How Top Enterprises Ensure Data Trust at Scale
Introduction: Why Flexibility Is Your Data Superpower
In today's multi-cloud, complex data landscape, unreliable data isn’t just an inconvenience—it’s a multi-trillion-dollar problem. A 2023 IBM report estimates that poor data quality costs U.S. businesses $3.1 trillion annually, while an IDC study (2024) reveals that only 44% of users trust their data for analytics.
The solution? Data observability that adapts to your workloads—whether processing in-place for moderately sized datasets or scaling for massive, high-volume transformations. That’s where PushDown and PullUp (Spark) execution modes come in.
What You’ll Learn in This Blog
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The two execution modes for data observability
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How to select the right one based on your enterprise needs
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Real-world wins proving their impact
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Steps to kickstart your observability strategy
PushDown Execution: Moderate Datasets and Simpler Data Quality Requirements
What It Is
PushDown execution runs observability tasks—such as quality checks, schema drift detection, and freshness monitoring—natively within platforms like Snowflake or Google BigQuery, leveraging built-in compute power. As the name suggests, it pushes operations down into the source data system.
Who Benefits Most
- Regulated Industries – A healthcare giant used PushDown in Snowflake to meet HIPAA compliance while profiling patient data securely in-platform.
- Cost-Savvy Teams – Enterprises with moderate data volumes save significantly by avoiding external compute dependencies.
Supported Platforms
PullUp (Spark) Execution: Larger Datasets and Advanced Computational Needs
What It Is
PullUp execution harnesses Spark’s distributed computing to handle high-volume, cross-platform workloads—like anomaly detection, reconciliation, and deep analytics. As the name suggests, it pulls data out from the source system into Acceldata’s Spark cluster within the customer’s environment.
Who Benefits Most
- Big Data Players – A retailer reconciled supply chain data across AWS and Databricks, spotting inconsistencies faster than traditional batch processes.
- AI Innovators – Teams running ML-driven observability rely on PullUp for scalability and computational power.
Supported Platforms
PushDown vs. PullUp: Your Decision Cheat Sheet
Choosing the Right Execution Mode:
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Go PushDown if governance, cost, or in-platform observability execution is a priority.
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Go PullUp if you need scalability, deep analytics, or cross-platform insights.
Why Leading Enterprises Choose Acceldata
Acceldata is the leading data observability platform offering both PushDown and PullUp execution—giving enterprises the power to optimize every workload dynamically.
- Switch execution modes per workload—no lock-in
- Monitor and improve cloud data quality efficiently
- Balance cost and performance with intelligent automation
Real-World Impact
A pharmaceutical leader slashed costs and ensured GDPR compliance by using PushDown for in-platform checks in Snowflake and PullUp for AWS-based transformations, handling terabytes of patient data without trust gaps.
Your Next Step: Future-Proof Your Data Observability Strategy
Data chaos isn’t inevitable. With flexible execution modes, enterprises can cut costs, ensure compliance, and scale confidently.
Schedule a Demo to explore Acceldata’s industry-first dual execution model—and see how it transforms your data quality and observability strategy.
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