Tuning Archives -

Teradata Join Indexes vs. Snowflake Materialized Views — A Technical and Pragmatic Comparison

03/09/202611/25/2025 by Roland Wenzlofsky

Database features should be compared based on their documented behavior, their operational impact, and the architectural principles behind them. This applies especially to physical optimization structures such as Teradata Join Indexes (JIs) and Snowflake Materialized Views (MVs)—two features often mentioned together during migration planning, yet substantially different in scope and design. The intention of this …

Understanding Skew in Teradata and Snowflake

03/09/202610/13/2025 by Roland Wenzlofsky

Performance degradation caused by uneven workload distribution is one of the oldest and most persistent challenges in parallel data warehouse systems. Both Teradata and Snowflake can experience this imbalance, commonly known as skew. Although the term is shared, the mechanics differ fundamentally: Teradata can suffer from both persistent and runtime skew, whereas Snowflake’s skew occurs …

Teradata vs. Snowflake: Why GROUP BY Performance Differs at Scale

03/09/202609/27/2025 by Roland Wenzlofsky

When migrating analytical workloads from Teradata to Snowflake, one subtle but important performance factor often gets overlooked: how the two systems handle GROUP BY operations on huge tables. The SQL looks the same, but the execution engines behave differently. If you’ve relied on Teradata for years, you may be surprised by Snowflake’s behavior. GROUP BY …

Skewed Joins, Straight Answers: A Neutral Guide for Snowflake/Teradata Teams

04/11/202609/22/2025 by Roland Wenzlofsky

Snowflake’s physical join execution is predominantly hash-based. In practice you’ll observe hash-join variants with two distributions: If you come from Teradata, the intent will feel familiar: both systems aim to co-locate equal keys before matching. This article explains Snowflake’s strategies, maps them to Teradata’s (including dynamic plan fragments), and shows how to recognize and mitigate …

When Teradata Space Shortage Impacts System Performance

04/11/202605/11/2023 by Roland Wenzlofsky

Running out of free Cylinders in Teradata Encountering a situation where free cylinders are exhausted is a significant concern when managing a system, and no more Teradata Space is available. It’s an issue that can adversely impact the operations and efficiency of the database, leading to potential slowdowns or even complete halts in data processing …

Negative Impact of Applying Functions to Join Columns in Teradata Joins: Performance Implications and Solutions

03/08/202605/05/2023 by Roland Wenzlofsky

Functions on Join Columns and Their Impact on Teradata Performance In many Teradata systems, developers apply functions directly in join conditions to work around data-model inconsistencies.While this approach might seem harmless, it can dramatically affect optimizer decisions and query performance — and often reveals deeper data-model issues. Example of a Problematic Join Applying functions to …

Filter efficiently with Teradata NOS

03/08/202605/05/2023 by Roland Wenzlofsky

Teradata NOS facilitates querying data in an S3 object store with ease. To attain maximum performance, partitioning external data is crucial for efficient reading. This article outlines the key considerations for optimal efficiency when reading data from the object store. To begin, we must establish S3 access by obtaining an AUTHORIZATION object. In this instance, …

Maximizing Performance and Space Savings: Teradata Compression Techniques

04/11/202605/05/2023 by Roland Wenzlofsky

Introduction to Teradata Compression Note: Teradata Block Level Compression is now permanently enabled and cannot be turned off. Nonetheless, this article remains useful for current Teradata systems utilizing block-level compression. It demonstrates the continued advantages of multi-value compression (Teradata MVC). With Teradata’s introduction of block-level compression, the utilization of multi-value compression at various Teradata sites …

How the Number of Rows per Data Block Affects Teradata NUSI Selectivity: A Case Study

03/08/202605/05/2023 by Roland Wenzlofsky

Teradata NUSI Selectivity and Data-Block Density The goal of this article is to show how the number of rows per base-table data block impacts the selectivity threshold for Non-Unique Secondary Indexes (NUSI) in Teradata. Understanding this correlation is critical when analyzing query plans and tuning indexing strategies.The number of qualifying rows that make a NUSI …

Teradata Index Selection: Factors to Consider

03/08/202605/05/2023 by Roland Wenzlofsky

The Teradata Tutorial to support the selection of indexes