327 technical articles on Teradata, Snowflake, Databricks, SQL tuning and data warehouse architecture — written from production experience, not vendor decks.
A normalized data model can increase the complexity of creating performant queries due to the higher number of tables that must be linked compared to a denormalized data model. It is essential to select a primary index precisely to optimize queries and joins, enabling them to have a direct access path. However, relationship tables often …
Introduction This blog post discusses tactical workloads on a Teradata system. Despite Teradata’s implementation of features that support tactical workloads, this workload category remains challenging to manage. Selecting an optimal physical design is essential to meet user expectations for query speed. Designing the Teradata tactical workload on a test environment can be frustrating, especially when …
Create the target table once in a dedicated test schema and leave it empty before loading. The function does not drop an existing table. Use credentials from your environment and validate source widths before FastLoad. The audit tested the loading protocol and stored values with a small deterministic fixture; the 100,000-row random generator below is …
How to find out if the Teradata Statistics we created for a specific workload are used? Teradata statistics greatly affect SQL query efficiency. We need a reliable method to get this information. Various objects, such as tables and join indexes, can have statistics collected on them. As performance tuners, it is important to confirm their …
Problem-solving without a Teradata Recursive Query To depict a corporate hierarchy in our instance, we can employ a non-recursive approach, illustrated by the query presented below: The above query has several harmful properties: Problem-solving with a Teradata Recursive Query The above recursive query has several benefits: The path is bounded by VARCHAR(1024), and the query …
This article illustrates loading a flat file into an empty Teradata table using TPT. The example was successfully tested on Teradata 16.20. Although TPT does offer a wide range of advanced options for loading files, it can be overwhelming for basic tasks. In this demonstration, I will present a simplified approach to loading compatible delimited …
The DBQL example needs configured logging and a bounded time window. HASHAMP()+1 assumes the relevant map/AMP scope; it may misrepresent a request using only a subset of AMPs. Use the actual participating-AMP count for meaningful skew/impact calculations, and clear temporary query bands after the measured workload. 1. Complete and up-to-date Statistics At the start of …
This article will highlight the contrasting architectural features of Teradata and SQL Server, along with their shared aspects and performance impacts. The piece titled “Teradata versus SQL Server” will be periodically updated. For comparisons against other platforms: how Teradata and Amazon Redshift differ and what makes Teradata an RDBMS rather than an appliance. The same …
What is Teradata Query Rewriting? Teradata query rewriting is an integral component of the optimization process for Teradata. The optimizer replaces your query with a more efficient and faster version. Both queries must produce identical results. Teradata implements various optimization techniques to enhance performance. We will demonstrate the commonly used optimization methods. 1. Removing unreferenced …
Introduction to Teradata Dynamic AMP Sampling Dynamic AMP sampling supplies selected demographic estimates when optimizing. Do not include USI distinctness as though it needs sampling: uniqueness supplies that information. These samples do not replace full column histograms. A random AMP is selected for each table based on the row hash of its internal TABLE_ID, using …
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