Picture this. Your organization has invested millions in a powerful data warehouse like Snowflake or Amazon Redshift, and it’s humming along nicely, processing thousands of queries daily. But now you need to move some of that data to your new lakehouse architecture, or perhaps join it with datasets living in other systems. Suddenly, what seemed like a straightforward data engineering task becomes a complex orchestration involving network configurations, performance tuning, and governance policies.
This scenario plays out daily across organizations working with massively parallel processing (MPP) systems. These distributed computing powerhouses break down large analytical workloads across multiple independent nodes, enabling organizations to crunch through massive datasets at impressive speeds. Systems like Amazon Redshift, Snowflake, Vertica, Azure Synapse, and Greenplum have become the backbone of modern analytics, but getting data in and out of them efficiently presents unique challenges that traditional ETL tools weren’t designed to handle.
Why MPP is essential for AI
MPP platforms typically serve dual roles in the modern data stack. They function as enterprise data warehouses storing historical facts and dimensional data, while simultaneously acting as high-performance analytics engines for business intelligence, ad hoc SQL queries, and increasingly, AI and machine learning workflows. What makes working with them tricky is that each platform has evolved its own approaches to data export, connectivity, and security, creating a fragmented landscape where one-size-fits-all solutions rarely work.
The importance of MPP systems extends far beyond their raw computational power. These platforms have become essential infrastructure for organizations pursuing data-driven decision-making at scale. Unlike traditional single-server databases, MPP architectures distribute both data storage and query processing across clusters of commodity hardware, enabling linear scalability that can handle petabyte-scale datasets without performance degradation.
Here’s a video showing the process for Trino, a leading MPP technology.
Where MPP fits in the analytics ecosystem
In real-world deployments, MPP systems often anchor complex data ecosystems. A retail company might use Snowflake to power daily sales reporting while simultaneously feeding that same data into machine learning models for demand forecasting. A financial services firm could leverage Amazon Redshift for regulatory reporting while using query federation to join that data with real-time market feeds stored in other systems.
These platforms excel particularly well in scenarios requiring consistent, sub-second query performance across large datasets. Healthcare organizations use them to analyze patient outcomes across millions of records, while telecommunications companies leverage MPP systems to process network performance data from thousands of cell towers. The parallel processing capabilities mean that adding more nodes to the cluster can directly improve query performance, making MPP ideal for workloads where response time is critical.
Supporting AI and machine learning workflows
Modern MPP platforms increasingly feed AI and analytics solutions, often serving as the data foundations for large-scale feature engineering. Data scientists can leverage the distributed compute power to transform raw transactional data into ML-ready features across millions of records simultaneously.
The challenge arises when these MPP-processed datasets need to be sent to other systems. A machine learning team might need to move engineered features from Redshift to a lakehouse for model training, or a business intelligence team might need to join MPP-stored historical data with real-time streams. Each of these scenarios demands efficient, reliable mechanisms for moving data at MPP scale without disrupting production workloads.
Navigating the technical obstacles of MPP
Working with MPP systems at scale reveals a series of interconnected challenges that can quickly derail data engineering projects. These obstacles span technical, operational, and governance domains, each requiring specialized knowledge to address effectively.
Performance and scale challenges
The most immediate challenge involves moving large datasets without overwhelming the source system. Traditional approaches using JDBC or ODBC connections can create significant bottlenecks, as these protocols weren’t designed for the multi-terabyte transfers common in MPP environments. Each major platform has developed server-side solutions to address this limitation: Redshift’s UNLOAD command runs parallel exports across compute nodes, while Snowflake’s COPY INTO functionality can partition outputs across multiple files for efficient downstream processing.
However, leveraging these native capabilities requires deep platform-specific knowledge. Teams often find themselves building custom orchestration for each MPP system, creating operational complexity that scales linearly with the number of platforms in their environment. A multi-cloud organization might need separate pipelines for Redshift exports, Snowflake unloads, and Vertica exports, each with different syntax, performance characteristics, and failure modes.
Network and connectivity complexities
Enterprise MPP deployments typically sit behind multiple layers of network security, creating connectivity challenges for data movement tools. Opening firewall routes between ingestion systems and MPP clusters requires careful coordination between data engineering and infrastructure teams. Synapse deployments require specific firewall and TLS configurations, while cloud-based systems like Redshift may leverage private endpoints for secure connectivity.
Cross-cloud and cross-region data movement introduces additional complexity, including egress charges and latency considerations. Snowflake documents specific charges for cross-region and cross-provider copy operations, costs that can quickly accumulate for organizations with distributed infrastructure. Teams must balance performance requirements against these financial considerations while maintaining security postures.
