Why data warehouse professionals have been doing “bronze, silver, gold” for over 20 years. If you have been working in data warehousing for any length of time, the first time you heard about the “medallion architecture” you probably had one reaction: We already do this. You were right. The medallion architecture, popularized by Databricks as …
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 …
One of the smallest but most persistent sources of data mismatches during a Teradata-to-Snowflake migration comes from an unassuming function: ROUND(). While both systems follow ANSI SQL semantics, they differ in how they handle values that lie exactly halfway between two rounding candidates (i.e., when the digit after the rounding precision is 5). Standard Rounding …
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 …
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 …
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 …
Introduction to Teradata Performance and NOT NULL Welcome to our latest Teradata performance blog post, a series designed to provide valuable insights into SQL queries. This article spotlights ‘NOT NULL’. To delve deeper into ‘NOT IN’, it is crucial to comprehend a frequently neglected SQL database design principle: properly defining columns that cannot hold NULL …
Learn about the differences between GROUP BY and DISTINCT for aggregations in Teradata. The optimizer now selects the appropriate algorithm depending on data demographics. Understand when to use each method to optimize query performance.
Learn about Query Logging with Teradata DBQL Tables, a powerful feature for workload analysis and performance tuning. Configure settings and select which key figures to store and their level of detail. The article covers how to implement and activate DBQL tables, determine which information to collect, and analyze tactical queries.
What are Deadlocks in Teradata? Deadlocks arise when two transactions hold locks on database objects required by the other transaction. Here is an example of a deadlock: Transaction 1 locks some rows in Table 1, and transaction 2 locks some rows in Table 2. The next step of transaction 1 is to lock rows in table 2, and …
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