Case Study
Enhancing Storage Query Performance with Databricks
Business Needs
Pella’s data team needed to improve data processing performance and ensure better utilization of compute for their growing storage datasets. Their business teams were experiencing slower access to insights, leading to delays in decision-making and operational reporting.
Systech’s Delivery
Systech partnered with Pella to streamline their storage data pipeline within the Databricks environment. By analyzing the bottlenecks and inefficiencies in the way Delta Lake data was being stored, partitioned, and queried, we implemented strategic optimizations that drastically improved query speed and reduced cost.
Tools Used
Databricks | Delta Lake | Azure Data Lake Storage | PySpark | DBT
The Challenge
Storage datasets had grown significantly, but query performance wasn’t scaling with it. Data consumers reported latency issues, while costs associated with redundant I/O and compute usage continued to rise. The system needed re-architecture to handle higher throughput and more efficient data access patterns.
The Detailed Solution Process
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Evaluated Delta table structures and identified suboptimal partitioning logic.
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Enabled OPTIMIZE and ZORDER BY strategies to improve storage layout and read efficiency.
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Refactored ingestion logic using PySpark to better handle large volumes of write activity.
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Enabled auto-compaction and VACUUM strategies to reduce I/O cost and improve performance.
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Integrated monitoring and observability to capture job metrics and health indicators.
The Impact
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Query performance improved by 3.2x for key storage datasets.
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Cost savings of over 40% in I/O and compute resources were observed post-optimization.
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Business users experienced faster access to insights, improving time-to-decision.
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Data engineering SLAs improved due to reduced pipeline delays.
The Added Value
Systech’s deep expertise with Delta Lake and Databricks’ best practices allowed us to identify performance bottlenecks quickly. Our understanding of storage-layer behavior in large-scale environments helped Pella realize not just speed improvements, but also measurable savings.
Why Databricks + Systech
Databricks Lakehouse provided the unified analytics foundation needed for handling Pella’s massive and fast-growing storage workloads. With Systech’s tailored implementation and optimization expertise, the platform delivered sustained performance and cost efficiencies.
Let’s Talk
Looking to optimize performance and reduce costs across your data platforms?
Reach out to us at www.systechusa.com or marketing@systechusa.com.
Let’s co-create the blueprint for your intelligent enterprise.
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