Schema and governance propagation
Perhaps the most subtle but impactful challenge involves maintaining data consistency and governance as information moves between systems. MPP platforms use different data types, precision levels, and semantic interpretations for common constructs like timestamps and decimal numbers. Starburst’s Vertica connector documentation illustrates the complexity of bidirectional type mappings, showing how seemingly simple data movement requires careful attention to edge cases.
Governance policies present an even thornier problem. Row-level security, column masking, and view-based access controls defined within an MPP system don’t automatically transfer when data moves to external systems. Organizations often discover they’ve inadvertently exposed sensitive information because governance controls weren’t properly replicated in the destination environment. This challenge becomes exponentially more complex when multiple source systems are involved, each with its own security model and policy definitions.
Operational coordination challenges
MPP systems serve production workloads around the clock, making data export timing a critical operational consideration. Large unload operations share cluster resources with business-critical BI dashboards and ETL processes. Redshift’s slice-based parallelization means that poorly planned exports can impact query performance across the entire cluster, potentially affecting end-user experience during business hours.
Teams must develop sophisticated scheduling and resource management practices, often requiring a deep understanding of each platform’s internal architecture. This operational complexity is compounded by the fact that each MPP system exposes different monitoring and control mechanisms, making it difficult to develop standardized operational procedures across a heterogeneous environment.
Building a practical approach to MPP integration
Successfully implementing MPP data integration requires a strategic approach that balances immediate needs with long-term architectural goals. Rather than attempting to solve every challenge simultaneously, organizations benefit from starting with focused use cases and expanding their capabilities incrementally.
Choosing your starting point
The most effective MPP integration projects begin by identifying scenarios where federation can eliminate the need to copy data entirely. Starburst’s federation capabilities enable teams to query MPP data from a single point of access, joining it with information stored in object storage or other databases without creating copies. This approach provides immediate value while teams develop expertise with more complex integration patterns.
For organizations with legacy on-premises MPP systems such as Teradata or Greenplum, data migration solutions often drive initial integration requirements. These projects benefit from starting with read-only federation to validate data quality and performance characteristics before committing to full migration. Starburst’s Teradata Direct connector provides high-throughput HTTP data paths that can significantly accelerate these validation processes.
Organizations looking to modernize from older architectures should also consider Hadoop modernization strategies that leverage open data lakehouse architecture principles.
Implementing performance optimizations
Once basic connectivity is established, teams should focus on optimizations that provide immediate returns. Predicate pushdown and dynamic filtering can dramatically reduce the amount of data that needs to move across network boundaries, improving both performance and cost efficiency. These optimizations work automatically once properly configured, providing ongoing benefits without operational overhead.
For scenarios requiring bulk data movement, materialized views in Galaxy offer an elegant solution. Teams can define federated queries that span multiple MPP sources and persist the results as Apache Iceberg tables in object storage. This pattern effectively creates curated, reusable datasets while maintaining full lineage and governance context, particularly when leveraging optimizing Iceberg table performance features.
When choosing storage formats, teams should compare open table formats and evaluate the latest innovations, such as Apache Iceberg v3 features, to enhance performance.
Addressing governance from day one
Governance considerations should be integrated into MPP projects from the beginning rather than retrofitted later. Starburst Galaxy’s ABAC policies can enforce consistent access controls across federated data sources, ensuring that governance policies remain effective regardless of where data physically resides. For organizations using Starburst Enterprise platform, Apache Ranger integration provides centralized policy management across the entire data ecosystem.
Planning for long-running workloads requires attention to fault tolerance and resource management. Fault-tolerant execution automatically retries failed tasks, while autoscaling adjusts compute capacity based on workload demand. These capabilities become especially valuable for periodic bulk loads or migration activities that might otherwise overwhelm fixed infrastructure.
Measuring success and scaling adoption of MPP
Successful MPP integration projects establish clear metrics from the outset. Beyond obvious performance indicators like query response times and data transfer rates, teams should track operational metrics such as pipeline reliability, governance policy compliance, and total cost of ownership. Organizations often discover that federation reduces infrastructure costs by eliminating the need to maintain multiple copies of large datasets.
Real-world implementations demonstrate the potential impact of modern MPP integration approaches. As teams gain confidence with federated approaches, they can gradually expand their use of advanced features, such as full query passthrough to leverage source-specific optimizations.
Overall, the goal is to build sustainable practices that enable long-term success rather than optimizing for short-term gains. This includes developing comprehensive approaches to managing the data product lifecycle and ELT data processing that support both current needs and future growth.
