallel execution: Leveraging each platform's native processing capabilities
Optimized transformation strategies: Aligning with platform-specific best practices
This model ensures that Matillion workflows benefit from each platform’s architectural advances, whether it’s Snowflake’s automatic scaling, BigQuery’s serverless abstraction, Redshift’s concurrency enhancements, or Azure Synapse’s flexible scaling and data distribution.
Snowflake: Automatically scales compute clusters based on workload, optimizing performance without manual intervention.
BigQuery: Uses a serverless model that abstracts infrastructure management, allowing automatic scaling to handle petabytes of data.
Redshift: Leverages concurrency scaling and optimized data distribution for efficient execution of large queries.
Azure Synapse Analytics: Benefits from both dedicated and serverless pools, optimizing performance through efficient data distribution and parallel processing.
By pushing transformations to the platform, Matillion maximizes efficiency, reduces overhead, and fully utilizes the unique features of each cloud data platform.
Matillion combines a drag-and-drop UI with full SQL access. This hybrid approach allows users to:
Create complex, performant jobs quickly
Collaborate across technical and non-technical teams
Align transformations with platform-specific best practices
The visual interface highlights which steps are pushed down and flags potential performance issues, giving users real-time visibility into how their pipelines interact with the underlying MPP engine.
Case Study: Slack Scales Analytics with Matillion and Massively Parallel Processing in Snowflake
As Slack’s business grew, so did its data complexity. Their traditional ETL tools couldn’t keep up with the volume and velocity of data required to power business intelligence at scale. Engineering teams were bogged down maintaining brittle pipelines, and dashboards lagged under the weight of growing queries.
To modernize its analytics stack, Slack turned to Matillion on Snowflake—a combination that brought Massively Parallel Processing (MPP) into the heart of its data workflows.
With Matillion’s pushdown ELT approach, data transformations are executed inside Snowflake, allowing Slack to fully leverage the platform’s MPP engine. Rather than relying on a centralized ETL server, transformations are distributed across Snowflake’s high-performance compute clusters, making them faster, more scalable, and easier to maintain.
The results:
80% reduction in pipeline development time, thanks to Matillion’s visual designer and pushdown logic
Near real-time data processing, enabled by Snowflake’s ability to run concurrent transformations in parallel
Faster time-to-insight for data teams and business users alike
One of my team's key responsibilities is delivering business systems data to the broader organization so that employees are empowered to drive innovation. Using Matillion ETL for Snowflake makes it easier to do that.
Vamsee KataManager, Platform Architecture & Ops| Slack
This combination of Matillion’s ELT design and Snowflake’s MPP architecture allowed Slack to scale without friction, reduce operational overhead, and empower internal teams with trusted, timely data.
Matillion doesn’t just optimize transformations, it also orchestrates them with remarkable efficiency. Unlike external ETL tools that introduce latency and bottlenecks between job steps, Matillion's orchestration design is streamlined and integrated directly into the data platform. This approach eliminates bottlenecks, minimizes delays and ensures high-performance operations, even during complex workflows. Here’s how Matillion achieves that:
Execution Within the Data Warehouse: Matillion handles orchestration inside the data warehouse, eliminating the need to move data between external systems and the platform. This reduces the friction commonly associated with job scheduling and increases the overall speed of data processing.
Preserving Parallelism Across Chained Jobs: Unlike traditional ETL systems that may break parallelism across job stages, Matillion maintains parallelism even in multi-step transformations. This ensures that multiple processes can run simultaneously, fully utilizing available resources for faster data processing.
Zero Overhead: being a single platform, Matillion brings simplicity and efficiency to the entire data and AI lifecycle. With just one license, one platform to manage, and one skillset to learn, organizations can streamline operations and remove the complexity that comes from juggling multiple point solutions. Matillion covers everything from data loading and transformation to orchestration and observability, for structured and unstructured data..
This orchestration model enables the execution of highly complex data workflows at scale, with the speed and efficiency that modern data platforms are designed to deliver, free from artificial bottlenecks.
MPP-Optimized Transformations
Massively Parallel Processing (MPP) has become a cornerstone of modern cloud data platforms, allowing organizations to scale processing workloads efficiently. Some operations—such as joins, filters, and aggregations—are particularly well-suited for MPP execution, and Matillion ensures these tasks are optimized for maximum performance.
Pushdown-First Design: Unlike legacy ETL tools that often process data externally before it reaches the data platform, Matillion adopts a pushdown-first approach. This ensures that operations such as joins, filters, and aggregations run directly within the warehouse, leveraging the MPP architecture to maximize throughput.
Linear Scalability with Data Volume: As data volumes grow, Matillion’s MPP-optimized transformations scale effortlessly. With MPP, transformations don’t experience a degradation in performance as data increases. Instead, the platform automatically adjusts resources to handle the expanded workload, ensuring consistent speed and efficiency.
Continuous Platform Improvements: MPP platforms like Snowflake, BigQuery, and Redshift are constantly evolving, with improvements that make data processing faster and more efficient. Matillion ensures that your workflows always benefit from these improvements, allowing users to take full advantage of the advancements in cloud data architecture.
By fully utilizing MPP capabilities, Matillion offers unparalleled speed and scalability in handling complex data transformations, delivering insights faster and more efficiently than legacy systems.
Differentiator: No External Processing Engine
A major differentiator of Matillion is its 100% pushdown-native architecture. While many ETL tools claim pushdown capabilities, they still rely on external processing engines (like Spark or Python runtimes) that introduce unnecessary overhead and complexity. Matillion stands apart in the following ways:
No External Processing Layers: Many traditional ETL solutions send data to separate processing engines before returning it to the data warehouse. This introduces delays and added infrastructure complexity. Matillion’s fully integrated design ensures that all transformations are processed within the data platform itself, without the need for intermediate steps.
No Staging to Cloud Storage Between Steps: A common practice among traditional ETL tools is to write intermediate data to cloud storage before performing the next step in the pipeline. This staging process can add significant delays and incur additional costs. Matillion eliminates this practice by handling all transformations directly within the data warehouse, speeding up execution and lowering costs.
Native Transformation Execution: With Matillion, all data transformations occur directly within the data warehouse, utilizing the full power of the cloud platform’s compute engine. This streamlined architecture ensures that your data pipelines are as fast and cost-efficient as possible.
By removing external processing engines and staging steps, Matillion guarantees faster, more efficient data transformations, allowing you to maximize the power of your cloud data platform.
Common MPP Pitfalls and How Matillion Helps Avoid Them
As with any complex data integration solution, there are common pitfalls that organizations may encounter. Matillion’s approach addresses these challenges head-on:
Pitfall 1: Breaking the Pushdown Chain
Custom scripts or procedural logic can break the chain of pushdown execution, leading to performance degradation. Matillion flags these issues and offers recommendations for redesigning jobs to ensure full in-warehouse execution, maintaining optimal performance.
Pitfall 2: Designing Jobs That Break Parallelism
Serial workflows can limit the benefits of MPP, slowing down processing speeds. Matillion’s job canvas encourages parallelism by supporting concurrent job steps and nested orchestration, ensuring that data workflows fully leverage the power of MPP platforms.
Pitfall 3: Overusing External Staging or Scripts
Writing data to external storage between steps can result in unnecessary overhead and inefficiencies. Matillion eliminates this by executing all transformations within the data platform, ensuring faster processing and reducing costs.
Matillion actively helps customers avoid these pitfalls through continuous support and proactive design recommendations, ensuring that data pipelines remain optimized and efficient.
Support and Enablement
Matillion offers robust support through its customer success and solutions engineering teams. These teams regularly assist customers with:
Refactoring Jobs for Better Performance: Matillion helps optimize existing data workflows to ensure they are as efficient as possible.
Identifying Pushdown Blockers: Solutions engineers identify any obstacles preventing full SQL pushdown execution and work with customers to redesign jobs for better performance.
Tuning Job Design for Each Cloud Platform: Matillion ensures that every customer’s pipeline is tailored to the specific performance characteristics of their cloud platform (e.g., Snowflake, BigQuery, or Redshift).
With Matillion, organizations gain not only powerful technology but also expert guidance to continuously improve and optimize their data integration pipelines.
More Power, Less Friction
MPP: The Future of Enterprise Data Processing
Matillion stands out in the data integration landscape because it removes friction while providing powerful orchestration and MPP-optimized transformations. By eliminating bottlenecks, avoiding external processing layers, and continuously optimizing for the cloud data platforms’ evolving capabilities, Matillion empowers organizations to achieve faster insights, lower costs, and scalable operations.
As MPP technologies evolve, Matillion remains at the cutting edge, ensuring that its customers can keep pace with future advancements and continue to build high-performance data pipelines with minimal effort.
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